1z0-1195-25 Oracle Data Platform 2025 Foundations Associate Free Practice Exam Questions (2025 Updated)

The "MySQL Database Service" on Oracle Cloud Infrastructure uses "Block Volume Storage" (C) for all data storage. Block Volume provides high-performance, persistent block storage that is directly attached to compute instances running the MySQL database, ensuring low-latency access for transactional and operational workloads. "Archive Storage" (A) is for long-term, infrequently accessed data, "Object Storage" (B) is for unstructured data, and "File Storage" (D) is for shared file systems—none of which suit the primary storage needs of a MySQL database. Oracle’s MySQL Database Service documentation confirms Block Volume as the default storage platform.

When provisioning an "Autonomous Database" on Oracle Cloud Infrastructure, the supported workload types include "JSON" (B) for document-centric applications, "Data Warehouse" (C) for analytics, and "APEX" (D) for low-code application development, alongside Transaction Processing (not listed here). "MySQL" (A) is not a workload type for Autonomous Database; instead, it is a separate database service (Oracle MySQL Database Service) on OCI, not part of the Autonomous Database offering. Oracle’s documentation specifies the distinct workload options available during Autonomous Database provisioning, excluding MySQL.

From the OCI Console for an "Oracle Autonomous Database," you can "Scale up/down CPU" (A) to adjust compute resources dynamically and "Increase Storage allocated for Database" (D) to expand capacity, both with no downtime. "Increase Network Bandwidth" (B) is not a console-adjustable parameter (managed by Oracle), and "Scale up/down Memory" (C) is not independentlyconfigurable (tied to CPU scaling). Oracle’s documentation confirms CPU and storage scaling as user-controlled actions.

A prerequisite for running "APEX" (Application Express) on an Autonomous Database is provisioning "an Autonomous Database with the workload type Data Warehouse, TransactionProcessing, JSON, or APEX" (B). APEX is supported across these workload types, with ORDS (Oracle REST Data Services) pre-enabled. Option A is incorrect, as ORDS is automatically configured. Option C is too restrictive, as APEX isn’t limited to the APEX workload type alone. Option D is unnecessary, as user creation follows provisioning, not precedes it. Oracle’s Autonomous Database documentation confirms APEX availability across these workload types with no manual ORDS setup required.

"Oracle Autonomous Database" accelerates innovation with: "Scaling of CPUs with very little downtime" (A), enabling dynamic performance adjustments online; "Built-in AI and ML helps find patterns that could identify undiscovered anomalies" (B), leveraging in-database machine learning; and "Instant scaling of storage" (C), providing flexibility without interruption. "Provisions a data warehouse in seconds" (D) is true but more a setup feature than an ongoing innovation driver. Oracle’s documentation emphasizes these capabilities for rapid development and insights.

The two main benefits of "Oracle APEX" (Application Express) include "Develop responsive web apps" (D), as it enables rapid creation of scalable, mobile-friendly applications using a low-code platform. The second benefit, though not perfectly listed, aligns with faster development (implied but misstated in A). "Faster development time using hand coding" (A) is incorrect, as APEX avoids hand coding with its declarative approach. "Less productivity compared to hand coding" (B) is false, as APEX increases productivity. "Store data in PL/SQL objects" (C) is a feature, not a primary benefit. Oracle’s APEX documentation emphasizes responsive web app development and rapid delivery as key advantages.

For serverless Spark processing in Oracle’s Lakehouse architecture, "OCI Data Flow" (C) is the designated service. OCI Data Flow is a fully managed, serverless platform that enables users to run Apache Spark applications without managing infrastructure, making it ideal for big data processing tasks like ETL, machine learning, and analytics. "Oracle Analytics Cloud" (A) is an analytics and visualization tool, not a Spark processing engine. "OCI Object Storage" (B) provides scalable storage but lacks processing capabilities, and "OCI Data Catalog" (D) is for metadata management, not Spark job execution. Oracle’s OCI documentation confirms OCI Data Flow as the primary service for serverless Spark workloads in the Lakehouse.

For an "Autonomous Database," the DBA can "Open the database in OCI Console and increase the number of OCPU. No downtime required" (C). Autonomous Database supports dynamic scaling of OCPUs online, with no service interruption, leveraging its self-managing capabilities. Options A and B involve unnecessary downtime, and D adds an unneeded user restriction. Oracle’s documentation confirms zero-downtime OCPU scaling as a key feature.

