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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Features and Capabilities | 30% | - Apply AI-assisted performance tuning and optimization - Use Oracle Machine Learning within Autonomous Database - Implement Select AI for natural language to SQL generation - Configure and use AI Vector Search |
| Topic 2: Database Management and Operations | 25% | - Monitor and manage database performance and resources - Perform patching, upgrades and lifecycle management - Configure auto-scaling and storage management - Implement backup, recovery and high availability strategies |
| Topic 3: Autonomous Database Architecture and Provisioning | 25% | - Provision and configure Autonomous Database on shared infrastructure - Integrate Autonomous Database with Oracle Cloud Infrastructure services - Describe Autonomous Database architecture and core components - Provision and configure Autonomous Database on dedicated infrastructure |
| Topic 4: Security, Integration and Data Management | 20% | - Implement data encryption and security controls - Load, migrate and integrate data from various sources - Configure identity and access management - Use Autonomous Database tools for development and administration |
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NEW QUESTION # 93
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 # 94
What REST verb is used to create an Autonomous Database service using REST APIs?
Answer: C
Explanation:
Full Detailed In-Depth Explanation:
REST APIs use HTTP verbs:
A: True. POST creates new resources, such as an Autonomous Database instance.
B: False. GET retrieves data, not creates.
C: False. PUT updates existing resources.
D: False. INSERT is not a REST verb; it’s SQL-specific.
NEW QUESTION # 95
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?
Answer: D
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 # 96
What are three characteristics of Data Lake data captured in Object Storage? (Choose three.)
Answer: C,D,E
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 # 97
Which statement is false about Autonomous Database Oracle Client Credentials (Wallets)?
Answer: D
Explanation:
Oracle Client Credentials (wallets) are essential for secure connections to the Autonomous Database. The false statement is:
The Wallet for the Autonomous Database is the same as the TDE wallet (A): This is incorrect. The Oracle Client Credential wallet (used for client authentication and connection security) is distinct from the Transparent Data Encryption (TDE) wallet (used to encrypt data at rest within the database). The client wallet contains certificates and keys for mutual TLS (mTLS) authentication, while the TDE wallet manages encryption keys for data security. They serve different purposes and are not the same.
The correct statements are:
You must have an Oracle Client Credential wallet in order to connect to the Autonomous Database (B): True. The wallet is required for mTLS, which is the default authentication method for Autonomous Databases, ensuring secure connections.
In addition to the Oracle Client Credential Wallet, a user must have a username and password in order to connect to the Autonomous Database (C): True. Alongside the wallet, a database username and password are needed for full authentication (mTLS + user credentials).
The Oracle Client Credential file is downloaded as a ZIP file (D): True. When downloaded from the Oracle Cloud Infrastructure (OCI) console, the wallet is provided as a ZIP file containing configuration files like tnsnames.ora and certificates.
This distinction is critical for understanding security mechanisms in Autonomous Database.
NEW QUESTION # 98
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