1z0-931-25 Oracle Autonomous Database Cloud 2025 Professional Free Practice Exam Questions (2025 Updated)

Autonomous Database on dedicated infrastructure supports multiple workload types. The correct answer is:

ATP and ADW (C):Autonomous Database on dedicated infrastructure supports both Autonomous Transaction Processing (ATP) for OLTP workloads (high concurrency, low latency) and Autonomous Data Warehouse (ADW) for analytical workloads (highthroughput, complex queries). This dual support allows flexibility within a single dedicated Exadata infrastructure.

The incorrect options are:

Autonomous Transaction Processing only (A):Incorrect, as ADW is also supported.

Hybrid Columnar Compression (B):HCC is a data compression feature, not a workload type; it’s used within ADW but doesn’t define the workload.

Autonomous Data Warehouse only (D):Incorrect, as ATP is also supported.

This versatility is a key feature of dedicated deployments.

Oracle Cloud Infrastructure (OCI) is designed with a hierarchical structure to ensure high availability and fault tolerance:

Correct Answer (B): “An OCI region is a localized geographic area, and an availability domain is one or more data centers located within a region” accurately describes OCI’s architecture. A region is a standalone geographic area (e.g., US East), and availability domains (ADs) are isolated data centers within that region, enhancing resilience.

Correct Answer (D): “Because availability domains do not share infrastructure such as power or cooling, or the internal availability domain network, a failure at one availability domain within a region is unlikely to impact the availability of the others within the same region” reflects OCI’s design for fault isolation. ADs are physically separate, minimizing the risk of cascading failures.

Incorrect Options:

A: Regions are independent, not dependent, and there’s no 5,000-kilometer restriction; they are globally distributed for latency and redundancy purposes.

Auto scaling in Autonomous Database dynamically adjusts OCPU usage up to three times the base allocation. Let’s evaluate each statement:

Correct Answer (C): “The base number of OCPU cores allocated to a database is not guaranteed” isfalse. The base OCPU count, set during provisioning or manual scaling, is always guaranteed as the minimum available resource, even with auto scaling enabled. Auto scaling only increases usage above this baseline when needed.

True Statements:

A: On dedicated Exadata, the max cores for auto scaling are constrained by available free cores not used by other databases, ensuring resource fairness.

B: The total cores in the Exadata instance define the upper limit for any database’s auto scaling capacity.

Graph query languages, like Oracle’s Property Graph Query Language (PGQL), are designed for graph databases:

Correct Answer (D): “Ability to specify patterns” is a defining feature. Graph queries excel at defining and matching patterns (e.g., nodes and edges) to explore relationships, such as finding paths or subgraphs, critical for applications like social network analysis or fraud detection.

Incorrect Options:

A: Key-value queries are typical of NoSQL key-value stores, not graph databases.

B: While scripting may be possible in some contexts, it’s not a core feature of graph query languages.

Full Detailed In-Depth Explanation:

Maintenance on dedicated infrastructure:

A:True. You can adjust maintenance windows for container databases.

B:False. Exadata Infrastructure maintenance (e.g., OS, grid patching) is separate from container database patching, which occurs independently.

C:True. Oracle reschedules missed maintenance to the next quarter.

D:True. Infrastructure maintenance occurs quarterly.

Compartment quotas in Oracle Cloud Infrastructure (OCI) manage resource allocation:

Correct Answer (D): “Compartment quotas cannot be set on the root compartment” isfalse. Quotas can be applied to any compartment, including the root compartment, to limit resource usage tenancy-wide. While direct quotas on the root are less common (child compartment quotas are typically used), it’s technically possible via OCI policies.

True Statements:

A: Quotas offer flexible resource limits (e.g., OCPUs, storage) via policy statements.

B: Parent compartment quotas take precedence over child quotas, enforcing hierarchical control.

Oracle Spatial Studio is a self-service tool for working with spatial data in Autonomous Database. The three correct functions are:

Map visualization (A):Spatial Studio provides robust capabilities to visualize spatial data on interactive maps, enabling users to explore geographic patterns and relationships visually.

Spatial analysis (C):It offers tools for performing spatial operations like proximityanalysis, spatial joins, and buffering, which are essential for deriving insights from geographic data.

Spatial data editing (E):Users can edit spatial data, such as modifying geometries or updating attributes, directly within Spatial Studio, making it a powerful tool for data management.

The incorrect options are:

Custom SQL queries (B):While Spatial Studio supports spatial operations, it is primarily a graphical tool and does not focus on executing custom SQL queries. Such functionality is more aligned with tools like SQL Developer.

