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Oracle 1Z0-931-25 Exam Syllabus Topics:

Topic
Details

Topic 1

Autonomous Database Shared: This section of the exam measures the skills of Cloud Engineers and focuses on creating and managing shared Autonomous Database instances. It includes provisioning, scaling, and starting or stopping instances, as well as database consolidation with Elastic Resource Pools. It also covers user management, cloning, database migration, monitoring, backup and restore processes, and introduces Data Guard for high availability, ensuring cloud engineers can maintain optimal database performance.

Topic 2

Migrating to Autonomous Database: This section of the exam measures the skills of Cloud Migration Specialists and covers strategies for migrating existing databases to Autonomous Database. It includes understanding migration considerations, and available options, and using Oracle Data Pump to transfer data seamlessly while minimizing downtime, ensuring smooth transitions to Oracle Cloud infrastructure.

Topic 3

Developing on Autonomous Database: This section of the exam measures the skills of Application Developers and focuses on developing and extending applications using Autonomous Database. It covers using generative AI for natural language queries, Autonomous JSON Database, Oracle Text for document search, location-based analysis with Autonomous Spatial, Autonomous Graph for data relationships, and integration with Object Storage, enabling developers to build intelligent, scalable applications.

Topic 4

Autonomous Database Tools: This section of the exam measures the skills of Data Analysts and covers the tools available within Autonomous Databases for advanced data processing and analytics. It includes Oracle Machine Learning, APEX, and SQL Developer Web for database development, as well as data transformation, business model creation, data insights, and data analysis, allowing analysts to extract valuable insights from large datasets.

Topic 5

Data Lake Analytics with Autonomous Database: This section of the exam measures the skills of Big Data Engineers and explores how Autonomous Database can be used for analytics in data lake environments. It includes data ingestion, query optimization, and leveraging cloud-native analytics services, ensuring engineers can efficiently process and analyze large volumes of structured and unstructured data.

Topic 6

Autonomous Database Dedicated: This section of the exam measures the skills of IT Architects and explores the workflows and functionality of Autonomous Database Dedicated and Autonomous Database Cloud@Customer. It includes provisioning dedicated resources, setting up OCI policies, monitoring infrastructure, scheduling maintenance tasks such as patching, and managing encryption keys for enhanced security. IT Architects will learn how to integrate dedicated database environments within their cloud strategy.

Topic 7

Managing and Maintaining Autonomous Database: This section of the exam measures the skills of Database Administrators and focuses on the ongoing management and maintenance of Autonomous Database instances. It includes using REST APIs and OCI CLI for automation, configuring access control lists and private endpoints, monitoring performance, setting up notifications, utilizing features like auto-indexing and data safe, handling connectivity through wallets and service handles, and configuring disaster recovery using Data Guard to ensure business continuity.

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Oracle Autonomous Database Cloud 2025 Professional Sample Questions (Q13-Q18):

NEW QUESTION # 13
Which statement is true about connecting a marketplace Oracle Cloud Infrastructure (OCI) image to an Autonomous Database on Shared Infrastructure?

A. The Autonomous Database must belong to a Network Security Group (NSG).
B. The marketplace OCI image does not support connecting to an Autonomous Database on Shared Infrastructure.
C. A Private Endpoint is required to connect to the Autonomous Database on Shared Exadata infrastructure.
D. The marketplace OCI image can be used with an Always Free Autonomous Database account.

Answer: D

Explanation:
Full Detailed In-Depth Explanation:
Connecting a marketplace OCI image (e.g., a pre-configured VM) to an Autonomous Database on Shared Infrastructure involves network and compatibility considerations. Let's analyze each option:
A . The Autonomous Database must belong to a Network Security Group (NSG): False. NSGs enhance security but are optional. Connectivity can be achieved without them using security lists or other rules.
B . The marketplace OCI image can be used with an Always Free Autonomous Database account: True. The Always Free tier supports connectivity from OCI marketplace images, provided network configurations (e.g., VCN, subnet) allow it. This makes B the correct answer.
C . A Private Endpoint is required to connect to the Autonomous Database on Shared Exadata infrastructure: False. While private endpoints enhance security, public endpoints are also supported for shared infrastructure, making this optional.
D . The marketplace OCI image does not support connecting to an Autonomous Database on Shared Infrastructure: False. Marketplace images are designed to integrate with OCI services, including Autonomous Databases on shared infrastructure.
Option B stands out as it aligns with Oracle's support for Always Free tiers, offering flexibility for users to leverage marketplace tools without additional cost.