For the "MySQL Database Service," the "Start" command (A) resumes OCPU billing, as it activates the database instance, incurring compute costs. "Stop" (B) halts billing by suspending the instance, "Restart" (C) is a stop-then-start sequence, and "Backup" (D) affects storage, not OCPU billing. Oracle’s MySQL Service documentation ties billing to the Start command.

The "Autonomous JSON Database" supports NoSQL-style document access through "SODA" (Simple Oracle Document Access) APIs (B). SODA provides a simple, intuitive interface for developers to interact with JSON documents stored in the database using operations like insert, query, and update, without requiring SQL knowledge. Options A ("OASD"), C ("DOSA"), and D ("OSDA") are not recognized API sets in Oracle’s documentation. SODA is explicitly documented as the standard NoSQL API for Autonomous JSON Database, aligning with Oracle’s converged database strategy to support multiple data models.

Two important reasons to upgrade and patch are "Support Compliance" (A), ensuring the database remains under Oracle’s support policy with access to updates, and "Security" (C), applying patches to mitigate vulnerabilities and protect data. "Validation of application" (B) is a byproduct, not a primary reason, and "Restructure the database" (D) is unrelated to patching. Oracle’s maintenance guides emphasize compliance and security as key drivers.

The "MySQL Database Service" on OCI uses "Block Volume Storage" (D) for all data storage, providing high-performance, persistent block storage attached to compute instances for database operations. "Object Storage" (A) is for unstructured data, "Flash Local Storage" (B) is temporary and instance-specific, and "File Storage" (C) is for shared file systems—none suit MySQL’s needs. Oracle’s MySQL Service documentation confirms Block Volume as the standard.

Oracle’s Data Management strategy aims to deliver "the fastest, most scalable converged SQL database" (A), exemplified by its Autonomous Database and Exadata systems, which support multiple data types and workloads efficiently. It also seeks to "automate everything for developers, data analysts, DBAs, and data scientists" (B), leveraging self-managing, self-tuning capabilities to reduce manual effort and enhance productivity. Option C ("multiple integration points for 3rd party databases") is not a primary objective, as Oracle focuses on its own ecosystem. Option D ("best platform for warehouse workloads only") is too narrow, as the strategy spans beyond just warehousing to include transactional, analytical, and JSON workloads. Oracle’s official data management documentation emphasizes speed, scalability, and automation as core goals.

The Oracle Cloud and Microsoft Azure interconnect offers "Low Latency" (B), achieved through a high-speed, private connection between OCI and Azure data centers, reducing delays for cross-cloud workloads. It also provides "Unified identity and access management" (C), integrating Oracle IAM with Azure Active Directory for seamless user authentication. "Upgrade compliance" (A) is vague and not a documented benefit, while "Connection to other multicloud environments" (D) is not specific to this partnership. Oracle’s multicloud documentation highlights latency and identity integration as key advantages.

In the Autonomous Database (ADB), "Data Insights" (B) is the tool to discover anomalies, outliers, and hidden patterns, leveraging built-in AI and ML capabilities to analyze data. "Data Load" (A) is for importing data, "Data Transforms" (C) is for data preparation, and "Catalog" (D) is for metadata (OCI Data Catalog). Oracle’s documentation identifies Data Insights for pattern detection.

In the Oracle Data Platform, the "Supply Side" refers to foundational data elements that are passively collected and structured to provide meaning within the data ecosystem. According to Oracle’s official documentation, the supply side includes "Business rules" (A), which define the logic and policies governing data usage, ensuring consistency and compliance, and "Domain objects" (B), which represent structured entities or concepts within a business domain, forming the backbone of data modeling. "Technical definitions of business terms" (C) pertain to metadata management, and "Reports" (D) are outputs rather than core supply-side models. Oracle’s Data Platform Foundations emphasize business rules and domain objects as key components of the supply side for data integration and processing.

Oracle offers a "Dedicated cloud region" (D) as an industry-first feature to support multicloud strategies. This allows customers to run a fully managed OCI region within their own data center or a partner cloud, integrating with other providers like Azure or AWS. "Private and public cloud network" (A) is vague, "Cloud databases" (B) are common across vendors, and "Oracle FastConnect" (C) is a connectivity service, not unique to multicloud. Oracle’s documentation highlights the Dedicated Region as a pioneering multicloud solution.