Sunday, November 1, 2026

Upgrading a MariaDB Master/Slave Pair in Production

I'm an Oracle DBA by background, and most of my years have gone into Oracle EBS and Exadata. Lately I've been looking after MariaDB too. Recently I took a production MariaDB master/slave pair from 11.4.4 to 11.4.13. The data was about 640 GB, running on RHEL 8 in AWS, and the plan was a full outage with no application traffic.

It was my first MariaDB upgrade on a replicated production pair. It also took two attempts, because the first window was postponed. Along the way DEV and SIT taught me several lessons the hard way.

This post covers the lessons, the runbook, and the health check script I used. Hostnames in this article are generic placeholders: "db-primary" is the master and "db-replica" is the slave.


The setup

  • MariaDB 11.4.x on RHEL 8, installed from MariaDB's own yum repository
  • One master and one slave, using classic file and position replication (not GTID)
  • SSL replication, with the slave replicating one filtered database
  • About 640 GB of data
  • Backups: AWS AMI snapshots, taken with the instances stopped
  • DEV and SIT first (standalone servers, no replication), then production

The upgrade itself is simple: stop MariaDB, run dnf upgrade on the MariaDB packages, start MariaDB, and run mariadb-upgrade. The complicated part is everything around it.


Lesson 1: Check your repo file before anything else

On DEV, my first dnf upgrade said "Nothing to do". Then a partial upgrade updated only the compat packages, and the server stayed on the old version.

The cause was the MariaDB repo file. The [mariadb-main] section had its baseurl pinned to the exact version I was already running:

baseurl = https://dlm.mariadb.com/repo/mariadb-server/11.4.4/yum/rhel/8/x86_64

So dnf could never see anything newer. I first suspected a conflict with the RHEL AppStream repository, which was wrong. The thing that showed the truth was:

dnf repoquery --qf '%{name}-%{version} : %{repoid}' MariaDB-server MariaDB-client MariaDB-common MariaDB-shared

It listed only versions up to 11.4.4 from the mariadb-main repo. The fix is to point the baseurl at the 11.4 series instead of the exact version:

cp /etc/yum.repos.d/mariadb.repo /etc/yum.repos.d/mariadb.repo.bak_$(date +%Y%m%d)
sed -i 's|mariadb-server/11.4.4/yum/rhel/8/x86_64|mariadb-server/11.4/yum/rhel/8/x86_64|' /etc/yum.repos.d/mariadb.repo
dnf clean all && dnf makecache --refresh

dnf clean all and makecache only refresh the list of available packages. They are safe on a running database. After that, repoquery showed 11.4.13.

Takeaway: before any upgrade window, run repoquery and confirm the target version is visible on every node.


Lesson 2: A hostname change can split your binary logs

After an OS patching cycle, one of my servers came back with a different hostname. MariaDB names its binary logs after the hostname by default, so the binlog series split into two: one set named after the old hostname and one after the new.

Two things were behind it:

  1. cloud-init was allowed to reset the hostname on boot (preserve_hostname: false).
  2. The binlog basename was not set explicitly, so it followed the hostname.

On DEV and SIT I fixed it in two parts. First, I locked the hostname:

echo "preserve_hostname: true" | sudo tee /etc/cloud/cloud.cfg.d/99_preserve_hostname.cfg

Second, I set the binlog name explicitly in the server config so it no longer depends on the hostname:

log_bin = /database/mysql/mysql-bin

On production I made a different, deliberate choice. Replication there is file and position based, and the slave knows the master's binlog file names. I applied the hostname lock but did not change the binlog naming, so the logs keep their existing names and the slave simply resumes after the restart. Changing the binlog name on a live master is a separate planned change, not something to slip into an upgrade window.

Takeaway: lock the hostname on cloud instances, and know how your binlogs are named before any change that could touch the hostname.


Lesson 3: "Already upgraded" is normal within 11.4

After a successful package upgrade on SIT, running mariadb-upgrade printed:

This installation of MariaDB is already upgraded to 11.4.3.
There is no need to run mariadb-upgrade again for 11.4.13, because they're both 11.4.

That looks like a failure, but it isn't. Within the same release series (11.4.x), there is nothing for mariadb-upgrade to change in the system tables. You can still run it as a habit, but don't use --force just to see output.

Another normal message: the package step warns that server.cnf.rpmnew was created. That means your custom server.cnf was kept and the new default was saved next to it. Your configuration stays in place.


Lesson 4: The physical backup that never finished

My first plan for a backup was mariadb-backup, a hot physical backup, taken on the slave while it was running. I let it run for about 3 hours. It copied close to 600 GB, and then failed with a message about the redo log:

Was only able to copy log ... try increasing innodb_log_file_size

During a 3-hour copy, the InnoDB redo log wrapped around faster than the backup could read it. The redo log was only 512 MB (innodb_log_file_size = 536870912). Raising it needs a restart, so it is a separate planned change and could not be done in that window. Compression also saved much less than I'd estimated, around 7 to 10 percent, not the 40 to 60 percent I'd guessed.

I stopped chasing it and used AMI snapshots, taken with the instances stopped, as the only restore point.

Takeaway: on a large, busy database, check innodb_log_file_size before you rely on mariadb-backup, and always have a second restore method.


Lesson 5: No verified backup means no upgrade

The first production window was postponed because I did not have a verified restore point. That was the right decision, even though it was uncomfortable.

On the rescheduled night, the same rule was tested. In the first attempt, the slave's AMI did not complete, so we stopped before changing anything. Nothing was upgraded, and I brought the slave back to normal and confirmed replication. In the second attempt, both AMIs completed before I touched a single package.

Rule I now follow: no confirmed AMI on both nodes means no upgrade. The upgrade never starts until the SA team says, in words, that both snapshots are complete.


Lesson 6: Replication does not restart itself

Two weeks before the window, during pre-flight checks, I found the slave with both replication threads stopped and no errors recorded:

Slave_IO_Running: No
Slave_SQL_Running: No
Last_Errno: 0
Seconds_Behind_Master: NULL

I hadn't stopped it. After a MariaDB restart, the slave's replication threads did not come back on their own. On the upgrade night it showed the same behavior after the restart. So the runbook has an explicit START SLAVE and a checkpoint right after it. Never assume replication resumes by itself.


Lesson 7: Verify the filesystem before you start the database

After the AMI snapshots, on the night of the upgrade, the /database mount did not come up automatically on the restarted instances, and the SA team attached it manually. If MariaDB had started against an unmounted data directory, it could have created or used the wrong location.