Geocoding (D):Geocoding (converting addresses to coordinates) is not a core feature of Spatial Studio. It focuses on visualization, analysis, and editing rather than address-to-coordinate conversion, which is typically handled by separate Oracle services or tools.

These functions align with Spatial Studio’s purpose of simplifying spatial data management and analysis.

Monitoring Autonomous Database on Dedicated Infrastructure involves specific tools:

Correct Answer (C):Enterprise Manager Cloud Controlprovides comprehensive monitoring of OCI resources, including CPU, memory, I/O, and SQL performance for dedicated deployments.

Correct Answer (D):OCI Metricsoffers 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 Hubis 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.

Third-party software can connect to an Autonomous Database using standard Oracle connectivity options. The correct answer is:

Any protocol supported by Oracle Net Services (B):Autonomous Database leveragesOracle Net Services (also known as SQL*Net) for connectivity, supporting protocols like TCP/IP with TLS for secure communication. This allows a wide range of third-party tools (e.g., SQL Developer, Tableau, or custom applications) to connect using established Oracle networking standards.

The incorrect options are:

New ADB-specific protocols (A):There are no proprietary, ADB-specific protocols; it uses Oracle’s existing Net Services framework.

SQL*NET only (C):While SQLNet is part of Oracle Net Services, the broader term “Oracle Net Services” encompasses all supported protocols, not just SQLNet.

JDBC only (D):JDBC is a specific Java-based API for database connectivity, not a protocol, and it’s just one of many ways to connect via Oracle Net Services.

This flexibility ensures compatibility with diverse client applications.

Oracle Data Pump is a key tool for migrating databases to Autonomous Database. The two objects imported are:

Data (B):Data Pump imports the actual data from the source database into the target Autonomous Database. This includes rows from tables, LOBs, and other data types stored in the dump file (e.g., .dmp). For example, if you export a table CUSTOMERS with 1 million rows, Data Pump imports all that data into ADB using DBMS_CLOUD.COPY_DATA after uploading the dump to OCI Object Storage. This ensures the content of your database is transferred intact.

Schemas (C):Data Pump imports schema definitions, including tables, views, indexes, triggers, and other objects owned by the schema. For instance, exporting a schema HR with tables like EMPLOYEES and DEPARTMENTS will recreate those objects in ADB, preserving their structure. The impdp utility or DBMS_CLOUD handles schema metadata, though some objects (e.g., indexes) may be recreated automatically by ADB’s optimization.

The incorrect options are:

Tablespaces (A):Tablespaces are not imported directly. In Autonomous Database, storage is fully managed, and tablespaces are abstracted away. Data Pump imports data and schemas into ADB’s managed tablespaces (e.g., DATA), not user-defined ones from the source. For example, a source tablespace USERS isn’t replicated; its data is mapped to ADB’s default storage.

Report (D):“Report” is not a database object; it might refer to query outputs or logs, but Data Pump doesn’t import such entities. It focuses on database content, not external artifacts.

This process ensures a smooth migration of data and structure to ADB’s managed environment.

The Catalog tool in Autonomous Database provides metadata exploration:

Correct Answer (A): “You can delete an entity from the Autonomous Database via the Catalog tool” isfalse. The Catalog is a read-only interface for viewing database objects (tables, views, etc.) and their relationships; it does not support modification or deletion actions.

True Statements:

B: Filtering by name, schema, type, or application is a core search feature.

C: Viewing DDL (data definition language) for entities is supported.

Oracle’s automatic indexing feature, available in Autonomous Database, optimizes query performance by creating and managing indexes. The requirement here is to enable it but ensure new indexes are invisible (not used by the optimizer unless explicitly made visible). The correct command is:

EXEC DBMS_AUTO_INDEX.CONFIGURE('AUTO_INDEX_MODE','REPORT ONLY') (C):The REPORT ONLY mode enables automatic indexing, where the database identifies and creates candidate indexes based on workload analysis. However, these indexes are created as invisible by default, meaning the optimizer does not use them in SQL execution plans unless a DBA manually makes them visible (e.g., via ALTER INDEX ... VISIBLE). This mode is ideal for testing or validation without impacting production queries. For example, if a query frequently filters on a column, an invisible index might be created, but it won’t affect performance until explicitly activated.

The incorrect options are:

EXEC DBMS_AUTO_INDEX.CONFIGURE('AUTO_INDEX_MODE','IMPLEMENT') (A):The IMPLEMENT mode fully enables automatic indexing, creating and maintaining visible indexes that the optimizer uses immediately. This contradicts the requirement for invisible indexes.

