1Z0-931-25 VALID EXAM SIMS | 1Z0-931-25 PASS4SURE EXAM PREP

1Z0-931-25 Valid Exam Sims | 1Z0-931-25 Pass4sure Exam Prep

1Z0-931-25 Valid Exam Sims | 1Z0-931-25 Pass4sure Exam Prep

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Oracle 1Z0-931-25 Pass4sure Exam Prep - 1Z0-931-25 Exam Introduction

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Oracle 1Z0-931-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Data Lake Analytics with Autonomous Database: This section of the exam measures the skills of Big Data Engineers and explores how Autonomous Database can be used for analytics in data lake environments. It includes data ingestion, query optimization, and leveraging cloud-native analytics services, ensuring engineers can efficiently process and analyze large volumes of structured and unstructured data.
Topic 2
  • Developing on Autonomous Database: This section of the exam measures the skills of Application Developers and focuses on developing and extending applications using Autonomous Database. It covers using generative AI for natural language queries, Autonomous JSON Database, Oracle Text for document search, location-based analysis with Autonomous Spatial, Autonomous Graph for data relationships, and integration with Object Storage, enabling developers to build intelligent, scalable applications.
Topic 3
  • Autonomous Database Tools: This section of the exam measures the skills of Data Analysts and covers the tools available within Autonomous Databases for advanced data processing and analytics. It includes Oracle Machine Learning, APEX, and SQL Developer Web for database development, as well as data transformation, business model creation, data insights, and data analysis, allowing analysts to extract valuable insights from large datasets.
Topic 4
  • Getting Started with Autonomous Database: This section of the exam measures the skills of Database Administrators and covers the architecture and key features of Oracle Autonomous Database. It explains how the database integrates within the Oracle ecosystem and provides an overview of different Autonomous Database offerings and their licensing models, helping administrators understand how to deploy and manage these cloud-based databases efficiently.
Topic 5
  • Autonomous Database Dedicated: This section of the exam measures the skills of IT Architects and explores the workflows and functionality of Autonomous Database Dedicated and Autonomous Database Cloud@Customer. It includes provisioning dedicated resources, setting up OCI policies, monitoring infrastructure, scheduling maintenance tasks such as patching, and managing encryption keys for enhanced security. IT Architects will learn how to integrate dedicated database environments within their cloud strategy.
Topic 6
  • Managing and Maintaining Autonomous Database: This section of the exam measures the skills of Database Administrators and focuses on the ongoing management and maintenance of Autonomous Database instances. It includes using REST APIs and OCI CLI for automation, configuring access control lists and private endpoints, monitoring performance, setting up notifications, utilizing features like auto-indexing and data safe, handling connectivity through wallets and service handles, and configuring disaster recovery using Data Guard to ensure business continuity.
Topic 7
  • Autonomous Database Shared: This section of the exam measures the skills of Cloud Engineers and focuses on creating and managing shared Autonomous Database instances. It includes provisioning, scaling, and starting or stopping instances, as well as database consolidation with Elastic Resource Pools. It also covers user management, cloning, database migration, monitoring, backup and restore processes, and introduces Data Guard for high availability, ensuring cloud engineers can maintain optimal database performance.

Oracle Autonomous Database Cloud 2025 Professional Sample Questions (Q25-Q30):

NEW QUESTION # 25
Which two statements are correct about Autonomous Data Warehouse on Shared Exadata Infrastructure?

  • A. Oracle Database Result Cache is enabled by default for all SQL statements.
  • B. You have direct access to the database node.
  • C. Parallelism is not enabled by default.
  • D. Compression is enabled by default. Autonomous Data Warehouse uses Hybrid Columnar Compression for all tables by default.

Answer: A,D

Explanation:
Let's evaluate each statement about Autonomous Data Warehouse (ADW) on Shared Exadata Infrastructure:
A . Oracle Database Result Cache is enabled by default for all SQL statements: True. ADW enables the Result Cache by default to improve query performance by storing frequently accessed results in memory.
B . You have direct access to the database node: False. ADW is a fully managed service; users do not have direct access to underlying nodes, which Oracle manages for patching, scaling, and security.
C . Parallelism is not enabled by default: False. Parallelism is enabled by default in ADW to optimize large analytical queries across multiple CPUs.
D . Compression is enabled by default. Autonomous Data Warehouse uses Hybrid Columnar Compression for all tables by default: True. Hybrid Columnar Compression (HCC) is applied automatically to reduce storage and enhance query performance.
Thus, A and D are correct, reflecting ADW's default performance optimizations.


