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DP-900 · Practice set 4 of 4

Analytical Workloads: 10 practice questions

10 questions · Untimed · Free

10 free DP-900 practice questions on Analytical Workloads, with an explanation for every answer. Untimed. The full mock exam and the timed version are in the app.

Set 4 · Analytical Workloads · 10 questions Read the lesson

  1. Question 1 of 10

    Which system pattern is typical of analytical data processing?

    1. AA write-optimized system containing individual live transactions
    2. BA CRUD system containing current line-of-business events
    3. CA transactional system containing rapidly changing payment records
    4. DA read-mostly system containing vast volumes of historical data
    Show the answer

    Analytical processing emphasizes queries over historical data or business metrics, whereas transactional processing emphasizes current event recording and both reads and writes.

    Next → 1 / 10

  2. Question 2 of 10

    What is the operation order in an ETL process?

    1. AExtract, transform, and then load
    2. BLoad data, extract it, and then transform it
    3. CTransform the data, load it, and then extract it
    4. DExtract, load, then transform
    Show the answer

    ETL applies transformation before the load into analytical storage; ELT is the neighboring pattern that performs the load first.

    Next → 2 / 10

  3. Question 3 of 10

    Which description identifies a data warehouse?

    1. AA relational schema optimized for read queries and reporting
    2. BA file store optimized for frequent transactional record updates
    3. CA semantic model optimized for raw event write operations
    4. DA live database optimized exclusively for create and delete operations
    Show the answer

    A data warehouse uses a fully relational SQL-oriented structure for analytical reads, especially reporting and visualization queries.

    Next → 3 / 10

  4. Question 4 of 10

    How does ELT differ from ETL in an analytical architecture?

    1. AELT queries reports before extracting their source data.
    2. BELT aggregates data before loading it into any store.
    3. CELT applies transformations before extracting operational data.
    4. DELT loads extracted data before applying transformations.
    Show the answer

    The position of transformation is decisive: ELT performs it after loading, while ETL performs it before loading.

    Next → 4 / 10

  5. Question 5 of 10

    How are numeric summaries organized in an OLAP or semantic model?

    1. AMeasures from fact tables are aggregated across dimensions.
    2. BOperational facts are deleted after each query.
    3. CMeasures from dimension tables are stored without aggregation.
    4. DDimensions from fact tables are written as individual transactions.
    Show the answer

    Measures provide aggregated numeric values, while dimensions supply the perspectives whose intersections define summaries such as revenue by date, customer, and product.

    Next → 5 / 10

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  7. Question 6 of 10

    Which capability combination defines a data lakehouse?

    1. AFlexible data lake storage with relational warehouse-style querying
    2. BRelational warehouse storage with processing limited to transactional writes
    3. COperational database storage with create-only record access
    4. DPreaggregated semantic storage with no file-based foundation
    Show the answer

    A lakehouse unifies the scalable file-oriented foundation of a data lake with the relational query semantics associated with a warehouse.

    Next → 6 / 10

  8. Question 7 of 10

    Which activity is associated with a data analyst in the analytical architecture?

    1. ARecording retail payments in a live transactional application
    2. BConsuming preaggregated dashboards as a business user
    3. CQuerying warehouse tables for complex reports and visualizations
    4. DExploring raw data lake files directly to build scientific data models
    Show the answer

    The architecture distinguishes direct file exploration by data scientists, warehouse reporting by data analysts, and report consumption by business users.

    Next → 7 / 10

  9. Question 8 of 10

    A team must collect and analyze a very large volume of file-based data. Which analytical store matches this requirement?

    1. AA transactional payment record
    2. BA data lake
    3. CA live OLTP database
    4. DA line-of-business application
    Show the answer

    The deciding characteristics are scale and file-based storage, which align directly with the analytical data lake pattern.

    Next → 8 / 10

  10. Question 9 of 10

    Users need fast access to precomputed sales summaries and must navigate from region to city to individual address. Which storage type best fits?

    1. AA raw file collection in a data lake
    2. BA live database for transactional CRUD operations
    3. CA preaggregated OLAP or semantic model
    4. DAn operational source awaiting extraction and transformation
    Show the answer

    Preaggregation provides fast summaries, and dimension levels provide drill-up and drill-down navigation through the geographic hierarchy.

    Next → 9 / 10

  11. Question 10 of 10

    A team designs lakehouse tables from an OLTP source and accepts some duplicate values to improve analytical query speed. Which design choice does this describe?

    1. AIsolating concurrent transactions in the analytical tables
    2. BDeleting duplicate values after every reporting query
    3. CNormalizing every value to accelerate transactional writes
    4. DDenormalizing the source data for the lakehouse schema
    Show the answer

    Lakehouse table schemas may trade some duplication for faster analytical queries by denormalizing data that originated in an OLTP source.

    Next → 10 / 10

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