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Oracle 1Z0-931-25 Exam Syllabus Topics:

Topic
Details

Topic 1

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 2

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 3

Getting Started with Autonomous Database: This section of the exam measures the skills of Database Administrators and covers the architecture and key features of Oracle Autonomous Database. It explains how the database integrates within the Oracle ecosystem and provides an overview of different Autonomous Database offerings and their licensing models, helping administrators understand how to deploy and manage these cloud-based databases efficiently.

Topic 4

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.

Oracle Autonomous Database Cloud 2025 Professional Sample Questions (Q22-Q27):

NEW QUESTION # 22
In which four ways can Oracle Database optimally access data in Object Storage? (Choose four.)

A. Optimized data archive using hybrid partitioned tables
B. Scan avoidance using block skipping when reading parquet and orc files
C. Optimized data archive using partitioned external tables
D. Scan avoidance using columnar pruning for .csv files
E. Scan avoidance using columnar pruning for columnar stores like parquet and orc
F. Scan avoidance using partitioned external tables

Answer: A,C,E,F

Explanation:
Oracle Database provides several techniques to optimize data access from Object Storage, particularly in the context of Autonomous Database, leveraging external tables and advanced storage formats. The question asks for four correct methods, and based on Oracle documentation, the following are the most applicable:
Correct Answer (A): Scan avoidance using partitioned external tables
Partitioned external tables allow Oracle Database to skip irrelevant partitions when querying data stored in Object Storage. By organizing data into partitions (e.g., by date or region), the database engine can prune partitions that don't match the query predicates, significantly reducing the amount of data scanned and improving performance. This is a well-documented optimization for external data access in Oracle Database and Autonomous Database environments.
Correct Answer (D): Scan avoidance using columnar pruning for columnar stores like parquet and orc Columnar pruning is a technique where only the required columns are read from columnar file formats such as Parquet or ORC stored in Object Storage. These formats store data column-wise, enabling the database to avoid scanning entire rows or irrelevant columns, which is particularly efficient for analytical queries common in Autonomous Data Warehouse (ADW). This is a standard optimization supported by Oracle's external table framework when accessing Object Storage.
Correct Answer (E): Optimized data archive using hybrid partitioned tables Hybrid partitioned tables combine local database partitions with external partitions stored in Object Storage. This allows older, less frequently accessed data to be archived efficiently in the cloud while remaining queryable alongside active data in the database. The database optimizes access by seamlessly integrating these partitions, reducing costs and improving archival efficiency. This feature is explicitly supported in Oracle Database and enhanced in Autonomous Database for data lifecycle management.
Correct Answer (F): Optimized data archive using partitioned external tables Similar to hybrid partitioned tables, using partitioned external tables alone optimizes data archiving by storing historical data in Object Storage with partitioning (e.g., by year). This method enables efficient querying of archived data by pruning unneeded partitions, offering a cost-effective and scalable archival solution. It's a distinct approach from hybrid tables, focusing solely on external storage, and is widely used in Oracle environments.
Incorrect Options:
B . Scan avoidance using columnar pruning for .csv files
CSV files are row-based, not columnar, and lack the internal structure of formats like Parquet or ORC. While Oracle can read CSVs from Object Storage via external tables, columnar pruning is not applicable because CSVs don't support column-wise storage or metadata for pruning. This makes this option incorrect as a specific optimization technique, though basic predicate pushdown might still reduce scanning to some extent.
C . Scan avoidance using block skipping when reading parquet and orc files Block skipping (or row group skipping) is a feature in some database systems where metadata in Parquet or ORC files allows skipping entire blocks of data based on query filters. While Oracle supports Parquet and ORC through external tables and can leverage their columnar nature (via pruning), "block skipping" is not explicitly highlighted as a primary optimization in Oracle's documentation for Autonomous Database. It's more commonly associated with systems like Apache Spark or Hive. Oracle's focus is on columnar pruning and partitioning, making this option less accurate in this context.
Why Four Answers?
The question specifies "four ways," and while six options are provided, A, D, E, and F are the most directly supported and documented methods in Oracle Autonomous Database for optimizing Object Storage access. Options B and C, while conceptually related to data access optimizations, are either inapplicable (CSV lacks columnar structure) or not explicitly emphasized (block skipping) in Oracle's feature set for this purpose.
This selection aligns with Oracle's focus on partitioning and columnar formats for efficient cloud data access, ensuring both performance and archival optimization.
Reference:
External Tables and Object Storage
Hybrid Partitioned Tables
Autonomous Database Data Loading

NEW QUESTION # 23
Which three tools can be used to monitor the usage and activities of Autonomous Database on Dedicated Infrastructure? (Choose three.)

A. Performance Hub
B. Enterprise Manager Cloud Control
C. RMAN
D. OCI Metrics
E. Logs

Answer: A,B,D

Explanation:
Monitoring Autonomous Database on Dedicated Infrastructure involves specific tools:
Correct Answer (C): Enterprise Manager Cloud Control provides comprehensive monitoring of OCI resources, including CPU, memory, I/O, and SQL performance for dedicated deployments.
Correct Answer (D): OCI Metrics offers detailed metrics via the OCI Monitoring service, allowing custom dashboards and alerts for key performance indicators (e.g., OCPU usage, storage).
Correct Answer (E): Performance Hub is a built-in tool in Autonomous Database for analyzing historical performance data, identifying trends, and troubleshooting issues.
Incorrect Options:
A: RMAN (Recovery Manager) is for backup and recovery, not real-time monitoring of usage or activities.
B: Logs provide diagnostic data but are not a standalone monitoring tool; they require manual analysis or integration with other tools.
These tools collectively enable proactive management and optimization.