NEW QUESTION # 14
A corporation is building a web application to allow its customers to schedule service requests online. There is also a need to run operational reports at times during non-peak hours. The architecture team is debating whether such reports should be run on the OLTP database or in a separate data mart. The DBA Manager does not want to add any more admin responsibility to the team and is looking for a database option that's low to zero maintenance, but meets their strict performance requirements as well. Which Oracle Cloud Infrastructure database service is appropriate for this scenario?

A. ATP using 'tpurgent' and 'high' TNS services to separate connection types
B. It is best to build a separate data warehouse, and move the OLTP data on a nightly basis
C. Since the application needs to be highly available, it should be deployed on a Kubernetes Cluster
D. ADW since operational reporting is a higher priority in this scenario

Answer: A

Explanation:
The scenario requires a low-maintenance, high-performance database for an OLTP web application with occasional reporting. The correct answer is:
ATP using 'tpurgent' and 'high' TNS services to separate connection types (A): Autonomous Transaction Processing (ATP) is ideal here. It's a fully managed database optimized for OLTP workloads (e.g., scheduling service requests) with zero maintenance overhead-Oracle handles patching, backups, and tuning. ATP supports multiple connection services:
'tpurgent': Prioritizes low-latency, time-critical transactions (e.g., customer scheduling requests), ensuring fast response times for the web app.
'high': Suited for high-concurrency or reporting queries, allowing operational reports to run during non-peak hours without impacting the OLTP workload.
By using these predefined services, the architecture separates transactional and reporting workloads within the same database, meeting performance needs without additional administration. For example, the web app connects via tpurgent for real-time updates, while a reporting tool uses high for batch queries at night, leveraging ATP's auto-scaling if needed.
The incorrect options are:
Since the application needs to be highly available, it should be deployed on a Kubernetes Cluster (B): This misinterprets the question-it's about the database service, not the application deployment. Kubernetes is for container orchestration, not a database solution, and adds complexity counter to the low-maintenance goal.
It is best to build a separate data warehouse, and move the OLTP data on a nightly basis (C): While a separate Autonomous Data Warehouse (ADW) could handle reporting, it requires data movement (e.g., via ETL), increasing admin effort and complexity, which the DBA Manager wants to avoid. ATP can handle both workloads with proper service separation.
ADW since operational reporting is a higher priority in this scenario (D): ADW is optimized for analytics, not OLTP. The web app's transactional needs are primary, with reporting secondary and occasional, making ATP more suitable.
ATP's self-managing nature and service flexibility make it the best fit.

NEW QUESTION # 15
As a database architect, you are tasked with configuring a high concurrency, production OLTP (EXAM) application to connect to an Autonomous Transaction Processing database with a requirement to have some reporting queries run in parallel mode. Which connection service is appropriate for such a workload?

A. TP
B. HIGH
C. TPURGENT
D. MEDIUM

Answer: B

Explanation:
Autonomous Transaction Processing (ATP) provides predefined connection services to optimize different workload types. For a high-concurrency OLTP application with parallel reporting queries, the appropriate service is:
HIGH (C): The HIGH connection service in ATP is designed for workloads requiring high concurrency and throughput, making it ideal for a production OLTP application like "EXAM" that handles many simultaneous transactions (e.g., user requests). Additionally, HIGH supports parallel query execution, which is critical for running reporting queries efficiently during off-peak times. It allocates more resources (e.g., OCPUs and I/O) compared to other services, ensuring performance for both transactional and analytical tasks. For example, the OLTP app might use HIGH to process thousands of concurrent user requests, while a nightly report leverages parallel processing to aggregate data quickly. In the wallet's tnsnames.ora, the HIGH service might look like exam_high = (DESCRIPTION=...), offering the best balance for this mixed workload.
The incorrect options are:
TPURGENT (A): The TPURGENT service prioritizes low-latency, time-critical transactions (e.g., sub-second response times for single-user actions). It's optimized for latency-sensitive operations, not high concurrency or parallel reporting, and would underperform for the broader OLTP and reporting needs here. For instance, it's better suited for a single critical transaction than a multi-user system.
TP (B): The TP (Transaction Processing) service is a general-purpose option for OLTP workloads, offering moderate concurrency and latency. However, it's not optimized for high concurrency or parallel query execution, making it less suitable for a production app with reporting demands. It's a middle ground, not a top-tier performer like HIGH.
MEDIUM (D): The MEDIUM service balances performance and resource usage for moderate workloads. It supports some concurrency but lacks the resource allocation and parallel execution capabilities of HIGH, making it inadequate for a high-concurrency OLTP system with reporting requirements.
The HIGH service's ability to handle both high transactional volume and parallel reporting aligns perfectly with the scenario's strict performance needs, all within ATP's zero-maintenance framework.