Before starting anything, I checked:

df -h /database
mount | grep /database
du -sh /database
ls -ld /database/mysql

I looked for the right device, the expected size, the real database files and mysql:mysql ownership. Only then did I start MariaDB and check its journal for errors. I also asked the SA team to fix the /etc/fstab entry so a future reboot doesn't repeat it.

Takeaway: after any instance restart, confirm that storage is mounted and correct before the database starts.


The runbook

This is the order I used for a master/slave pair with a full outage. Slave first, always.

Pre-flight (the day before and again before the window)

On both nodes:

grep -E "^\[|baseurl" /etc/yum.repos.d/mariadb.repo
dnf clean all && dnf makecache --refresh
dnf repoquery --qf '%{name}-%{version} : %{repoid}' MariaDB-server MariaDB-client MariaDB-common MariaDB-shared | grep mariadb-main
rpm -qa | grep -i MariaDB | sort
cat /etc/cloud/cloud.cfg.d/99_preserve_hostname.cfg
hostname
df -h /database

On the slave:

mariadb -e "SHOW SLAVE STATUS\G" | grep -E "Slave_IO_Running|Slave_SQL_Running|Seconds_Behind_Master|Last_Errno"

Checks: both nodes see the same target version, the rpm database has no errors, the hostname lock exists, there is enough disk space, and replication shows Yes, Yes, lag 0.

Also check which packages are actually installed on each node. In my case, MariaDB-backup was installed on the slave but not on the master, so the two upgrade commands differed.

Phase 1: stop everything and take the AMIs

  1. The application team confirms in writing that traffic is stopped.
  2. On the slave, confirm lag 0, then:
    mariadb -e "STOP SLAVE;"
    systemctl stop mariadb
  3. On the master:
    systemctl stop mariadb
  4. Confirm both are stopped (systemctl status mariadb, and no mariadbd process in ps), then shut down both instances.
  5. The SA team takes AMI snapshots of both. Wait for an explicit confirmation that both completed.
  6. Power on, then check hostnames, mounts and data directories.

Phase 2: upgrade the slave first

systemctl start mariadb
mariadb -e "SELECT VERSION();"
mariadb -e "STOP SLAVE;"
systemctl stop mariadb
dnf upgrade MariaDB-server MariaDB-client MariaDB-backup MariaDB-common MariaDB-shared MariaDB-client-compat MariaDB-server-compat -y
rpm -qa | grep -i MariaDB | sort
systemctl start mariadb
mariadb-upgrade -u root
mariadb -e "SELECT VERSION();"
mariadb -e "START SLAVE;"
sleep 20
mariadb -e "SHOW SLAVE STATUS\G" | grep -E "Slave_IO_Running|Slave_SQL_Running|Seconds_Behind_Master|Last_Errno|Last_IO_Error|Last_SQL_Error"

Checkpoint 1: both threads Yes, lag 0, no errors. If it is not clean, stop. Do not touch the master. Troubleshoot or roll back.

Phase 3: upgrade the master

Start the master first at the old version so the slave has something to connect to. After Checkpoint 1 passes:

systemctl stop mariadb
dnf upgrade MariaDB-server MariaDB-client MariaDB-common MariaDB-shared MariaDB-client-compat MariaDB-server-compat -y
rpm -qa | grep -i MariaDB | sort
systemctl start mariadb
mariadb-upgrade -u root
mariadb -e "SELECT VERSION();"
mariadb -e "SHOW MASTER STATUS;"

Phase 4: validate

On both nodes: hostname, SHOW VARIABLES LIKE 'server_id'; and the health check script below. On the slave, run the replication check again.

Checkpoint 2: both threads Yes, lag 0, no errors, same version on both nodes, hostnames and server IDs unchanged.

Then the application team runs their smoke test and confirms before traffic resumes.

Rollback

Decide the trigger before you start, not during. Mine was a fixed time in the window, set so that there was still enough time to restore from the AMIs, restart, and validate. The primary rollback is restoring the AMI snapshots with the SA team. Package downgrade is a fallback only. If the slave is the only affected node, the master stays untouched.


The health check script

This is a one-go script I run before and after every change. It checks the service, connectivity, version, connections, InnoDB, query counters, replication, disk and the error log. It connects as root over the local socket, because root on my servers uses unix_socket authentication with no password, so no credentials are stored in the script.

#!/bin/bash
# MariaDB Health Check - run as root (socket auth)

LOGFILE="/tmp/mariadb_health_$(date +%Y%m%d_%H%M%S).log"
MYSQL_CMD="mariadb"
command -v mariadb &>/dev/null || MYSQL_CMD="mysql"

if [ "$EUID" -ne 0 ]; then
    echo "Run as root: sudo $0"
    exit 1
fi

log() { tee -a "$LOGFILE"; }

echo "MariaDB Health Check - $(date)" | log

echo -e "\n--- 1. Service Status ---" | log
if systemctl is-active --quiet mariadb; then
    echo "MariaDB service: RUNNING" | log
else
    echo "MariaDB service: NOT RUNNING" | log
    exit 1
fi

echo -e "\n--- 2. Connectivity ---" | log
if $MYSQL_CMD -e "SELECT 1;" &>/dev/null; then
    echo "Connection: OK" | log
else
    echo "Connection: FAILED" | log
    exit 1
fi

echo -e "\n--- 3. Version and Uptime ---" | log
$MYSQL_CMD -e "SELECT VERSION() AS version;" 2>/dev/null | log
$MYSQL_CMD -e "SHOW GLOBAL STATUS LIKE 'Uptime';" 2>/dev/null | log

echo -e "\n--- 4. Connections and Threads ---" | log
$MYSQL_CMD -e "SHOW GLOBAL STATUS WHERE Variable_name IN
  ('Threads_connected','Threads_running','Max_used_connections','Connections','Aborted_connects');" 2>/dev/null | log
$MYSQL_CMD -e "SHOW VARIABLES LIKE 'max_connections';" 2>/dev/null | log

echo -e "\n--- 5. InnoDB Buffer Pool and Locks ---" | log
$MYSQL_CMD -e "SHOW GLOBAL STATUS WHERE Variable_name IN
  ('Innodb_buffer_pool_pages_free','Innodb_buffer_pool_pages_total',
   'Innodb_row_lock_current_waits','Innodb_row_lock_time_avg');" 2>/dev/null | log

