Oracle 1z0-931-25 Exam With Confidence Using Practice Dumps

Full Detailed In-Depth Explanation:

Bring Your Own License (BYOL) allows using existing on-premises Oracle licenses in the cloud:

A:False. Exadata is included in Autonomous Database; no separate license is needed.

B:False. Active Data Guard is an optional feature, not required for BYOL.

C:True. RAC is needed for BYOL when scaling beyond 16 OCPUs, as Autonomous Database uses RAC for high availability in larger deployments.

D:True. Multitenant is mandatory, as Autonomous Database uses a multitenant architecture (CDB/PDB).

E:False. TDE is included by default, not a BYOL requirement.

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.

Scaling OCPUs in Autonomous Database is designed to be seamless. The two true statements are:

Scaling can happen while there are active transactions in the database (B):ADB supports online scaling, meaning you can increase or decrease OCPUs (e.g., from 2 to 4) via the OCI console or CLI (e.g., oci db autonomous-database update --cpu-core-count 4) without stopping the database. Active transactions (e.g., INSERT INTO orders VALUES (...)) continue running during this process. Oracle’s architecture ensures the database remains available, adjusting resources in the background. For example, a web app processing orders won’t notice the scaling operation starting at 10:00 AM.

Active transactions continue running unaffected (C):During scaling, existing transactions are not interrupted, terminated, or paused. They complete normally, with Oracle managing resource allocation transparently (e.g., shifting CPU usage without killing sessions). For instance, a long-running UPDATE statement started before scaling finishes successfully, leveraging the database’s high-availability design. The status shows “SCALING IN PROGRESS,” but users experience no downtime.

The incorrect options are:

Active transactions are terminated and rolled back (A):False. Scaling is non-disruptive; transactions aren’t killed or rolled back, preserving data integrity and user experience. Termination only occurs during explicit stops or failures, not scaling.

Active transactions are paused (D):False. There’s no pausing mechanism during scaling; transactions run continuously, as pausing would disrupt OLTP or analytical workloads, countering ADB’s autonomous promise.

This online scaling capability is a key benefit, ensuring uninterrupted service.