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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Features and Capabilities | 30% | - Configure and use AI Vector Search - Implement Select AI for natural language to SQL generation - Apply AI-assisted performance tuning and optimization - Use Oracle Machine Learning within Autonomous Database |
| Topic 2: Autonomous Database Architecture and Provisioning | 25% | - Provision and configure Autonomous Database on dedicated infrastructure - Describe Autonomous Database architecture and core components - Provision and configure Autonomous Database on shared infrastructure - Integrate Autonomous Database with Oracle Cloud Infrastructure services |
| Topic 3: Security, Integration and Data Management | 20% | - Use Autonomous Database tools for development and administration - Configure identity and access management - Implement data encryption and security controls - Load, migrate and integrate data from various sources |
| Topic 4: Database Management and Operations | 25% | - Implement backup, recovery and high availability strategies - Perform patching, upgrades and lifecycle management - Monitor and manage database performance and resources - Configure auto-scaling and storage management |
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NEW QUESTION # 69
Which statement is true when you provision an Autonomous Database using the cloning method?
Answer: D
Explanation:
Cloning in Autonomous Database creates a new instance from an existing source:
Correct Answer (A): “A clone database source can be a running database instance” is true. You can clone from a live, running Autonomous Database instance (full clone), capturing its current state, including data and configuration, without needing to stop it.
Incorrect Options:
B: There are multiple cloning types: full clone (from a running instance) and refreshable clone (a read-only copy that syncs with the source), plus cloning from backups.
C: Cloning from a backup requires the backup to be at least 2 hours old for consistency, not less, making this false.
D: Security zone rules prevent cloning a database from a security zone to a non-security zone due to compliance restrictions.
This flexibility supports rapid provisioning from active databases.
NEW QUESTION # 70
Which statement is FALSE about Autonomous Database maintenance on Dedicated Infrastructure?
Answer: B
Explanation:
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.
NEW QUESTION # 71
What are three characteristics of Data Lake data captured in Object Storage? (Choose three.)
Answer: A,C,D
Explanation:
Data Lakes in OCI Object Storage store raw data for analysis. The three correct characteristics are:
Schema on read (C): Data Lakes store data in its raw, native format (e.g., JSON, CSV, Parquet) without a predefined schema. The schema is applied when data is read or processed, not when written, offering flexibility. For example, a Parquet file with sales data might be queried with SQL only when analyzed, not structured upfront like in a database.
Multiple subject areas (D): Data Lakes aggregate data from diverse sources—sales, HR, IoT—spanning multiple subject areas. This enables cross-domain analysis, like combining customer data with weather data for insights, all stored in a single OCI bucket.
Mixed data types (E): Data Lakes support varied formats: structured (e.g., CSV tables), semi-structured (e.g., JSON documents), and unstructured (e.g., videos). For instance, a bucket might hold CSV logs, JSON events, and image files, all accessible for processing.
The incorrect options are:
High concurrency (A): Data Lakes in Object Storage are not designed for high-concurrency transactional access (e.g., thousands of simultaneous updates). They’re optimized for batch processing or analytics, unlike ATP’s concurrency focus.
High transaction performance (B): Transactional performance (e.g., fast commits) is a database strength, not a Data Lake’s. Object Storage prioritizes scalability and durability over transactional speed, making it unsuitable for OLTP workloads.
These traits make Data Lakes ideal for big data analytics, not real-time transactions.
NEW QUESTION # 72
Which three are use cases for Graph Studio? (Choose three.)
Answer: B,D,E
Explanation:
Graph Studio in Autonomous Database supports graph-based analysis:
Correct Answer (C): Pattern matching identifies relationships (e.g., fraud rings) using graph queries like PGQL.
Correct Answer (D): Clustering groups related nodes (e.g., communities) using graph algorithms.
Correct Answer (B): Churn analysis leverages graph relationships to predict customer loss (e.g., via influence networks), though less emphasized than C and D, it’s a valid use case.
Incorrect Options:
A: Facial recognition is image-based, not graph-based.
E: 3-D modelling is a misinterpretation; it’s not a primary graph use case.
These align with Graph Studio’s strengths in relationship analysis.
NEW QUESTION # 73
You want to enable automatic indexing in a database, but any new auto indexes should be created as invisible indexes, so that they cannot be used in SQL statements. Which command will you use?
Answer: A
Explanation:
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.
NEW QUESTION # 74
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