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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Security, Integration and Data Management | 20% | - Configure identity and access management - Implement data encryption and security controls - Load, migrate and integrate data from various sources - Use Autonomous Database tools for development and administration |
| Topic 2: Database Management and Operations | 25% | - Perform patching, upgrades and lifecycle management - Implement backup, recovery and high availability strategies - Monitor and manage database performance and resources - Configure auto-scaling and storage management |
| Topic 3: AI Features and Capabilities | 30% | - Apply AI-assisted performance tuning and optimization - Implement Select AI for natural language to SQL generation - Configure and use AI Vector Search - Use Oracle Machine Learning within Autonomous Database |
| Topic 4: Autonomous Database Architecture and Provisioning | 25% | - Describe Autonomous Database architecture and core components - Provision and configure Autonomous Database on shared infrastructure - Integrate Autonomous Database with Oracle Cloud Infrastructure services - Provision and configure Autonomous Database on dedicated infrastructure |
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NEW QUESTION # 120
Which two statements are true about Data Insights?
Answer: B,D
Explanation:
Full Detailed In-Depth Explanation:
A: True. Data Insights works with tables and business models in ADW.
B: False. Exclusive to Autonomous Database, not on-premises.
C: True. Search history is retrievable.
D: False. Included in ADW, not separately priced.
NEW QUESTION # 121
Autonomous Database's auto scaling feature allows your database to use up to three times the current base number of OCPU cores at any time. As demand increases, auto scaling automatically increases the number of cores in use. Likewise, as demand drops, auto scaling automatically decreases the number of cores in use. Which statement is FALSE regarding the auto scaling feature?
Answer: B
Explanation:
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” is false. 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.
D: Auto scaling is not enabled by default (must be explicitly activated) and can be toggled on/off, though this statement’s phrasing could be clearer—it’s still true in context.
This guarantees predictable minimum performance while allowing flexibility for peak loads.
NEW QUESTION # 122
Which option should you use to create a graph with Graph Studio?
Answer: B
Explanation:
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.
E: A license key isn’t a creation method; graph features are included with Autonomous Database.
This integrates graph capabilities with existing data.
NEW QUESTION # 123
Which three are use cases for Graph Studio? (Choose three.)
Answer: A,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 # 124
Which statement is FALSE about loading data into the Autonomous Database using the Data Load card in Database Actions?
Answer: D
Explanation:
The Data Load card in Database Actions (within ADB’s web interface) simplifies data loading. The false statement is:
You must first manually create your database credentials using DBMS_CLOUD.CREATE_CREDENTIAL before accessing your Oracle Object Storage Bucket (D): This is incorrect. The Data Load card automates credential management for Oracle Object Storage by leveraging the ADB instance’s IAM permissions. When you select an OCI Object Storage bucket in the UI, it uses the instance’s resource principal or user OCI credentials (e.g., from your signed-in OCI session), eliminating the need to manually run DBMS_CLOUD.CREATE_CREDENTIAL. For example, uploading sales.csv from a bucket via the Data Load card requires only bucket selection and file mapping—no PL/SQL credential setup. This automation enhances usability, contrasting with manual methods where CREATE_CREDENTIAL is needed (e.g., in SQL scripts).
The true statements are:
Data formats supported include: text, CSV, JSON, Avro, and Parquet (A): The Data Load card supports these formats, parsing them into tables using DBMS_CLOUD under the hood. E.g., a JSON file { "id": 1, "name": "John" } loads as rows.
Data can be loaded from a local data source (B): You can upload files directly from your local machine (e.g., a CSV on your desktop) via the browser interface, staging them temporarily for loading.
Data can be uploaded from several cloud storage sources including S3, Azure, Google Cloud, and Oracle Object Storage (C): The card supports these external cloud sources, requiring credentials (e.g., AWS keys), alongside native OCI Object Storage integration.
Data can be loaded from a remote database using Database Links (E): DBLinks allow pulling data from another Oracle database (e.g., INSERT INTO local_table SELECT * FROM remote_table@link), supported in the Data Load card.
The automation of credentials in D is a key differentiator for the UI-based Data Load feature.
NEW QUESTION # 125
......
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