echo -e "\n--- 6. Query Performance ---" | log
$MYSQL_CMD -e "SHOW GLOBAL STATUS WHERE Variable_name IN
  ('Slow_queries','Questions','Com_select','Com_insert','Com_update','Com_delete');" 2>/dev/null | log

echo -e "\n--- 7. Replication Status ---" | log
REPL=$($MYSQL_CMD -e "SHOW SLAVE STATUS\G" 2>/dev/null)
if [ -n "$REPL" ]; then
    echo "$REPL" | grep -E "Slave_IO_Running|Slave_SQL_Running|Seconds_Behind_Master|Last_Error" | log
else
    echo "No replication configured (or not a replica)." | log
fi

echo -e "\n--- 8. Disk Space (Data Directory) ---" | log
DATADIR=$($MYSQL_CMD -N -e "SHOW VARIABLES LIKE 'datadir';" 2>/dev/null | awk '{print $2}')
[ -n "$DATADIR" ] && df -h "$DATADIR" | log

echo -e "\n--- 9. Recent Errors ---" | log
ERRLOG=$($MYSQL_CMD -N -e "SHOW VARIABLES LIKE 'log_error';" 2>/dev/null | awk '{print $2}')
if [ -n "$ERRLOG" ] && [ -f "$ERRLOG" ]; then
    tail -n 20 "$ERRLOG" | log
else
    # log_error is empty on some systems: errors go to the journal
    journalctl -u mariadb --no-pager -n 20 2>/dev/null | log
fi

echo -e "\n--- 10. Storage Engine Summary ---" | log
$MYSQL_CMD -e "SELECT ENGINE, COUNT(*) AS table_count
  FROM information_schema.tables
  WHERE table_schema NOT IN ('information_schema','mysql','performance_schema','sys')
  GROUP BY ENGINE;" 2>/dev/null | log

echo -e "\nReport saved to: $LOGFILE" | log

One lesson from this script: on my servers log_error was empty, so the original version said "error log not found". The journal fallback in section 9 fixes that. If an error log "can't be found", check where your logging really goes before you assume it's fine.


What I'd tell anyone doing this for the first time

  1. Do DEV and SIT first, and write down every surprise. Mine paid for production.
  2. Verify the repo, the target version and the rpm database on every node before the window.
  3. Have two checkpoints and respect them. Slave first, and never touch the master until the slave is clean.
  4. No confirmed restore point, no upgrade. Wait for a clear confirmation from the team that takes the snapshots.
  5. Decide your rollback trigger before you start.
  6. After any restart, check mounts, hostnames and replication before you trust anything.
  7. Get the expectations right with other teams: who stops traffic, who confirms, who takes the AMIs, and who tests afterwards.

The upgrade itself took very little time. Everything that mattered happened before it and around it. Next on my list, in separate windows, are OS patching and raising innodb_log_file_size so that physical backups can work on a database this size.


If you run MariaDB in production on AWS and you've hit the same issues, or found a better way to handle any of these, I'd like to hear about it in the comments.

Thursday, October 29, 2026

Stop AI Hallucinations: Complete RAG Architecture Explained with Enterprise AI Examples


Stop AI Hallucinations: How RAG Architecture Turns Raw Data into Precise Answers

A Complete Guide to Retrieval-Augmented Generation, Vector Databases, Embeddings, Enterprise AI, and Grounded LLM Responses

Generative AI | RAG | LLM | Oracle AI | Enterprise Architecture


1. Introduction: Why Traditional AI Needs RAG

Large Language Models (LLMs) have transformed how we interact with artificial intelligence. However, they have a fundamental limitation: their training knowledge does not automatically include current information or private enterprise documents.

When an LLM generates information without reliable supporting evidence, it may produce a confident but incorrect answer. This is known as an AI hallucination.

Retrieval-Augmented Generation (RAG) addresses this challenge by connecting an LLM to external knowledge repositories at query time.

Key Takeaway: RAG combines information retrieval with language generation, helping AI produce more relevant, verifiable, and source-grounded responses. RAG reduces hallucinations but does not guarantee their complete elimination.

2. What Is Retrieval-Augmented Generation?

RAG is an AI architecture that retrieves relevant information from external sources before generating an answer.

Instead of relying exclusively on training data, the model receives relevant documents as context.

Common enterprise knowledge sources include:

  • PDF files and technical documentation
  • Company policies and internal documents
  • Standard Operating Procedures (SOPs)
  • Web pages and product manuals
  • Database administration runbooks
  • Cloud architecture documentation
  • Incident resolution knowledge articles

3. Complete Enterprise RAG Architecture Diagram

A RAG system consists of two interconnected processing pipelines:

  • Offline ingestion: Prepares documents for efficient retrieval.
  • Online retrieval and generation: Searches knowledge and generates answers.

Phase 1: Offline Data Ingestion

1. Enterprise Documents PDFs, Policies, Manuals, Web Pages
↓
2. Document Loading Extract Text and Metadata
↓
3. Text Chunking Split Documents into Smaller Segments
↓
4. Embedding Model Convert Text into Numerical Vectors
↓
5. Vector Database Index Embeddings, Chunks, and Metadata

Phase 2: Real-Time Query Processing

1. User Question Natural Language Query
↓
2. Query Embedding Convert Question into a Vector
↓
3. Retriever + Vector Search Search the Indexed Knowledge Base
↓
4. Relevant Text Chunks Retrieve Matching Passages and Sources
↓
5. Context + Question + Instructions Construct the Augmented Prompt
↓
6. Large Language Model Generate an Answer Using Context
↓
7. Grounded Answer Response with Supporting References

Architecture Note: The online retriever searches the vector database populated by the offline ingestion pipeline. Both phases share the same indexed knowledge base.

4. Step 1: Document Ingestion and Chunking

Enterprise knowledge is often stored in large, unstructured documents. Searching entire documents for every question can be inefficient.

Chunking divides documents into smaller text segments that can be independently indexed and retrieved.

Example: Oracle Database Documentation

Imagine a 200-page Oracle Database administration manual containing information about:

  • RMAN backup and recovery
  • Oracle Data Guard
  • ASM storage management
  • Database patching
  • Performance tuning

If a DBA asks about RMAN recovery, the system can retrieve the relevant recovery sections instead of passing the entire manual to the LLM.

Good chunking strategies preserve context using document headings, overlapping segments, and source metadata.

5. Step 2: Embeddings and Semantic Understanding

An embedding model converts each text chunk into a high-dimensional numerical vector.

These vectors represent semantic characteristics of the text, enabling similarity-based retrieval.

Semantic Search Example

Question A: How can I restore an Oracle database?

