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

The scenario requires a low-maintenance, high-performance database for an OLTP web application with occasional reporting. The correct answer is:

ATP using 'tpurgent' and 'high' TNS services to separate connection types (A):Autonomous Transaction Processing (ATP) is ideal here. It’s a fully managed database optimized for OLTP workloads (e.g., scheduling service requests) with zero maintenance overhead—Oracle handles patching, backups, and tuning. ATP supports multiple connection services:

'tpurgent':Prioritizes low-latency, time-critical transactions (e.g., customer scheduling requests), ensuring fast response times for the web app.

'high':Suited for high-concurrency or reporting queries, allowing operational reports to run during non-peak hours without impacting the OLTP workload.By using these predefined services, the architecture separates transactional and reporting workloads within the same database, meeting performance needs without additional administration. For example, the web app connects via tpurgent for real-time updates, while a reporting tool uses high for batch queries at night, leveraging ATP’s auto-scaling if needed.

The incorrect options are:

Since the application needs to be highly available, it should be deployed on a Kubernetes Cluster (B):This misinterprets the question—it’s about the database service,not the application deployment. Kubernetes is for container orchestration, not a database solution, and adds complexity counter to the low-maintenance goal.

It is best to build a separate data warehouse, and move the OLTP data on a nightly basis (C):While a separate Autonomous Data Warehouse (ADW) could handle reporting, it requires data movement (e.g., via ETL), increasing admin effort and complexity, which the DBA Manager wants to avoid. ATP can handle both workloads with proper service separation.

ADW since operational reporting is a higher priority in this scenario (D):ADW is optimized for analytics, not OLTP. The web app’s transactional needs are primary, with reporting secondary and occasional, making ATP more suitable.

ATP’s self-managing nature and service flexibility make it the best fit.

Full Detailed In-Depth Explanation:

Oracle Data Safe enhances database security across various deployments. Let’s evaluate:

A:True. Data Safe assesses cloud database configurations for vulnerabilities.

B:True. It evaluates user authentication, types, and password policies.

C:False. Data Safe supports Autonomous Databases, Exadata Cloud Service, OCI VMs, and on-premises Oracle databases, not just Autonomous Databases.

D:True. It scans data and metadata to identify sensitive information.

Option C is the false statement, as Data Safe’s scope extends beyond Autonomous Databases.

Oracle Spatial Studio is a self-service tool for working with spatial data in Autonomous Database. The three correct functions are:

Map visualization (A):Spatial Studio provides robust capabilities to visualize spatial data on interactive maps, enabling users to explore geographic patterns and relationships visually.

Spatial analysis (C):It offers tools for performing spatial operations like proximityanalysis, spatial joins, and buffering, which are essential for deriving insights from geographic data.

Spatial data editing (E):Users can edit spatial data, such as modifying geometries or updating attributes, directly within Spatial Studio, making it a powerful tool for data management.

The incorrect options are:

Custom SQL queries (B):While Spatial Studio supports spatial operations, it is primarily a graphical tool and does not focus on executing custom SQL queries. Such functionality is more aligned with tools like SQL Developer.

Geocoding (D):Geocoding (converting addresses to coordinates) is not a core feature of Spatial Studio. It focuses on visualization, analysis, and editing rather than address-to-coordinate conversion, which is typically handled by separate Oracle services or tools.

These functions align with Spatial Studio’s purpose of simplifying spatial data management and analysis.