NEW QUESTION # 24
What is a best practice when planning and instituting access controls for your Autonomous Dedicated environment regarding subnets, compartments, and user groups?

A. Only 1 of each is allowed to be allocated per environment.
B. Create a separate VCN that contains only public subnets.
C. Create at least 2 of each resource.
D. Only 1 subnet and 1 compartment are allowed, multiple groups highly advised.

Answer: C

Explanation:
When designing access controls for an Autonomous Dedicated environment, Oracle recommends redundancy and separation for security and manageability:
Correct Answer (D): "Create at least 2 of each resource" (subnets, compartments, user groups) is a best practice:
Subnets: Using at least two subnets (e.g., one public, one private) enhances security by segregating traffic and provides failover options if one subnet encounters issues.
Compartments: Multiple compartments allow logical separation (e.g., dev, test, prod), simplifying access control and resource management.
User Groups: At least two groups (e.g., admins, developers) streamline permission assignments and reduce the risk of over-privileging users.
Incorrect Options:
A: OCI allows multiple subnets and compartments; restricting to one limits flexibility and security.
B: There's no such limitation; multiple resources are supported and encouraged.
C: A VCN with only public subnets contradicts security best practices, as private subnets are critical for database isolation.
This approach aligns with Oracle's guidance for secure and scalable deployments.

NEW QUESTION # 25
What are two advantages of using Data Pump to migrate your Oracle databases to Autonomous Database? (Choose two.)

A. Data Pump is faster to migrate database than using RMAN
B. Data Pump creates the tablespaces used by your Autonomous Database
C. Data Pump is platform-independent - it can migrate Oracle databases running on any platform
D. Data Pump can exclude migration of objects like indexes and materialized views that are not needed by Autonomous Database

Answer: C,D

Explanation:
Oracle Data Pump is a powerful tool for migrating databases to Autonomous Database (ADB). The two correct advantages are:
Data Pump can exclude migration of objects like indexes and materialized views that are not needed by Autonomous Database (C): True. Data Pump's EXCLUDE parameter allows skipping objects like indexes and materialized views during export (e.g., expdp ... EXCLUDE=INDEX,MATERIALIZED_VIEW). In ADB, these objects are often redundant because the database automatically creates and manages them based on workload (e.g., auto-indexing). For example, exporting a schema with EXCLUDE=INDEX from an on-premises database reduces migration overhead, as ADB recreates necessary indexes post-import, optimizing for its managed environment. This flexibility minimizes unnecessary data transfer and speeds up the process.
Data Pump is platform-independent - it can migrate Oracle databases running on any platform (D): True. Data Pump uses a platform-agnostic dump file format (.dmp), enabling migration from any Oracle Database (e.g., on Linux, Windows, or Solaris) to ADB in the cloud. For instance, you could export from an Oracle 12c database on AIX, upload the dump to OCI Object Storage, and import it into ADB using DBMS_CLOUD.COPY_DATA, regardless of endianness or OS differences (handled by Data Pump's conversion). This universality makes it ideal for heterogeneous migrations.
The incorrect options are:
Data Pump is faster to migrate database than using RMAN (A): False. Speed depends on context-RMAN (Recovery Manager) is faster for physical migrations (e.g., restoring backups) of entire databases, especially large ones, due to block-level copying. Data Pump, a logical migration tool, extracts and loads data row-by-row, which can be slower for massive datasets but offers more control (e.g., schema selection). No universal speed advantage exists; it's workload-specific.
Data Pump creates the tablespaces used by your Autonomous Database (B): False. In ADB, tablespaces are fully managed by Oracle (e.g., DATA tablespace), and Data Pump doesn't create them. It imports data into existing, pre-allocated storage, ignoring source tablespace definitions. E.g., a source tablespace USERS is mapped to ADB's default storage, not recreated.
These advantages make Data Pump a versatile, tailored migration solution for ADB.

NEW QUESTION # 26
During the provisioning of an Autonomous Database on dedicated hardware, what should the database administrator select to determine where the Autonomous Database is created on the dedicated infrastructure?

A. The container database in which the Autonomous instance should run.
B. The Listener endpoint where the Autonomous Database should register on.
C. The compartment of the dedicated infrastructure because each compartment only runs one dedicated environment.
D. The Client Credentials wallet, which is used to connect to the Autonomous Database.

Answer: A

Explanation:
Full Detailed In-Depth Explanation:
When provisioning an Autonomous Database (ADB) on dedicated infrastructure, the placement within the infrastructure is critical:
A . Listener endpoint: Incorrect. This is for connectivity, not placement.
B . Client Credentials wallet: Incorrect. This is for secure connections, not determining location.
C . The container database in which the Autonomous instance should run: Correct. On dedicated infrastructure, an Autonomous Container Database (ACD) hosts multiple ADB instances. Selecting the ACD during provisioning determines where the ADB resides, affecting resource allocation and isolation.
D . The compartment: Incorrect. Compartments organize resources in OCI, but multiple ACDs can exist within a compartment, so this doesn't pinpoint the exact location.
Choosing the ACD is a key provisioning step for dedicated deployments.

NEW QUESTION # 27
......

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Fred Reed Fred Reed

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