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Oracle Autonomous Database Cloud 2025 Professional Sample Questions (Q108-Q113):

NEW QUESTION # 108
Which set of options can be specified when defining the preferred maintenance schedule of the Exadata Infrastructure for Autonomous Database Dedicated?

A. Month of the Year, Week of the Month, Day of the Week, Start Hour
B. Quarter of the Year, Month of the Quarter, Date of the Month, Start Hour
C. Week of the Quarter, Day of the Week, Start Hour
D. Month of the Quarter, Week of the Month, Day of the Week, Start Hour

Answer: D

Explanation:
Defining a maintenance schedule for Dedicated Exadata Infrastructure allows customization:
Correct Answer (B): "Month of the Quarter, Week of the Month, Day of the Week, Start Hour" is the correct set. For example, you can specify "Month 2 of Q1, Week 3, Wednesday, 02:00 AM," aligning with quarterly planning while offering weekly and daily precision.
Incorrect Options:
A: "Quarter of the Year" and "Date of the Month" are less flexible than week-based options and not the standard format.
C: "Month of the Year" skips the quarterly structure used in dedicated maintenance.
D: Omits "Month of the Quarter," reducing granularity.
This flexibility minimizes disruption for dedicated deployments.

NEW QUESTION # 109
You have been tasked with analyzing a year's worth of sales data stored in an Autonomous Data Warehouse instance. Which tool would you use to automate data exploration and create pivot tables?

A. Business Models
B. SQL Developer Web
C. Data Load
D. APEX

Answer: A

Explanation:
Full Detailed In-Depth Explanation:
A: False. APEX is for app development, not data exploration.
B: False. SQL Developer Web is for SQL queries, not pivot tables.
C: True. Business Models in ADW automates data exploration and generates pivot tables.
D: False. Data Load is for importing data, not analysis.

NEW QUESTION # 110
Which predefined service connection should you use when running lots of high concurrent queries in an Autonomous Database?

A. DBNAME_MEDIUM
B. DBNAME_CONCURRENT
C. DBNAME_LOW
D. DBNAME_HIGH

Answer: C

Explanation:
Full Detailed In-Depth Explanation:
Service connections in Autonomous Database:
A . DBNAME_LOW: Optimized for high concurrency with minimal resources per query, ideal for many simultaneous queries.
B . DBNAME_MEDIUM: Balanced concurrency and performance.
C . DBNAME_HIGH: Prioritizes individual query performance, not concurrency.
D . DBNAME_CONCURRENT: Not a valid service name.

NEW QUESTION # 111
Which statement is true about OCPUs and storage when you choose to scale your Autonomous Database?

A. OCPUs and storage must remain in sync
B. Use auto scaling to increase storage
C. Increasing OCPUs will automatically increase storage
D. OCPUs and storage can be scaled independently

Answer: D

Explanation:
Scaling resources in Autonomous Database offers flexibility. The true statement is:
OCPUs and storage can be scaled independently (A): In Autonomous Database, you can adjust the number of OCPUs (compute resources) and storage capacity (in terabytes) separately via the OCI console or CLI. For example, you might provision an ADB with 2 OCPUs and 1 TB of storage. Later, you could scale to 4 OCPUs without changing storage, or increase storage to 2 TB without touching OCPUs. This decoupling allows tailored resource allocation-e.g., more compute for a CPU-intensive workload or more storage for growing data-without over-provisioning. Scaling is online, with no downtime, and status shows "SCALING IN PROGRESS" during the operation.
The incorrect options are:
Use auto scaling to increase storage (B): Auto scaling applies to OCPUs only, allowing up to 3x the base CPU allocation dynamically. Storage scaling is manual; you specify a new size (e.g., via oci db autonomous-database update), not via auto scaling.
Increasing OCPUs will automatically increase storage (C): There's no automatic linkage between OCPU and storage scaling. They are independent parameters, and increasing one does not affect the other unless explicitly requested.
OCPUs and storage must remain in sync (D): No such synchronization is required. You can have 1 OCPU with 10 TB or 10 OCPUs with 1 TB, depending on workload needs.
This independence is a hallmark of Autonomous Database's elasticity.

NEW QUESTION # 112
When integrating object storage with Autonomous Database, how effectively does Oracle use the files in the object storage?

A. Scan hybrid partition tables
B. Prune columns in parquet files
C. Scan partition tables
D. Prune columns in CSV files

Answer: C

Explanation:
When Autonomous Database integrates with object storage (e.g., Oracle Cloud Infrastructure Object Storage), it optimizes data access. The correct answer is:
Scan partition tables (B): Oracle leverages partition pruning when scanning tables stored in object storage. This means it only scans the relevant partitions based on query predicates, significantly reducing I/O and improving performance. This is particularly effective for partitioned tables stored in formats like Parquet or ORC, which support partitioning.
The incorrect options are:
Prune columns in parquet files (A): While column pruning is possible with columnar formats like Parquet, the primary optimization highlighted in Oracle's documentation for object storage integration is partition pruning, not column pruning alone.
Scan hybrid partition tables (C): "Hybrid partition tables" is not a standard term in this context. Oracle supports external partitioned tables, but the focus is on scanning partitioned tables, not a hybrid-specific optimization.
Prune columns in CSV files (D): CSV files are row-based and do not natively support column pruning, making this less effective compared to partition scanning.
Partition pruning is a key optimization for external tables in object storage, enhancing query efficiency.

NEW QUESTION # 113
......

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