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| Section | Objectives |
|---|---|
| Topic 1: Administration and Operations | - Scale compute and storage resources - Manage users and security - Monitor database health and performance |
| Topic 2: Provisioning and Deployment | - Select workload type and deployment options - Create and provision Autonomous AI Database - Configure networking and connectivity |
| Topic 3: Performance, Backup and Recovery | - Backup and restore operations - High availability and disaster recovery - Performance tuning |
| Topic 4: Security and Integration | - Automate administrative tasks - Implement database security features - Integrate with Oracle Cloud services |
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NEW QUESTION # 106
What are three characteristics of Data Lake data captured in Object Storage? (Choose three.)
Answer: B,C,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 # 107
Which three are use cases for Graph Studio? (Choose three.)
Answer: A,B,C
Explanation:
Graph Studio in Autonomous Database supports graph-based analysis:
Correct Answer (C): Pattern matching identifies relationships (e.g., fraud rings) using graph queries like PGQL.
Correct Answer (D): Clustering groups related nodes (e.g., communities) using graph algorithms.
Correct Answer (B): Churn analysis leverages graph relationships to predict customer loss (e.g., via influence networks), though less emphasized than C and D, it’s a valid use case.
Incorrect Options:
A: Facial recognition is image-based, not graph-based.
E: 3-D modelling is a misinterpretation; it’s not a primary graph use case.
These align with Graph Studio’s strengths in relationship analysis.
NEW QUESTION # 108
Which management operation is correct about Autonomous Databases on Shared Exadata Infrastructure?
Answer: C
Explanation:
Management operations for Autonomous Databases on Shared Exadata Infrastructure are limited due to its fully managed nature. The correct statement is:
You cannot configure the scheduling for your Autonomous Databases on Shared Exadata Infrastructure (C): In shared infrastructure, Oracle fully controls maintenance scheduling (e.g., patching, upgrades). Unlike dedicated infrastructure, where users can set maintenance windows, shared ADB users cannot adjust timing. Oracle notifies users of upcoming maintenance (e.g., via email or console), typically in a 7-day window, but the exact schedule is Oracle-driven to optimize the shared Exadata platform. For example, a quarterly patch might occur on a Tuesday at 2 AM UTC, and users must adapt, not reschedule.
The incorrect options are:
You can skip a scheduled maintenance run... (A): False. Shared infrastructure does not allow skipping maintenance runs, even for two quarters. This flexibility exists only in dedicated infrastructure, where users have more control (e.g., skipping up to two consecutive updates). In shared mode, Oracle enforces updates for security and stability across all tenants.
You can perform a "rolling restart"... (B): False. Rolling restarts (restarting nodes sequentially for availability) are not user-initiated in ADB shared infrastructure. Restarts, if needed, are managed by Oracle during maintenance, and users cannot control the process or ensure node-by-node availability.
You can choose to use Release Update or Release Update Revision updates... (D): False. In shared infrastructure, Oracle applies Release Updates (RUs) uniformly across all databases; users cannot choose between RU or Release Update Revisions (RURs), a feature reserved for dedicated deployments.
This reflects the trade-off of shared infrastructure: lower cost and management effort for less control.
NEW QUESTION # 109
When you are increasing the number of OCPUs in your Autonomous Database, what does its status show?
Answer: B
Explanation:
Scaling OCPUs in an Autonomous Database triggers a specific status update. The correct answer is:
SCALING IN PROGRESS (D): When you increase (or decrease) the number of OCPUs, the database status in the OCI console changes to “SCALING IN PROGRESS.” This indicates that the system is actively adjusting the compute resources, a process that typically completes in a few minutes with no downtime for active transactions.
The incorrect options are:
UPSCALE IN PROGRESS (A): “Upscale” is not an official status term used by Oracle for this operation.
RESIZING IN PROGRESS (B): While “resizing” might intuitively fit, Oracle specifically uses “SCALING IN PROGRESS” for CPU adjustments.
UPLIFT IN PROGRESS (C): “Uplift” is not a recognized status in the context of Autonomous Database scaling.
This status reflects Oracle’s terminology for dynamic scaling.
NEW QUESTION # 110
Which statement is FALSE about setting up compartment quotas?
Answer: A
Explanation:
Compartment quotas in Oracle Cloud Infrastructure (OCI) manage resource allocation:
Correct Answer (D): “Compartment quotas cannot be set on the root compartment” is false. Quotas can be applied to any compartment, including the root compartment, to limit resource usage tenancy-wide. While direct quotas on the root are less common (child compartment quotas are typically used), it’s technically possible via OCI policies.
True Statements:
A: Quotas offer flexible resource limits (e.g., OCPUs, storage) via policy statements.
B: Parent compartment quotas take precedence over child quotas, enforcing hierarchical control.
C: Managing quotas requires appropriate IAM permissions (e.g., manage quotas), ensuring security.
This flexibility supports granular resource management across the tenancy.
NEW QUESTION # 111
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