Question B: What is the RMAN database recovery procedure?

Although the wording differs, both questions are closely related. An embedding model can help identify their semantic similarity.

6. Step 3: Vector Database

Generated embeddings are stored and indexed in a vector-capable database.

A vector database supports similarity searches to identify the most relevant stored chunks for a query.

Popular Vector Technologies

  • Oracle AI Vector Search
  • PostgreSQL with pgvector
  • Pinecone
  • Milvus
  • Weaviate
  • OpenSearch vector search

Vector retrieval may use cosine similarity, dot product, or Euclidean distance, depending on the embedding model and index.

7. Step 4: The Retriever

The retriever is the component that connects the user's question with the indexed knowledge base.

It performs the following operations:

  1. Receives the user question.
  2. Creates or obtains the query embedding.
  3. Searches the vector database.
  4. Identifies the most relevant document chunks.
  5. Optionally applies filtering and reranking.
  6. Returns selected passages and source metadata.

Example: Employee Leave Policy

User: "How many vacation days can I carry forward?"

Retriever: Finds the relevant section in the approved HR policy.

LLM: Generates an answer based on the retrieved policy, with a reference to the supporting document.

8. Step 5: Grounded LLM Generation

After retrieval, the application constructs an augmented prompt containing:

  • The original user question
  • Relevant retrieved text chunks
  • Instructions for answer generation
  • Source metadata for citations
User Question + Retrieved Context + Instructions
↓
Large Language Model
↓
Grounded Answer with Source References

The LLM uses the retrieved information to generate a coherent answer. Applications can instruct the model to acknowledge insufficient evidence rather than inventing unsupported details.

9. Traditional LLM vs. RAG Architecture

Feature Traditional LLM RAG-Based LLM
Knowledge Primarily training knowledge Training knowledge plus retrieved context
Private Documents Not automatically available Available through authorized retrieval
Updated Information Requires external access or updates Can retrieve recently indexed information
Hallucination Risk Can produce unsupported answers Can reduce risk through grounding
Source Attribution Not inherently available Can provide source citations
Model Retraining May be needed for some knowledge changes Often unnecessary for document updates

10. Real-World Use Case: Oracle DBA RAG Assistant

Consider an enterprise database team managing Oracle E-Business Suite, Oracle Database, AWS infrastructure, and disaster recovery.

The organization maintains hundreds of technical documents and operational procedures.

A RAG knowledge assistant can index:

  • Oracle EBS ADOP patching procedures
  • Oracle RMAN backup and restore runbooks
  • Oracle Data Guard DR procedures
  • Database cloning and refresh SOPs
  • ASM storage troubleshooting guides
  • AWS RDS operational documentation

Example DBA Question

"What is our approved procedure to convert Oracle Data Guard physical standby into snapshot standby?"

RAG Workflow:

  1. The question is converted into an embedding.
  2. The retriever searches approved DR runbooks.
  3. Relevant snapshot standby sections are retrieved.
  4. The LLM summarizes the documented procedure.
  5. The answer provides references to the source runbook.

The DBA can verify the prerequisites, restore-point requirements, conversion steps, and rollback process before execution.

For production changes, RAG-generated guidance must still be validated against the environment and approved change-management procedures.

11. Enterprise RAG Best Practices

  • Trusted Sources: Index approved and authoritative documents.
  • Access Control: Enforce document-level permissions during retrieval.
  • Freshness: Update indexes when source documents change.
  • Chunking: Preserve context and document structure.
  • Hybrid Search: Combine semantic and keyword retrieval where useful.
  • Reranking: Improve relevance before sending context to the LLM.
  • Citations: Include verifiable source references.
  • Evaluation: Measure retrieval quality, faithfulness, latency, and cost.
  • Security: Protect against prompt injection and unauthorized data exposure.
  • Human Review: Require validation for high-impact decisions and production operations.

12. RAG and Agentic AI

RAG and Agentic AI are complementary architectures.

RAG retrieves knowledge, while AI agents may plan tasks, use tools, and execute authorized workflows.

For example, an AI-powered DBA assistant could retrieve a troubleshooting runbook, inspect read-only monitoring data, and recommend the next diagnostic steps.

Actions that modify production systems should require appropriate permissions and approvals.

13. Conclusion: From Raw Data to Grounded Intelligence

Retrieval-Augmented Generation provides a practical architecture for connecting generative AI with enterprise knowledge.

It separates knowledge storage from language generation, allowing organizations to update indexed information without continuously retraining the model.

Documents → Chunks → Embeddings → Vector Database → Retriever → Context + LLM → Grounded Answer

The goal of RAG is not simply to make AI sound more intelligent. It is to improve relevance, transparency, and factual grounding through reliable information retrieval.


Final Reflection

How could connecting AI models to your organization's internal documents, technical runbooks, and operational knowledge transform troubleshooting, automation, and decision-making?

Author: Punit Chitra

Oracle DBA | Cloud Database Technologies | Generative AI | Enterprise Automation

Punit Oracle DBA Blog

Topics: RAG Architecture | Generative AI | LLM | Vector Database | Oracle AI | Agentic AI

Wednesday, October 28, 2026

raditional AI vs Agentic AI vs Agentic RAG – Architecture, Differences & Oracle DBA Use Cases

Traditional AI vs Agentic AI vs Agentic RAG

Understanding the Next Evolution of Enterprise Artificial Intelligence: From Predictions to Autonomous Workflows and Context-Aware Reasoning

By Punit Kumar | October 28, 2026

Artificial Intelligence is rapidly transforming how organizations build applications, automate operations, and solve complex problems.

Traditional AI systems are often designed to make predictions, classifications, or recommendations. Modern agentic systems can go further by planning multi-step workflows, selecting tools, gathering evidence, and taking authorized actions.

Three approaches are particularly important: Traditional AI, Agentic AI, and Agentic RAG.

Quick Understanding
  • Traditional AI: Predicts or classifies.
  • Agentic AI: Plans, uses tools, and acts.
  • Agentic RAG: Uses retrieval and reasoning to solve problems with relevant context.

1. What Is Traditional AI?

Traditional AI includes machine learning, predictive analytics, and rule-based systems designed to perform defined tasks.

Common applications include:

  • Fraud detection and anomaly detection
  • Customer behavior prediction
  • Database capacity forecasting
  • Predictive maintenance
  • Workload classification

Traditional AI Architecture

Define Business Problem
↓
Collect Data
↓
Clean and Prepare Data
↓
Feature Engineering
↓
Train Machine Learning Model
↓
Evaluate Model
↓
Deploy Model
↓
Generate Predictions
↓
Monitor Model Performance

Oracle DBA Example: Tablespace Growth Forecasting

Consider an Oracle production database where a tablespace grows by approximately 100 GB every week.

