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| Section | Objectives |
|---|---|
| Topic 1: Performance, Backup and Recovery | - Performance tuning - High availability and disaster recovery - Backup and restore operations |
| Topic 2: Provisioning and Deployment | - Create and provision Autonomous AI Database - Configure networking and connectivity - Select workload type and deployment options |
| Topic 3: Security and Integration | - Automate administrative tasks - Integrate with Oracle Cloud services - Implement database security features |
| Topic 4: Administration and Operations | - Monitor database health and performance - Scale compute and storage resources - Manage users and security |
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NEW QUESTION # 87
Which three options do NOT change when enabling auto scaling? (Choose three.)
Answer: A,B,D
Explanation:
When enabling auto scaling in an Oracle Autonomous Database, the feature dynamically adjusts resources based on workload demand, but certain configuration elements remain unchanged unless manually altered.
Amount of storage (A): Auto scaling focuses on adjusting CPU and I/O resources to meet performance needs. The storage allocation remains constant because it is a separately provisioned resource that requires manual adjustment via the OCI Console or API. This ensures that storage capacity is not inadvertently reduced or expanded without explicit user intent.
Parallelism settings (B): These settings determine how many parallel processes are used for query execution. Auto scaling does not modify this pre-defined configuration; it adjusts resource usage (e.g., CPU cores) without altering the parallelism framework established for the database.
Level of concurrency (E): Concurrency refers to the maximum number of simultaneous user sessions. While auto scaling can indirectly influence concurrency by adjusting available resources, the concurrency limit itself is a fixed setting that remains unchanged unless explicitly modified by the administrator.
Incorrect Options:
I/O resources (C): Auto scaling directly impacts I/O resources by increasing or decreasing them alongside CPU adjustments to handle workload fluctuations.
Number of OCPUs displayed in the console (D): While the base OCPU count remains static, the effective number of OCPUs in use (up to three times the base) changes dynamically with auto scaling, and this is reflected in the console, making this option subject to change.
This behavior ensures that auto scaling optimizes performance without disrupting foundational database configurations.
NEW QUESTION # 88
Which two optimizations are different between Autonomous Data Warehouse and Autonomous Transaction Processing? (Choose two.)
Answer: C,D
Explanation:
Autonomous Data Warehouse (ADW) and Autonomous Transaction Processing (ATP) are optimized for different workloads, reflected in their configurations. The two differing optimizations are:
Memory Usage (A): ADW and ATP allocate memory differently to suit their purposes. ADW prioritizes a larger data cache (part of the SGA) to keep more data in memory, boosting analytical query performance (e.g., aggregations over millions of rows). For example, a SELECT SUM(sales) GROUP BY region runs faster with more cached data. ATP, conversely, balances memory across the SGA and PGA for transactional workloads, emphasizing concurrency and quick row-level operations (e.g., UPDATE orders SET status = 'shipped'). This difference ensures ADW excels at scan-heavy analytics, while ATP handles high-throughput updates.
Data Organization (B): ADW uses a columnar storage format (e.g., Hybrid Columnar Compression) optimized for analytics, storing data by column to speed up aggregations and reduce I/O (e.g., scanning only the sales column for a SUM). ATP uses a row-based format suited for OLTP, enabling fast single-row access and updates (e.g., retrieving or modifying a specific order_id). For instance, inserting a row in ATP is efficient due to row storage, while ADW’s columnar format accelerates SELECT AVG(price) FROM products.
The incorrect options are:
Backup Retention (C): Both ADW and ATP use the same automatic backup system (via Oracle’s Automatic Workload Repository), with a default 60-day retention adjustable by users. There’s no optimization difference here; it’s a shared managed feature.
Undo Management (D): Both databases use Oracle’s Flashback technology for undo (e.g., rolling back transactions or querying past states), with retention periods set similarly. Undo is managed automatically in both, not optimized differently.
These optimizations tailor ADW for analytics and ATP for transactions, despite their shared autonomous foundation.
NEW QUESTION # 89
What happens with the standby database when disabling Autonomous Data Guard?
Answer: D
Explanation:
Disabling Autonomous Data Guard affects the standby database:
Correct Answer (A): “The standby database is terminated” is true. When Data Guard is disabled via the OCI Console or API, the standby database is deleted, ending its role as a replica. This is a permanent action requiring re-enablement to recreate a standby.
Incorrect Options:
B: The standby doesn’t become read-write; it’s removed entirely.
C: It doesn’t remain read-only; termination stops all operations.
D: It’s not converted to a refreshable clone; that’s a separate feature.
This ensures resource cleanup when high availability is no longer needed.
NEW QUESTION # 90
Which stage of the indexing pipeline divides text into tokens?
Answer: D
Explanation:
The indexing pipeline in Oracle Text processes text for search:
Correct Answer (D): “Lexer” divides text into tokens (words, symbols) based on language rules and settings (e.g., whitespace, punctuation). It’s the stage responsible for tokenization in Oracle’s text indexing process.
Incorrect Options:
A: Sectioner identifies document sections (e.g., headers), not tokens.
B: Tokenizer is a generic term, but in Oracle Text, “Lexer” is the specific component.
C: Filter preprocesses text (e.g., removing stopwords), post-tokenization.
This step enables efficient text search capabilities.
NEW QUESTION # 91
When working with an Autonomous Exadata Infrastructure supporting Autonomous Databases, where do you go to view the maintenance history of the Exadata?
Answer: B
Explanation:
Viewing the maintenance history of Autonomous Exadata Infrastructure (AEI) requires navigating the OCI console correctly. The correct path is:
Under Solutions and Platforms then Platform Services then Autonomous Exadata (C): In the OCI console, AEI is categorized under “Solutions and Platforms” (a section for integrated services), then “Platform Services” (covering cloud platform offerings), and finally “Autonomous Exadata.” Here, you select your AEI instance (e.g., by name or OCID), and the details page displays a “Maintenance History” section listing past events (e.g., patching dates, durations, and statuses like “Completed on 2025-03-01”). For example, a quarterly RU applied on January 15 might show “Patch Applied: RU 23.1” with start/end times. This path reflects AEI’s role as a dedicated platform supporting Autonomous Container Databases (ACDs) and Autonomous Databases (ADBs).
The incorrect options are:
Under Core Infrastructure then Compute then Autonomous Exadata (A): “Core Infrastructure” > “Compute” is for virtual machines or bare metal hosts, not Exadata infrastructure. AEI isn’t a compute instance; it’s a database platform.
Under Database then Autonomous Transaction Processing then Autonomous Exadata (B): “Database” > “Autonomous Transaction Processing” focuses on ATP instances, not the underlying Exadata infrastructure. AEI maintenance is separate from specific ADB types.
Under Core Infrastructure then Autonomous Exadata (D): “Core Infrastructure” doesn’t directly list AEI; it’s too broad and lacks the “Platform Services” context needed for Exadata-specific management.
This navigation ensures you access AEI-specific maintenance details efficiently.
NEW QUESTION # 92
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