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| Section | Weight | Objectives |
|---|---|---|
| Security, Integration and Data Management | 20% | - Configure identity and access management - Use Autonomous Database tools for development and administration - Load, migrate and integrate data from various sources - Implement data encryption and security controls |
| AI Features and Capabilities | 30% | - Apply AI-assisted performance tuning and optimization - Use Oracle Machine Learning within Autonomous Database - Implement Select AI for natural language to SQL generation - Configure and use AI Vector Search |
| Autonomous Database Architecture and Provisioning | 25% | - Provision and configure Autonomous Database on dedicated infrastructure - Provision and configure Autonomous Database on shared infrastructure - Integrate Autonomous Database with Oracle Cloud Infrastructure services - Describe Autonomous Database architecture and core components |
| Database Management and Operations | 25% | - Perform patching, upgrades and lifecycle management - Implement backup, recovery and high availability strategies - Monitor and manage database performance and resources - Configure auto-scaling and storage management |
Es gibt viele Methoden, die Oracle 1Z0-931-26 Zertifizierungsprüfung zu bestehen. Einerseits kann man viel Zeit und Energie auf die Oracle 1Z0-931-26 Zertifizierungsprüfung aufwenden, um die Fachkenntnisse zu konsolidieren. Andererseits kann man mit weniger Zeit und Geld die zielgerichteten Oracle 1Z0-931-26 Prüfungsfragen von EchteFrage benutzen.
71. Frage
Which two statements are true regarding active transactions when scaling OCPUs in an Autonomous Database? (Choose two.)
Antwort: A,D
Begründung:
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.
72. Frage
When working with an Autonomous Exadata Infrastructure supporting Autonomous Databases, where do you go to view the maintenance history of the Exadata?
Antwort: C
Begründung:
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.
73. Frage
Which three advanced options can you enable or disable while creating a business model? (Choose three.)
Antwort: A,B,E
Begründung:
When creating a business model in Autonomous Database (e.g., via Data Modeler), advanced options enhance analytical capabilities:
Correct Answer (A): Autonomous Aggregate Cache uses dimensional metadata to manage caching, improving query performance.
Correct Answer (B): Analytic View Transparency View exposes analytic views as standard views for compatibility with existing tools.
Correct Answer (C): Analytic View Base Table Query Transformation enables seamless integration of analytic views into existing reports.
Incorrect Options:
D: “Display private tables” is not a recognized advanced option in this context.
E: “Business Object Security Feature” is unrelated to Autonomous Database business modeling.
These options optimize analytical workloads.
74. Frage
Which four file formats are supported when loading data from Cloud Storage?
Antwort: A,C,E,F
Begründung:
Full Detailed In-Depth Explanation:
Supported formats:
A: False. DDL is a language, not a data format.
B: True. AVRO is supported for structured data.
C: True. Parquet is optimized for analytics.
D: False. DOC is not supported.
E: True. JSON is flexible for semi-structured data.
F: True. CSV is widely used for tabular data.
75. Frage
Which statement is FALSE about Data Insights?
Antwort: B
Begründung:
Data Insights is a feature in Autonomous Database that helps users understand their data. The false statement is:
Data Insights are automatically generated by various analytic functions built into the database (C): This is incorrect. Data Insights are not solely the result of automatic execution of built-in analytic functions (e.g., AVG, SUM, or RANK). Instead, they are generated through a combination of user-initiated analysis and Oracle’s machine learning-driven capabilities within the Data Insights dashboard (part of Database Actions or OCI console). Users select datasets or tables, and the system applies algorithms to identify patterns (e.g., trends in sales) or anomalies (e.g., outlier transactions), but this process isn’t just a passive outcome of pre-existing database functions—it’s an active, curated feature requiring configuration. For example, a user might explore a SALES table, and Data Insights highlights a spike in Q4 sales, but this requires user input to define scope, not just automatic function output.
The true statements are:
Data Insights display information about patterns and anomalies in the data of entities in your Oracle Autonomous Database (A): True. The feature visualizes trends (e.g., seasonal sales increases) and outliers (e.g., unexpected data drops) in tables or views, helping users spot significant data behaviors. For instance, it might show a bar chart of monthly revenue with an anomaly flagged for a sudden dip.
Data Insights provides a wide range of graphical data presentation capabilities (B): True. It offers visualizations like bar charts, line graphs, and scatter plots, customizable to represent data insights effectively. E.g., a line graph might track customer sign-ups over time, with options to adjust axes or filters.
The results of the Insight analysis appear as a series of bar charts in the Data Insights dashboard (D): True, partially. While bar charts are a common default (e.g., comparing sales by region), the dashboard supports multiple chart types, but the statement’s focus on bar charts aligns with typical output for simple insights.
The misconception in C overlooks the interactive, ML-assisted nature of Data Insights, distinguishing it from passive function-based analytics.
76. Frage
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