A machine learning model can analyze historical storage usage and estimate when additional storage may be required.

Example AI Prediction:

The DATA tablespace may reach 90% utilization within the next 25 days.

The system provides a prediction. A DBA or a separate automation process typically determines the appropriate remediation.

Traditional AI does not necessarily require retraining from scratch when data changes. Some models support incremental learning, scheduled retraining, and automated updates.

2. What Is Agentic AI?

Agentic AI refers to AI systems that can pursue goals by planning, using external tools, evaluating results, and selecting subsequent steps within defined operational boundaries.

Unlike a single prediction or chatbot response, an AI agent can execute an iterative workflow.

Agentic AI Architecture

User Goal / Business Objective
↓
AI Agent / LLM
↓
Reasoning and Planning
↓
Select Tools and APIs
↓
Execute Authorized Operation
↓
Observe Results
↓
Evaluate and Decide Next Step
↓
Complete Task / Return Result
↺ If the goal is not achieved, replan and repeat within execution limits.

Oracle DBA Example: Intelligent Performance Investigation

Suppose an Oracle database experiences CPU utilization above 90%.

A conventional monitoring system generates an alert. An agentic AI workflow could perform the following:

  1. Receive an Oracle Enterprise Manager or CloudWatch alert.
  2. Collect active-session and SQL performance statistics.
  3. Analyze wait events, AWR, ASH, and execution plans.
  4. Identify likely causes of resource consumption.
  5. Recommend remediation or initiate approved diagnostics.
  6. Verify whether performance improves.
Example Agent Recommendation:

SQL_ID abc123 appears to be consuming significant CPU. Review its execution plan, workload changes, and optimizer statistics before considering remediation.

Advantages of Agentic AI

  • Dynamic, multi-step problem solving
  • Integration with external tools and APIs
  • Automated investigation workflows
  • Feedback-driven planning
  • Reduced repetitive manual work

Agentic AI can adapt its workflow based on observations. However, this does not mean the underlying LLM automatically re-trains itself after every interaction.

3. What Is Retrieval-Augmented Generation (RAG)?

Retrieval-Augmented Generation, or RAG, combines a generative AI model with an external information retrieval system.

Instead of depending entirely on knowledge stored in model parameters, a RAG application retrieves relevant information before generating its response.

Basic RAG Workflow

User Question
↓
Query Processing
↓
Search Knowledge Sources
↓
Retrieve Relevant Information
↓
LLM + Retrieved Context
↓
Generate Grounded Response

Knowledge retrieval may use vector similarity search, keyword search, SQL, or hybrid search.

For Oracle environments, Oracle AI Vector Search can support semantic retrieval over embedded enterprise documents.

4. What Is Agentic RAG?

Agentic RAG extends conventional RAG by introducing agent-based planning, iterative retrieval, tool selection, and evidence evaluation.

Rather than retrieving documents once, the agent can decide whether additional searches are necessary and which sources should be consulted.

Agentic RAG Architecture

User Question / Objective
↓
AI Agent: Analyze and Plan
↓
Select Retrieval Sources
↓
Vector Search
SQL Database
APIs / Documents
↓
Retrieve and Combine Evidence
↓
Evaluate Relevance and Completeness
↓
Reason and Synthesize
↓
Verify Findings
↓
Evidence-Based Final Answer
↺ If evidence is insufficient, refine the query, search another source, and repeat.

Oracle DBA Example: ORA-12505 After Database Refresh

Imagine an Oracle E-Business Suite R12.2 environment encountering the following error after a database refresh:

ORA-12505: TNS:listener does not currently know of SID given in connect descriptor

An Agentic RAG system could:

  1. Analyze the error and environment details.
  2. Retrieve internal database refresh SOPs.
  3. Search previous incidents involving listener errors.
  4. Inspect authorized listener and database diagnostics.
  5. Compare the current configuration with documented settings.
  6. Recommend a validated correction.
  7. Prepare a verified incident summary for knowledge-base review.
Example Recommendation:

The connect descriptor may reference an SID not registered with the listener. Verify the SID or SERVICE_NAME, database service registration, listener configuration, and Oracle EBS context settings.

This illustrates how Agentic RAG combines historical knowledge, live diagnostics, and iterative reasoning.

5. Traditional AI vs Agentic AI vs Agentic RAG

Capability Traditional AI Agentic AI Agentic RAG
Primary purpose Prediction and classification Goal-oriented task execution Context-aware reasoning and retrieval
Decision-making Model-based or predefined Dynamic planning Dynamic, evidence-informed planning
External tools Optional Common Common
Knowledge retrieval Optional Optional Core capability
Multi-step workflows Usually externally orchestrated Yes Yes
Autonomous actions Not inherent Possible Possible
Memory Model-dependent Optional Optional
Oracle DBA example Predict storage growth Investigate performance issues Investigate using live data and runbooks

Important: Agentic RAG is a specialized application of Agentic AI, not a completely unrelated technology category.

6. Enterprise Use Cases for Oracle DBAs

Use Case 1: Database Performance Optimization

Traditional AI: Forecast CPU utilization.

Agentic AI: Investigate expensive SQL and recommend diagnostics.

Agentic RAG: Combine live performance metrics, historical AWR reports, and internal tuning procedures.

Use Case 2: Oracle EBS Patching

Traditional AI: Estimate patching duration.

Agentic AI: Coordinate prechecks and monitor approved workflows.

Agentic RAG: Consult Oracle patch documentation, ADOP runbooks, and historical patching failures.

Use Case 3: AWS RDS Monitoring

Traditional AI: Forecast storage consumption.

Agentic AI: Investigate CloudWatch alerts and initiate approved diagnostics.

Agentic RAG: Combine AWS metrics, architecture documents, and historical incidents.

Use Case 4: Oracle Data Guard DR Readiness

Traditional AI: Analyze replication-lag trends.

Agentic AI: Run authorized standby-health checks.

Agentic RAG: Combine transport/apply lag, DR runbooks, recovery objectives, and previous drill findings.

7. How to Build an Enterprise Agentic RAG System

A practical implementation should begin with a narrowly defined problem and expand only after reliability and security have been demonstrated.