NEW QUESTION # 26
Which three advanced options can you enable or disable while creating a business model? (Choose three.)

  • A. Analytic View Base Table Query Transformation
  • B. Display private tables
  • C. Autonomous Aggregate Cache
  • D. Business Object Security Feature
  • E. Analytic View Transparency View

Answer: A,C,E

Explanation:
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.


NEW QUESTION # 27
Which tab in the Data Load Explore tool shows the size of the table and the number of rows and columns?

  • A. Statistics tab
  • B. Hierarchies tab
  • C. Measures tab
  • D. Data Sources tab

Answer: A

Explanation:
The Data Load Explore tool in Autonomous Database helps users analyze loaded data. The correct tab is:
Statistics tab (B): The Statistics tab displays detailed metadata about a table, including its size (e.g., in MB or GB), number of rows, and number of columns. For example, after loading a CSV into a table SALES, this tab might show "Rows: 10,000, Columns: 5, Size: 2.3 MB," providing a quick overview of the dataset's scale and structure. It's designed for understanding data volume and composition, aiding decisions like partitioning or indexing.
The incorrect options are:
Hierarchies tab (A): This tab shows hierarchical relationships in the data (e.g., parent-child links), not basic table metrics like size or row count. It's more about data organization than statistics.
Data Sources tab (C): This tab lists the sources of loaded data (e.g., OCI Object Storage buckets or files), not the resulting table's properties. It focuses on origin, not metrics.
Measures tab (D): The Measures tab defines numerical aggregates (e.g., sum of sales), used for analytics, not raw table statistics like row or column counts.
The Statistics tab is the go-to for table sizing details in Data Load Explore.


NEW QUESTION # 28
Your company has a .NET application and wants to deploy it on Autonomous Database (ADB). What software is used to connect to ADB from the .NET application?

  • A. SQL*Plus
  • B. Oracle Data Access Components for Windows
  • C. You cannot use .NET with ADB
  • D. Java

Answer: B

Explanation:
Connecting a .NET application to Autonomous Database (ADB) requires a specific client library compatible with Oracle's database connectivity standards. The correct software is:
Oracle Data Access Components for Windows (D): Oracle Data Access Components (ODAC) for Windows is the recommended software for .NET applications to connect to ADB. ODAC includes the Oracle Data Provider for .NET (ODP.NET), which supports ADO.NET interfaces for database access. It enables .NET developers to use familiar APIs (e.g., OracleConnection, OracleCommand) to interact with ADB over Oracle Net Services, leveraging the secure TLS connection required by ADB (via the client wallet). For example, a .NET app might use ODAC to execute SELECT * FROM customers against an ADB instance, authenticating with a wallet downloaded from the OCI console. ODAC supports both managed and unmanaged modes: the managed ODP.NET is lightweight and assembly-based, while the unmanaged version integrates with Oracle Client libraries. To set it up, developers install ODAC (e.g., via NuGet or Oracle's download site), configure the wallet (e.g., tnsnames.ora), and write code like:
csharp
CollapseWrapCopy
using Oracle.ManagedDataAccess.Client;
string connString = "User Id=USER1;Password=pwd;Data Source=adb_high";
using (OracleConnection conn = new OracleConnection(connString)) {
conn.Open();
// Query execution here
}
This ensures seamless integration with ADB's managed environment, supporting features like connection pooling and high performance.
The incorrect options are:
SQL*Plus (A): SQL*Plus is a command-line tool for SQL execution and administration, not a programmatic library for .NET applications. It's unsuitable for embedding in a .NET app, as it lacks API integration and is meant for manual use (e.g., running scripts like SELECT * FROM table;).
You cannot use .NET with ADB (B): This is false. .NET is fully supported via ODAC, allowing applications (e.g., ASP.NET web apps or Windows services) to connect to ADB just like any Oracle database, provided the wallet and credentials are configured.
Java (C): Java uses JDBC (e.g., Oracle JDBC Driver) for database connectivity, not .NET. While JDBC works with ADB for Java apps, it's irrelevant for a .NET environment, where ODAC is the standard.
ODAC's robust support for .NET makes it the definitive choice, bridging Microsoft's ecosystem with Oracle's cloud database.


NEW QUESTION # 29
What are three characteristics of Data Lake data captured in Object Storage? (Choose three.)

  • A. High transaction performance
  • B. High concurrency
  • C. Mixed data types
  • D. Multiple subject areas
  • E. Schema on read

Answer: C,D,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 # 30
......

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