EXEC DBMS_AUTO_INDEX.CONFIGURE('AUTO_INDEX_MODE','OFF') (B):The OFF mode disables automatic indexing entirely, so no indexes (visible or invisible) are created, failing the requirement to enable it.

EXEC DBMS_AUTO_INDEX.CONFIGURE('AUTO_INDEX_MODE','INVISIBLE') (D):There is no INVISIBLE mode in DBMS_AUTO_INDEX.CONFIGURE. While indexes can be set as invisible individually, this is not a valid configuration option for the auto-indexing feature.

The REPORT ONLY mode provides a controlled approach, allowing index creation for analysis without immediate deployment. This is particularly useful in Autonomous Database, where automation is balanced with DBA oversight.

Full Detailed In-Depth Explanation:

In Autonomous Database on Shared Infrastructure, tablespace quotas are managed using standard SQL commands, as administrative packages like DBMS_CLOUD_ADMIN are restricted in this environment. The correct method is:

ALTER USER MTHEO QUOTA UNLIMITED ON TABLESPACE DATA:This command directly modifies the user’s quota on the DATA tablespace, which is the default tablespace in Autonomous Database. “UNLIMITED” removes any storage restrictions.

Analysis of options:

A:Incorrect. DBMS_CLOUD_ADMIN is used in dedicated infrastructure for advanced administrative tasks, not shared infrastructure for quota changes.

B:Correct. This is the standard SQL syntax supported in shared infrastructure.

C:Incorrect. ALTER TABLESPACE modifies tablespace properties, not user quotas, and isn’t applicable here.

D:Incorrect. ALTER DATABASE syntax is invalid for user quotas; it’s for database-wide settings.

Managing Autonomous Database via REST APIs relies on OCI authentication:

Correct Answer (D): “Database ADMIN password” is not needed. REST API access uses OCI IAM credentials, not database-level credentials like the ADMIN password.

Required Items:

A: User’s OCID identifies the API caller.

B: Tenancy’s OCID specifies the cloud environment.

Compartment quotas in OCI control resource usage within compartments. The incorrect statement is:

Compartment quotas are set by Oracle (A):This is false. Compartment quotas are not set by Oracle; they are defined by OCI administrators using quota policies in the Identity and Access Management (IAM) system. Admins write policies (e.g., set database quota to 2 autonomous-databases in tenancy) to limit resources like Autonomous Databases percompartment, giving customers full control over resource allocation. Oracle provides the framework, but the specific quotas are user-defined, not Oracle-imposed.

The correct statements are:

Along with compartment budgets, compartment quotas create a powerful toolset to manage your spending (B):True. Quotas limit resource creation (e.g., max 5 ADBs), while budgets track spending (e.g., $1000/month), together offering comprehensive cost management. For example, a quota might cap ADB instances, and a budget alerts when costs approach a threshold.

There are three types of quota policy statements which are set, unset, and zero (C):True. SET assigns a quota (e.g., set compute quota to 10 instances), UNSET removes it, and ZERO sets it to none, effectively blocking resource creation. These verbs provide flexible quota management.

Compartment quotas use policies that allow allocation of resources with a high level of flexibility (D):True. Policies are written in OCI’s IAM syntax, allowing granular control (e.g., set database quota to 1 autonomous-database where target.compartment.id = 'ocid1.compartment...'), tailored to specific compartments or resources.

This customer-driven approach distinguishes quotas from Oracle-managed limits.

Graph Studio in Autonomous Database enables graph creation and analysis:

Correct Answer (A): “Tables in an Autonomous Database instance” are used to create graphs. Graph Studio builds property graphs from relational tables by defining vertices (nodes) and edges (relationships) based on existing data, leveraging the database’s schema.

Incorrect Options:

B: Graph analytics algorithms analyze existing graphs, not create them.

C: NoSQL configuration is unrelated; Graph Studio uses relational data.

D: No external tool from oracle.com is required; Graph Studio is a built-in feature.

Oracle Database supports spatial data with a dedicated data type:

Correct Answer (A): SDO_GEOMETRY is the native data type for storing spatial information, such as points, lines, and polygons, in a structured format compatible with spatial queries and operations.

Incorrect Options:

B: SDO_LOCATION is not a valid data type; it may be a confusion with SDO_GEOMETRY.

C: SDO_RELATE is a spatial operator for relationship analysis, not a storage type.

Network Security Groups (NSGs) in OCI control network traffic to Autonomous Database instances:

Correct Answer (D): You can configure up to 5 NSGs for an Autonomous Database. This limit allows sufficient flexibility to define multiple security rules while maintaining manageability. NSGs can be assigned during provisioning or updated later via the OCI Console or API, restricting access to specific IP ranges or VCNs.

Incorrect Options:

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.