  1. Define the use case: Select a task such as database incident investigation.
  2. Prepare knowledge: Collect SOPs, runbooks, incident reports, and documentation.
  3. Implement retrieval: Use vector, keyword, SQL, or hybrid search.
  4. Select the LLM: Evaluate accuracy, tool support, cost, and security.
  5. Integrate tools: Connect approved database, monitoring, and cloud APIs.
  6. Build orchestration: Implement planning, retries, execution limits, and approval checkpoints.
  7. Evaluate: Test groundedness, accuracy, tool reliability, operational safety, and task completion.

Example Technology Stack

ComponentExample Technologies
LLMEnterprise-supported language models
Vector SearchOracle AI Vector Search
DatabaseOracle Database 23ai / 26ai
OrchestrationLangGraph, LangChain, custom workflows
CloudOCI, AWS
MonitoringOracle Enterprise Manager, CloudWatch
IntegrationREST APIs, approved SQL tools
SecurityIAM, RBAC, audit logging, approvals

8. Security and Governance

Autonomous systems require stronger safeguards when they can interact with production databases and infrastructure.

  • Apply least-privilege access.
  • Use read-only database access by default.
  • Require approval for high-impact operations.
  • Audit prompts, tool calls, and actions.
  • Protect credentials and sensitive data.
  • Defend against prompt injection in retrieved content.
  • Implement execution timeouts and rate limits.
  • Verify remediation results and maintain rollback plans.
Production DBA Security Rule:

Never provide an AI agent unrestricted SYSDBA access. Use scoped credentials, approved tools, and human authorization for operations that can affect availability, data integrity, or security.

9. Does Agentic AI Learn Automatically?

Agentic AI can improve future task execution through updated knowledge, retained task history, tool feedback, and refined workflows.

However, updating an agent's memory does not automatically retrain its underlying language model.

These are separate mechanisms:

  • Memory: Retains useful context or history.
  • Retrieval: Supplies relevant external knowledge.
  • Planning: Adjusts steps based on observations.
  • Training: Changes model parameters through a separate learning process.

10. Which AI Approach Should You Choose?

The best approach depends on the problem.

  • Choose Traditional AI for well-defined forecasting, anomaly detection, and classification tasks.
  • Choose Agentic AI for workflows involving multiple decisions, tools, and operational steps.
  • Choose Agentic RAG when complex tasks require dynamic access to enterprise knowledge and verifiable evidence.

Recommended Enterprise Adoption Roadmap

Phase 1: AI Knowledge Search
↓
Phase 2: Agentic RAG Investigation
↓
Phase 3: Read-Only Diagnostics
↓
Phase 4: Human-Approved Remediation
↓
Phase 5: Controlled Automation at Scale

11. The Future of AI in Database Administration

Database administrators increasingly manage complex, distributed environments across on-premises infrastructure, Oracle E-Business Suite, cloud databases, and hybrid platforms.

Agentic AI and Agentic RAG can help DBA teams with:

  • AI-assisted root cause analysis
  • Database health checks
  • SQL performance diagnostics
  • Oracle EBS patching investigations
  • Disaster recovery readiness assessments
  • AWS database capacity and cost analysis
  • Incident documentation and knowledge management
  • Approved operational runbook automation

The objective is not to replace DBA expertise. It is to reduce repetitive investigation and help engineers focus on reliability, architecture, security, and critical decisions.

12. Final Thoughts

Traditional AI, Agentic AI, and Agentic RAG offer different ways to apply artificial intelligence in enterprise systems.

Traditional AI: Helps predict what may happen.

Agentic AI: Helps determine what to do and coordinate actions.

Agentic RAG: Helps make decisions using relevant, retrieved evidence.

The future of enterprise AI is not simply about larger language models. It is about combining reasoning, enterprise knowledge, tools, verification, and human oversight into reliable workflows.

For Oracle DBAs and cloud engineers, these technologies create new opportunities to transform reactive troubleshooting into intelligent, proactive operations.

The real question is no longer only: "Can AI answer this question?"

It is: "Can AI gather the right evidence, determine the next step, and safely help complete the task?"

Which Approach Are You Building Today?

Are you working with Traditional AI, Agentic AI, or Agentic RAG?

Share your thoughts and practical experiences in the comments.

About the Author

Punit Kumar is an Oracle Database and Oracle E-Business Suite technology professional specializing in enterprise database administration, AWS environments, high availability, disaster recovery, performance optimization, and automation.

This blog shares practical DBA guides, cloud technology insights, troubleshooting experiences, and emerging artificial intelligence concepts.

Blog: Punit Oracle DBA

Sunday, October 25, 2026

Agentic AI Interview Glossary

The DBA's Guide to Oracle Agentic AI Foundations · Glossary

Every acronym, key idea and likely interview question from the series, explained in plain words with an Oracle DBA comparison. Bookmark it for revision before your exam or interview.

Full forms

The highlighted letters show where each acronym comes from. Learn the full form first; the meaning usually follows from it.

AI and model basics

AI

Artificial Intelligence

Machines doing tasks that need human-like thinking

ML

Machine Learning

Systems that learn patterns from data instead of fixed rules

GenAI

Generative AI

AI that creates new text, images or code

LLM

Large Language Model

The "brain" of an agent, trained on huge amounts of text

NLP

Natural Language Processing

Making computers understand human language

HITL

Human-in-the-Loop

A person approves each important step

Reasoning and retrieval

RAG

Retrieval-Augmented Generation

Fetch relevant documents first, then let the LLM answer from them

CoT

Chain-of-Thought

Reason step by step before answering

ReAct

Reason + Act

Thought → Action → Observation, in a loop

ToT

Tree-of-Thought

Explore several reasoning paths, keep the best

NL2SQL

Natural Language to SQL

Turn an English question into a SQL query

KB

Knowledge Base

The document library a RAG tool searches

Frameworks and protocols

MCP

Model Context Protocol

Open standard to plug any AI app into any tool ("USB-C of AI")

A2A

Agent2Agent protocol

Open standard for agents talking to other agents

SDK

Software Development Kit

Ready-made libraries for building apps, e.g. OpenAI Agents SDK

LCEL

LangChain Expression Language

Joins steps with a pipe: prompt | model | parser

API

Application Programming Interface

A defined way for software to talk to software

REST

Representational State Transfer

The common style for web APIs over HTTP

JSON

JavaScript Object Notation

The text format tools and LLMs exchange data in

JSON-RPC

JSON Remote Procedure Call

The message format MCP uses (version 2.0)

