Oracle Database provides several techniques to optimize data access from Object Storage, particularly in the context of Autonomous Database, leveraging external tables and advanced storage formats. The question asks for four correct methods, and based on Oracle documentation, the following are the most applicable:

Correct Answer (A): Scan avoidance using partitioned external tables

Partitioned external tables allow Oracle Database to skip irrelevant partitions when querying data stored in Object Storage. By organizing data into partitions (e.g., by date or region), the database engine can prune partitions that don't match the query predicates, significantly reducing the amount of data scanned and improving performance. This is a well-documented optimization for external data access in Oracle Database and Autonomous Database environments.

Correct Answer (D): Scan avoidance using columnar pruning for columnar stores like parquet and orc

Columnar pruning is a technique where only the required columns are read from columnar file formats such as Parquet or ORC stored in Object Storage. These formats store data column-wise, enabling the database to avoid scanning entire rows or irrelevant columns, which is particularly efficient for analytical queries common in Autonomous Data Warehouse (ADW). This is a standard optimization supported by Oracle's external table framework when accessing Object Storage.

Correct Answer (E): Optimized data archive using hybrid partitioned tables

Hybrid partitioned tables combine local database partitions with external partitions stored in Object Storage. This allows older, less frequently accessed data to be archived efficiently in the cloud while remaining queryable alongside active data in the database. The database optimizes access by seamlessly integrating these partitions, reducing costs and improving archival efficiency. This feature is explicitly supported in Oracle Database and enhanced in Autonomous Database for data lifecycle management.

Correct Answer (F): Optimized data archive using partitioned external tables

Similar to hybrid partitioned tables, using partitioned external tables alone optimizes data archiving by storing historical data in Object Storage with partitioning (e.g., by year). This method enables efficient querying of archived data by pruning unneeded partitions, offering a cost-effective and scalable archival solution. It's a distinct approach from hybrid tables, focusing solely on external storage, and is widely used in Oracle environments.

Incorrect Options:

B . Scan avoidance using columnar pruning for .csv files

CSV files are row-based, not columnar, and lack the internal structure of formats like Parquet or ORC. While Oracle can read CSVs from Object Storage via external tables, columnar pruning is not applicable because CSVs don't support column-wise storage or metadata for pruning. This makes this option incorrect as a specific optimization technique, though basic predicate pushdown might still reduce scanning to some extent.

C . Scan avoidance using block skipping when reading parquet and orc files

Block skipping (or row group skipping) is a feature in some database systems where metadata in Parquet or ORC files allows skipping entire blocks of data based on query filters. While Oracle supports Parquet and ORC through external tables and can leverage their columnar nature (via pruning), ''block skipping'' is not explicitly highlighted as a primary optimization in Oracle's documentation for Autonomous Database. It's more commonly associated with systems like Apache Spark or Hive. Oracle's focus is on columnar pruning and partitioning, making this option less accurate in this context.

Why Four Answers?

The question specifies ''four ways,'' and while six options are provided, A, D, E, and F are the most directly supported and documented methods in Oracle Autonomous Database for optimizing Object Storage access. Options B and C, while conceptually related to data access optimizations, are either inapplicable (CSV lacks columnar structure) or not explicitly emphasized (block skipping) in Oracle's feature set for this purpose.

This selection aligns with Oracle's focus on partitioning and columnar formats for efficient cloud data access, ensuring both performance and archival optimization.

External Tables and Object Storage

Hybrid Partitioned Tables

Autonomous Database Data Loading

Full Detailed In-Depth Explanation:

The Service Console is a database-specific management interface:

A: False. Auto-scaling is managed via the OCI Console, not the Service Console.

B: True. The Service Console allows monitoring and terminating runaway SQL statements that consume excessive resources.

C: False. Manual backups are created through OCI Console or SQL commands, not the Service Console.

D: False. Moving compartments is an OCI Console function, not a Service Console task.

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.

Full Detailed In-Depth Explanation:

Autonomous JSON Database supports:

A: True. SODA provides NoSQL-style access for JSON documents.

B: False. SQL is secondary; SODA/REST are primary for JSON data.

C: True. REST APIs are supported but require enablement.

D: True. The OCI console offers the JSON Online Developer tool.

E: False. No such downloadable tool exists for Autonomous JSON.

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.