Oracle Data Platform 2025 Foundations Associate Practice Exam 2025: Latest Questions

A retail team wants a high-level understanding of how Oracle’s data services fit together for ingestion, storage, processing, governance, and analytics. Which statement best describes the purpose of the Oracle Data Platform?

A data engineer must load daily data from an on-premises Oracle Database into Oracle Autonomous Data Warehouse. The requirement is near-real-time, incremental replication with minimal custom code. Which service is the most appropriate choice?

A business analyst wants to explore curated sales data using interactive dashboards and self-service data discovery. Which Oracle service is primarily intended for this purpose?

A team is building a data lake on OCI and wants a durable, scalable place to store raw files (CSV, JSON, Parquet) at low cost. Which OCI service is typically used as the foundational storage layer?

A company needs to integrate multiple sources into a warehouse. Some sources arrive as batch files nightly, while others require frequent incremental updates. Which approach best aligns with Oracle best practices for integrating these mixed patterns?

A warehouse team wants to improve query performance for large fact tables by reducing full table scans for common filters like date and region. Which design technique is most appropriate?

A data lake contains raw and curated zones in Object Storage. Analysts report inconsistent results because different teams interpret columns differently. Which capability most directly addresses this problem across the platform?

A data engineering team wants to run large-scale Spark transformations on files in OCI Object Storage without managing a long-running cluster. Which service best fits this requirement?

A team is using CDC to replicate changes from a source database into a target. They notice occasional duplicates after network interruptions, but they must guarantee that the target reflects each committed change exactly once. What is the best mitigation strategy?

An enterprise wants a lakehouse-style architecture: store raw data in Object Storage, process it with Spark, and expose curated, high-performance relational analytics with strong governance and SQL access. Which architecture best matches this goal?

A data engineer must ingest daily CSV files from Oracle Object Storage into Autonomous Data Warehouse (ADW). The files need basic cleansing (type casting and trimming) and must be loaded on a schedule with minimal custom code. Which Oracle Cloud service is the best fit?

A team is building dashboards in Oracle Analytics Cloud and needs a semantic model that enforces consistent business metrics (for example, "Net Revenue") across reports. Which component is intended to provide this governed semantic layer?

A data lake on OCI must store raw and curated data inexpensively and scale automatically. Which service is most commonly used as the foundational storage layer for an OCI data lake?

A company wants to integrate data from multiple SaaS applications and on-premises sources into a cloud data warehouse. They want to standardize on a service that provides built-in connectors, data transformation, and orchestration. Which approach best matches this requirement?

A BI team reports inconsistent query results because different analysts apply their own filters and joins when querying the same warehouse tables. What is the best practice to improve consistency and governance for analytics consumption?

A data pipeline loads customer records nightly. The target table must not contain duplicate customers, and new records should update existing ones based on a business key. Which loading pattern best supports this requirement?

A data architect is designing a lakehouse-style architecture on OCI. They want raw files in Object Storage, SQL analytics for analysts, and the ability to create curated, query-optimized datasets without forcing all users to manage file formats. Which high-level approach best meets the goal?

A data engineering team needs to orchestrate multi-step ingestion: land files, validate schema, load to staging, transform to curated tables, then notify downstream systems. They want retry logic and clear visibility into pipeline runs. What is the most appropriate solution pattern?

A team loads semi-structured JSON from Object Storage into a data warehouse for analytics. Queries are slow because analysts frequently parse JSON at query time and apply multiple nested filters. Which design change most directly improves performance and usability?

A company must build a governed data lake where different teams can access only their approved datasets. They want to enforce least privilege, audit access, and avoid embedding long-lived credentials in data pipelines. Which combination of practices best addresses these requirements?

A team is new to the Oracle Data Platform and wants a single, high-level capability that explains how Oracle handles data across ingestion, storage, processing, governance, and consumption. Which description best matches the Oracle Data Platform approach?

A retailer ingests daily CSV extracts from multiple vendors into Object Storage. The data sometimes arrives with missing columns or unexpected data types, causing downstream loads to fail. What is the best practice to improve reliability before loading curated datasets?

A business intelligence dashboard queries a star schema in a data warehouse. Users complain that filtering by time is slow, even though the fact table is large and continuously growing. Which design choice most directly improves query performance for common time-based filters?

A data lake has raw, curated, and sandbox datasets stored in Object Storage. The security team requires that data access be granted based on business roles (for example, Finance Analyst) and that permissions remain consistent across services used for analytics. Which approach best meets this requirement?

A team built an analytics pipeline that lands raw files in Object Storage, transforms them, and loads curated tables. After a recent change, the curated tables show duplicated rows for the same business keys. Re-running the pipeline sometimes makes duplicates worse. Which change most directly addresses the root cause in a robust way?