HTTP

Hypertext Transfer Protocol

Web transport; MCP's remote option is Streamable HTTP

SSE

Server-Sent Events

Older MCP remote transport, replaced by Streamable HTTP

stdio

Standard input/output

MCP transport for a local server on the same machine

OAuth

Open Authorization

Standard for secure delegated access to remote servers

Oracle and database

OCI

Oracle Cloud Infrastructure

Oracle's cloud

IAM

Identity and Access Management

Who can do what in OCI (policies, compartments)

ADB

Autonomous Database

Oracle's self-managing cloud database

HNSW

Hierarchical Navigable Small World

In-memory graph vector index: fastest, very accurate

IVF

Inverted File (Flat) index

Partition-based vector index: scales to huge data

ANN

Approximate Nearest Neighbor

Fast vector search using an index, slightly less than 100% accurate

KNN

k-Nearest Neighbors

Find the k closest vectors; exact search compares them all

ONNX

Open Neural Network Exchange

Model format you load into the database to create embeddings

VPD

Virtual Private Database

Row-level security that also applies to agent queries

PII

Personally Identifiable Information

Names, phone numbers, IDs: guardrails mask or block it

Key concepts

What each term means, and how to explain it using things a DBA already knows.

AI agent

An LLM that plans, uses tools and remembers, looping until a goal is met

The DBA wayThe on-call DBA who fixes the alert, not the one who only explains the error

Workflow (vs agent)

Fixed steps decided by code, with LLM calls inside

The DBA wayA scheduled RMAN script: same steps every time

Token

The small piece of text a model reads and writes (about ¾ of a word)

The DBA wayLike a block: the unit everything is counted and billed in

Context window

How many tokens the model can see at once; also its short-term memory

The DBA wayThe SGA: fast, limited, cleared when the session ends

System prompt

Fixed rules that set the agent's role and limits

The DBA wayThe runbook and change policy

Embedding

A list of numbers that captures the meaning of text

The DBA wayA fingerprint of meaning, not of spelling

Vector

The stored embedding; similar meanings sit close together

The DBA wayA row you search by closeness instead of equality

Semantic search

Finding results by meaning, not exact words

The DBA way"Tablespace full" also finds "no space in datafile"

Cosine similarity

Measures the angle between two vectors; the usual text metric

The DBA wayUsed inside VECTOR_DISTANCE(..., COSINE)

Chunking

Splitting documents into small pieces before embedding

The DBA wayLike splitting a large export into manageable files

Grounding

Tying answers to real data (tools, RAG)

The DBA wayAnswering from AWR, not from memory

Hallucination

A confident but made-up answer

The DBA wayA guess written into an RCA without proof

Function / tool calling

The LLM outputs a structured request; the app runs it

The DBA wayThe LLM writes the ticket; the operator executes it

Guardrails

Checks on input and output (injection, PII, toxicity)

The DBA wayChange control and audit for the agent

Prompt injection

Hidden instructions in input or tool results that try to hijack the agent

The DBA waySQL injection, but in plain language

Least privilege

Give tools only the access they need

The DBA wayA read-only DB user for a reporting agent

Handoff

One agent passes control to another

The DBA wayL1 hands the ticket to L2

Supervisor pattern

A manager agent delegates to specialists

The DBA wayThe incident manager in a P1 war room

Reflection

The agent critiques and improves its own output

The DBA wayReviewing your own RCA before sending it

Fine-tuning

Retraining a model on your data to change its behaviour or style

The DBA wayChanging the engine, not the fuel

RAG vs fine-tuning

RAG adds fresh facts at question time; fine-tuning changes how the model behaves

The DBA wayUse RAG for changing data like runbooks; it needs no retraining

Zero-shot / few-shot

Asking with no examples / with a few examples in the prompt

The DBA wayFew-shot = showing a junior two sample tickets first

Temperature

Controls randomness; low = consistent, high = creative

The DBA wayKeep it low for SQL and factual answers

Observability / tracing

A log of every step, tool call and result

The DBA wayThe audit trail and AWR for agents

Interview Q&A

The 15 questions you are most likely to hear. Each answer takes under 30 seconds to say.

Try answering out loud first, then tap a question to check yourself.
1What is an AI agent?

An AI agent is an LLM that can plan, use tools, remember and act in a loop until it reaches a goal. A chatbot answers once; an agent does the work, like an on-call DBA who checks, fixes and verifies.

2What are the components of an agent?

Five: the model, instructions, tools, memory and planning. I remember them as "My Instructions Tell Me Plans".

3Agent vs workflow: what's the difference?

In a workflow, code fixes the steps. In an agent, the LLM decides the next step based on what it finds. If the steps never change, a workflow is cheaper and more reliable.

4Does the LLM execute the tools itself?

No. The LLM returns a structured request with the tool name and arguments. The application runs it and sends the result back. That is function calling.

5Explain ReAct.

Reason + Act. The agent alternates Thought, Action and Observation until it can answer. It's how a DBA tunes a query: think, run the plan, read it, think again.

6What is RAG and why use it?

Retrieval-Augmented Generation. You retrieve relevant chunks from your documents and give them to the LLM with the question. It grounds answers in your data, reduces hallucination and needs no retraining.

7RAG or fine-tuning?

RAG for facts that change, like runbooks and policies. Fine-tuning to change the model's style or behaviour on a narrow task. Most enterprise use cases start with RAG.

8What is MCP?

Model Context Protocol, an open standard that connects AI apps to tools and data in one way. The host runs clients; each client connects to one server; servers expose tools, resources and prompts over JSON-RPC 2.0. Think of it as the Oracle listener for AI.

9Tools vs resources vs prompts in MCP?

Tools are actions the model decides to call. Resources are read-only context the application supplies. Prompts are templates the user picks.

10How do you build agents on OCI?

Create an agent with instructions, attach tools (RAG for documents, SQL for tables, custom functions for APIs), connect a knowledge base, and expose it through an endpoint with sessions and guardrails.

11What does Oracle AI Database add for agents?

It stores vectors next to relational and JSON data, so one query can combine semantic search and SQL. Existing security like privileges, VPD and auditing still applies, and there is no separate vector store to keep in sync.

12HNSW or IVF?

HNSW is an in-memory graph: fastest and very accurate, but needs the vector memory pool. IVF partitions vectors on disk and scales to very large data with less memory.

13What is Select AI?

Natural language to SQL inside the database, using DBMS_CLOUD_AI and an AI profile. Actions like showsql show the generated SQL, runsql runs it, and narrate answers in plain English.

14How do you make agents safe in production?

