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CompTIA Data+ (V2) Free Sample Questions

Description

Covers data concepts and infrastructure, data acquisition and transformation, selecting statistical methods and troubleshooting analysis, report visualization, and data governance and privacy.

12 free sample questions159 in the full practice test Other version: DA0-001(249)

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DA0-002 Sample Questions

  1. Question 1

    Intermediate

    Data Concepts and Environments · Identify types of data sources

    A healthcare provider requires a centralized data repository that can store massive volumes of unstructured medical imaging files while simultaneously supporting highly structured, ACID-compliant SQL transactions for patient billing records. Which of the following data repository architectures is BEST suited for this requirement?

    Answer and explanation

    Correct answer: C

    A Data Lakehouse is the correct answer. It is a modern architecture that combines the flexibility and cost-efficiency of a data lake (ideal for unstructured data like medical images) with the ACID transactional capabilities and data management features of a traditional data warehouse (required for billing records). Data Lakes lack native ACID transactional support, and Data Warehouses are poorly suited for unstructured data storage. A Data Mart is just a subset of a data warehouse focused on a specific business line.

  2. Question 2

    Beginner

    Data Concepts and Environments · Explain data concepts

    While configuring a new database table to store digitally signed PDF contracts that average 50MB in size, a database administrator asks you to define the data type for the contract column. Which data type is the MOST appropriate for this field?

    Answer and explanation

    Correct answer: C

    BLOB (Binary Large Object) is the correct data type for storing binary data such as images, multimedia, or PDF files. CLOB (Character Large Object) is used for massive amounts of text data (like large XML or JSON documents), not binary files. String is used for standard, short alphanumeric text. GUID is used for generating unique identifiers, not for file storage.

  3. Question 3

    AdvancedMultiple answers

    Data Concepts and Environments · Identify artificial intelligence (AI) concepts

    An e-commerce company is modernizing its analytics capabilities. The project sponsor wants to implement technologies that can automatically summarize thousands of long-form customer reviews into single-paragraph insights, and also create realistic, synthetic demographic data to safely test a new application without exposing real PII. Which TWO of the following AI concepts should the team implement? (Select TWO)

    Answer and explanation

    Correct answers: B, D

    Natural Language Processing (NLP) is required to understand, interpret, and summarize the human language in the customer reviews. Generative AI is required to create the realistic, synthetic demographic data for testing. RPA is used for automating repetitive, rule-based tasks (like data entry), not generating text or synthetic data. Predictive analytics forecasts future outcomes based on historical data. Descriptive analytics summarizes what happened in the past.

    Generative AI is required to create the realistic, synthetic demographic data for testing. Natural Language Processing (NLP) is required to understand, interpret, and summarize the human language in the customer reviews. RPA is used for automating repetitive, rule-based tasks (like data entry), not generating text or synthetic data. Predictive analytics forecasts future outcomes based on historical data. Descriptive analytics summarizes what happened in the past.

  4. Question 4

    Intermediate

    Data Concepts and Environments · Identify infrastructure concepts

    To ensure consistency across development and production environments, a data engineering team wants to package their Python-based data processing scripts along with all necessary libraries, dependencies, and a lightweight operating system. This package must be able to run identically on a developer's local laptop, an on-premises server, or in AWS. Which infrastructure concept accomplishes this goal?

    Answer and explanation

    Correct answer: B

    Containerization (using tools like Docker) packages code, libraries, and dependencies into a single isolated unit called a container. This ensures the application runs identically across different computing environments (local, on-premises, or cloud). Block storage is a storage architecture. A Data Lakehouse is a data repository. Object storage handles unstructured data storage at scale.

    graph TD subgraph Application Code[Python Scripts] Libs[Pandas/Libraries] Bins[OS Binaries] end Application --> Container[Container Engine / Docker] Container --> OS[Host Operating System] OS --> Hardware[Infrastructure: Laptop / On-Prem / AWS]

  5. Question 5

    Beginner

    Data Concepts and Environments · Identify common data analysis tools

    True or False: MongoDB Compass is a graphical user interface specifically designed for querying and managing relational SQL databases such as PostgreSQL and Microsoft SQL Server.

    Answer and explanation

    Correct answer: B

    This statement is False. MongoDB Compass is the official graphical user interface (GUI) specifically designed for MongoDB, which is a NoSQL document database. Tools like DBeaver, SQL Server Management Studio (SSMS), or pgAdmin are typically used for relational SQL databases like PostgreSQL or SQL Server.

  6. Question 6

    Intermediate

    Data Acquisition and Preparation · Given a scenario, use data acquisition methods

    A retail enterprise recently migrated to a highly scalable cloud data warehouse. They need to ingest massive volumes of raw web clickstream data quickly. To maximize efficiency, the data engineering team decides to load the raw data directly into the warehouse first, and then utilize the cloud warehouse's massive compute power to process and clean the data. Which integration pattern does this describe?

    Answer and explanation

    Correct answer: B

    This describes ELT (Extract, Load, Transform). In modern cloud data warehouses, it is often more efficient to Extract raw data, Load it directly into the target system, and then use the target system's robust compute capabilities to Transform the data natively. ETL performs the transformation in a separate staging server before loading. Delta loads and full loads refer to the volume of data being moved, not the architectural pattern of the transformation.

    flowchart LR Source[(Data Sources)] -->|Extract| Pipeline[Data Pipeline] Pipeline -->|Load Raw Data| Target[(Cloud Data Warehouse)] Target -->|Transform Natively| Target

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