1z0-1195-25 Practice Exam Questions with Answers Oracle Data Platform 2025 Foundations Associate Certification

"Oracle SQLcl" (SQL Developer Command Line) is a lightweight, command-line interface for interacting with Oracle Databases. It has two key characteristics: "Tracks database schema changes" (B), through integration with tools like Liquibase, allowing DBAs to monitor and manage schema evolution; and "Ability to execute SQL batch files" (C), enabling automation of multiple SQL statements in scripts. "Creates isolated development environments" (A) is not a feature of SQLcl, as it’s a client tool, not a virtualization platform. "Available in the OCI Cloud Shell by default" (D) is incorrect, as OCI Cloud Shell provides a general terminal but does not pre-install SQLcl. Oracle’s SQLcl documentation confirms these capabilities.

The primary goal of "Oracle Maximum Availability Architecture" (MAA) is "Active Replication, Data Protection and continuous availability" (B). MAA is designed to ensure high availability and disaster recovery through active replication (e.g., Data Guard), data protection (e.g., backups), and continuous availability (minimal downtime). Option A includes "network security," which issecondary, not primary. Option C focuses on "reporting," which is unrelated, and D emphasizes "scale out," which is a feature, not the goal. Oracle’s MAA documentation defines these core objectives.

A "MySQL Database Service DB System" includes a "Compute Instance" (A) for processing, a "Virtual Network Interface" (B) for networking, and "Block Storage" (D) for data. "FastConnect" (C), a high-speed connectivity service to OCI, is not a component of the DB system itself but an optional external connection. Oracle’s MySQL Service documentation lists the core components, excluding FastConnect.

An example of an in-database option in Oracle Automated Machine Learning (AutoML) is "Oracle Machine Learning for Python (OML4PY) APIs" (C). OML4Py enables Python-based machine learning directly within the database, leveraging in-database algorithms for performance and security. "REST Interface" (A) is an access method, "Cognitive text" (B) is a feature, not an algorithm type, and "Tuning of hyperparameter" (D) is a process, not a specific option. Oracle’s documentation highlights OML4Py as a key in-database ML tool.

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.

In Oracle’s security framework, the "Assess" security control area (C) is responsible for determining whether sensitive data exists within a system. This phase involves data discovery, classification, and risk analysis to identify sensitive information such as PII or financial data. "Users" (A) relates to access management, "Detect" (B) focuses on identifying threats or breaches, and "Protect" (D) involves implementing safeguards—none of these directly address the initial identification of sensitive data. Oracle’s Data Safe and OCI security documentation highlight "Assess" as the key stage for sensitive data discovery, often leveraging tools like Data Catalog or Data Safe.

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.

"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.

For connections to multiple public clouds via 3rd-party connectivity, Oracle supports "Megaport Cloud Router (MCR) and Virtual Cross Connect (VXC)" (C). Megaport provides a cloud router and virtual cross-connects to link OCI with other clouds (e.g., AWS, Azure) through a global network. "Layer 3 and FastConnect" (A) is incomplete, "VCNs and Amazon VPC" (B) are internal constructs, and "FastConnect and Direct Connect" (D) are specific to Oracle and AWS, not a 3rd-party solution. Oracle’s multicloud connectivity documentation confirms Megaport’s role.

The final release supporting the "non-CDB (non-Container Database)" architecture is "Oracle Database 19c" (B). Oracle deprecated the non-CDB architecture starting with 20c (never fully released) and mandated the multitenant architecture (CDB with PDBs) from 21c onward. 19c, as a long-term support release, was the last to support non-CDB databases, with Oracle recommending migration to multitenant in subsequent versions like 23c. Oracle’s documentation confirms 19c as the end of non-CDB support.

The "Converged Database" facilitates API access by "A REST API is automatically generated on top of SQL or stored procedures" (B), using Oracle REST Data Services (ORDS). This allows developers to expose database logic as RESTful endpoints without manual coding. Option A is incorrect, as no separate compute instance is provided. Option C oversimplifies, as multiple REST interfaces can be created, not just one. Option D misrepresents the ADB Wallet’s role, which is for security, not API generation. Oracle’s documentation highlights ORDS auto-generation as a converged feature.

The Exadata X8M leverages "Remote Direct Memory Access" (RDMA) to access "Persistent Memory" (B). RDMA allows direct memory-to-memory data transfer between systems without CPU involvement, enhancing performance. In Exadata X8M, RDMA accesses persistent memory (PMEM) to accelerate database operations by reducing latency and improving I/O throughput. "Compute Instances" (A) and "Database Servers" (D) are broader entities, not the specific resource RDMA targets, while "Storage Disks" (C) are traditional storage, not memory. Oracle’s Exadata documentation highlights RDMA’s use with persistent memory in X8M systems.