NEW QUESTION # 16
Which set of Oracle Cloud Infrastructure metrics is available only for Autonomous Database on Dedicated Infrastructure?

A. Transaction Count, Running Statements, Sessions
B. IOPS, Storage used by tablespace, Apply and Transport Lag
C. CPU Utilization, Storage Utilization
D. Query Latency, User Calls, Execute Count

Answer: B

Explanation:
Autonomous Database on Dedicated Infrastructure provides unique metrics due to its Exadata underpinnings:
Correct Answer (C): "IOPS, Storage used by tablespace, Apply and Transport Lag" are specific to dedicated deployments:
IOPS: Measures input/output operations per second, reflecting Exadata storage performance.
Storage used by tablespace: Tracks storage allocation at the tablespace level, unique to dedicated infrastructure's granular control.
Apply and Transport Lag: Relevant for Data Guard configurations, showing replication delays, available only in dedicated setups with standby databases.
Incorrect Options:
A: Transaction Count, Running Statements, and Sessions are general metrics available in both shared and dedicated environments.
B: CPU and Storage Utilization are standard across all Autonomous Database types.
D: Query Latency, User Calls, and Execute Count are also common metrics, not exclusive to dedicated infrastructure.
These metrics offer deeper insights into dedicated Exadata performance.

NEW QUESTION # 17
Which two optimizations are different between Autonomous Data Warehouse and Autonomous Transaction Processing? (Choose two.)

A. Data Organization
B. Memory Usage
C. Undo Management
D. Backup Retention

Answer: A,B

Explanation:
Autonomous Data Warehouse (ADW) and Autonomous Transaction Processing (ATP) are optimized for different workloads, reflected in their configurations. The two differing optimizations are:
Memory Usage (A): ADW and ATP allocate memory differently to suit their purposes. ADW prioritizes a larger data cache (part of the SGA) to keep more data in memory, boosting analytical query performance (e.g., aggregations over millions of rows). For example, a SELECT SUM(sales) GROUP BY region runs faster with more cached data. ATP, conversely, balances memory across the SGA and PGA for transactional workloads, emphasizing concurrency and quick row-level operations (e.g., UPDATE orders SET status = 'shipped'). This difference ensures ADW excels at scan-heavy analytics, while ATP handles high-throughput updates.
Data Organization (B): ADW uses a columnar storage format (e.g., Hybrid Columnar Compression) optimized for analytics, storing data by column to speed up aggregations and reduce I/O (e.g., scanning only the sales column for a SUM). ATP uses a row-based format suited for OLTP, enabling fast single-row access and updates (e.g., retrieving or modifying a specific order_id). For instance, inserting a row in ATP is efficient due to row storage, while ADW's columnar format accelerates SELECT AVG(price) FROM products.
The incorrect options are:
Backup Retention (C): Both ADW and ATP use the same automatic backup system (via Oracle's Automatic Workload Repository), with a default 60-day retention adjustable by users. There's no optimization difference here; it's a shared managed feature.
Undo Management (D): Both databases use Oracle's Flashback technology for undo (e.g., rolling back transactions or querying past states), with retention periods set similarly. Undo is managed automatically in both, not optimized differently.
These optimizations tailor ADW for analytics and ATP for transactions, despite their shared autonomous foundation.

NEW QUESTION # 18
......

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