Input and output guardrails, least-privilege tools, human approval for irreversible actions, iteration and token limits, and full tracing. The same discipline as change control on a production database.

15Single agent or multi-agent?

Start with one agent. Move to multi-agent (supervisor, handoffs) only when tasks need clear specialisation, because each extra agent adds latency, cost and debugging effort.

How to answer any question

Use the same three steps every time. They show both knowledge and real experience.

Step 1

Define it with the full form

"RAG stands for Retrieval-Augmented Generation. It retrieves relevant documents and gives them to the LLM before it answers."

Step 2

Give the DBA analogy

"It's like answering a performance question from the AWR report instead of from memory."

Step 3

Connect it to Oracle

"In Oracle AI Database, the chunks sit in a VECTOR column, and I find them with VECTOR_DISTANCE using an HNSW index."

Don't know an answer? Say what you do know and how you would find out: "I haven't used that in production, but it works like X, and I'd check the OCI documentation for Y."

Part of The DBA's Guide to Oracle Agentic AI Foundations. For the full picture in one diagram, see the Agentic AI Cram Board. Independent study notes, not official Oracle material.

DBA AGENTIC AI FOUNDATIONS

The DBA's Guide to Oracle Agentic AI Foundations · 1Z0-1157-26

Agentic AI Cram Board

One picture that connects all six exam modules, a 60-second way to explain it, and a sticky note per module. Colours match: each box in the diagram is the same colour as its note.

40 questions60 minutes65% = 26 correct$0 on MyLearn

The whole picture

Follow a request from left to right. Every exam module sits somewhere on this path.

M1Agent basics M2Reasoning & memory M3LangChain & Agents SDK M4MCP M5OCI agents M6Oracle AI Database

Explain it in 60 seconds

If someone asks "what is agentic AI?", walk the diagram in this order.

  1. A user's request reaches the agent endpoint. Sessions keep the conversation, and guardrails check what goes in and comes out.M5
  2. Behind it sits the agent: an LLM with instructions, memory and a plan. It doesn't just answer; it works toward a goal.M1
  3. It thinks in a loop: Thought → Action → Observation. That pattern is ReAct. Big jobs can be split across a supervisor and specialist agents.M2
  4. To act, it calls tools. The LLM only asks; the application runs them. RAG for documents, SQL for tables, custom functions for APIs.M5
  5. Outside tools plug in through MCP, a standard plug like an Oracle listener: client to server over JSON-RPC, exposing tools, resources and prompts.M4
  6. The knowledge lives in Oracle AI Database as vectors, found by meaning with VECTOR_DISTANCE and HNSW or IVF indexes. Select AI turns English into SQL.M6
  7. Developers build all of this with LangChain, LangGraph or the OpenAI Agents SDK.M3

Sticky notes

One per module. Read the "Say it" line out loud; if you can explain that sentence, you know the module.

Module 1

What is an agent?

  • Agent = LLM + Instructions + Tools + Memory + Planning
  • Trick: "My Instructions Tell Me Plans"
  • Loop: Perceive → Reason → Act → Observe → repeat
  • LLM requests a tool; the app executes it (function calling)
  • Fixed steps = workflow · LLM picks steps = agent
  • Human-in-the-loop → on-the-loop → autonomous
  • Grounding (tools, RAG) cuts hallucination

Say itAn agent is an LLM that plans and uses tools in a loop until the goal is met.

Module 2

How agents think

  • CoT: step-by-step reasoning, no tools
  • ReAct: Thought → Action → Observation
  • Plan-and-Execute: full plan first
  • Reflection: critiques its own output
  • Supervisor delegates · Handoff gives control away · Agent-as-tool keeps control
  • Memory: short = context window · long = vector store
  • Guardrails on input + output, least privilege, human approval, max iterations, tracing

Say itReAct is the DBA way: think, run a query, read the result, think again.

Module 3

The toolbox

  • Loader → Splitter → Embeddings → Vector store → Retriever
  • Prompt template · Chat model · Output parser
  • LCEL: prompt | model | parser
  • LangGraph: nodes, edges, state, checkpointer → loops and branches
  • Agents SDK: Agent, Runner, @function_tool, Handoff, Guardrails (tripwire), Sessions, Tracing

Say itStraight line is a chain; loops and branches need LangGraph.

Module 4

MCP

  • Open standard, "USB-C of AI", fixes the M×N problem
  • Host holds clients; each client ↔ one server
  • T-R-P: Tools = model · Resources = app (read-only) · Prompts = user
  • Client offers: Sampling · Roots · Elicitation
  • JSON-RPC 2.0 · stdio local · Streamable HTTP remote
  • tools/list to discover, tools/call to run
  • Consent, least privilege, OAuth

Say itMCP is the Oracle listener for AI: one standard way for any app to reach any tool.

Module 5

Agents on OCI

  • Agent → Tools → Knowledge base → Endpoint
  • Documents → RAG tool (with citations)
  • Tables → SQL tool (give schema description)
  • Actions → custom function tool
  • KB stores: Object Storage · OpenSearch · Oracle AI DB
  • New files? Rerun the ingestion job
  • Endpoint: sessions, guardrails (moderation, injection, PII), citations, trace
  • Access via IAM policies + compartments

Say itDocuments go to RAG, tables go to SQL, actions go to a custom tool.

Module 6

Oracle AI Database

  • VECTOR(768, FLOAT32) column
  • VECTOR_EMBEDDING + ONNX model in DB
  • DBMS_VECTOR_CHAIN: text → chunks → embeddings
  • ORDER BY VECTOR_DISTANCE(..., COSINE) FETCH FIRST 5
  • HNSW = in-memory graph · IVF = partitions, scale
  • Exact = 100% · APPROX ... TARGET ACCURACY = index
  • RAG: load → chunk → embed → store → embed question → search → LLM → answer
  • Select AI: DBMS_CLOUD_AI + AI profile; showsql shows, doesn't run

Say itThe database is the agent's memory: vectors next to tables, under the security we already manage.

Exam day

Before you click Start

  • 40 questions · 60 minutes · ~90 seconds each
  • Need 26 correct (65%)
  • Answer easy ones first, mark hard ones, come back
  • Cut two wrong options, then choose
  • Match keywords: "plan first", "critic", "read-only", "in-memory"
  • Never leave a question blank
  • Quiet room, ID ready, other apps closed

Say itI know the picture, so I can work out any question from it.

Independent study notes for Oracle Agentic AI Foundations Associate (1Z0-1157-26). Check current exam details and service names on Oracle MyLearn.