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Azure Data Fundamentals (DP-900) Exam Preparation: Analytics workloads

اختبار موضوعي بلا وقت محدد: 20 سؤالًا عن Analytics workloads، مع شرح بعد كل إجابة. تُخلط الأسئلة والإجابات في كل مرة.

  • دورة
  • كندا
  • عدد الأسئلة: 80
  • تمت المراجعة في 13 أكتوبر 2026
  • تم التحقق من الحقائق في 13 أكتوبر 2026
ابدأ بالدروسAzure Data Fundamentals (DP-900): Lessons: نحو 300 دقيقة من القراءة. كل أسئلة الاختبار مشروحة في الدروس.

اختبار الموضوع 4: Analytics workloads

  1. السؤال 1 من 20

    In a large-scale analytics solution, what happens in the data ingestion and processing layer?
    أظهر الإجابة

    الإجابة: A. Source data is loaded into a lake or warehouse with ETL or ELT

    The ingestion layer brings data from transactional stores, files and streams into an analytical store, usually with ETL or ELT.

  2. السؤال 2 من 20

    In a star schema, which table holds the numeric values you want to analyze, such as sales amounts?
    أظهر الإجابة

    الإجابة: B. The fact table

    Fact tables hold the numeric measures. Dimension tables hold the entities you aggregate by, such as customer, product, store and time.

  3. السؤال 3 من 20

    A data warehouse links a Product dimension table to a separate Category table. What is this design called?
    أظهر الإجابة

    الإجابة: A. A snowflake schema

    A snowflake schema adds more tables to the dimensions to represent hierarchies, such as products grouped into categories.

  4. السؤال 4 من 20

    Data lakes often apply a tabular schema to files only when the data is read for analysis. What is this approach called?
    أظهر الإجابة

    الإجابة: B. Schema-on-read

    Data lakes use schema-on-read, so files are stored without constraints and a schema is applied when the data is queried.

  5. السؤال 5 من 20

    Which open-source format adds relational table features on top of Parquet files and is the standard lakehouse format for Microsoft Fabric and Azure Databricks?
    أظهر الإجابة

    الإجابة: C. Delta Lake

    Delta Lake builds on Parquet by adding a transaction log, which enables ACID transactions, versioning and schema enforcement.

  6. السؤال 6 من 20

    Which statement describes Microsoft Fabric?
    أظهر الإجابة

    الإجابة: C. A unified SaaS analytics platform built on OneLake

    Fabric brings data engineering, data warehousing, real-time analytics, data science and Power BI together in one SaaS workspace on OneLake.

  7. السؤال 7 من 20

    Which platform is built on Apache Spark, runs as a managed service inside your Azure subscription and uses Delta Lake as its native format?
    أظهر الإجابة

    الإجابة: A. Azure Databricks

    Azure Databricks is a cloud analytics platform built on Apache Spark for data engineering, data science and SQL analytics.

  8. السؤال 8 من 20

    Your data is structured, your team writes SQL, and you need strong schema enforcement for many users at once. Which Fabric item fits best?
    أظهر الإجابة

    الإجابة: D. Fabric Warehouse

    Fabric Warehouse is a fully managed, SQL Server-compatible relational warehouse that suits structured data and strong schema enforcement.

  9. السؤال 9 من 20

    A team wants an Azure SQL Database to be copied into OneLake continuously, in near real time, without building pipelines. Which Fabric feature fits?
    أظهر الإجابة

    الإجابة: C. Mirroring

    Mirroring continuously replicates external databases such as Azure SQL Database or Azure Cosmos DB into OneLake in Delta Lake format.

  10. السؤال 10 من 20

    What is a OneLake shortcut?
    أظهر الإجابة

    الإجابة: D. A live reference to external data, with no copying

    A shortcut points to data where it already lives, such as ADLS Gen2 or Amazon S3. There is no pipeline, movement or duplication.

  11. السؤال 11 من 20

    A credit card company sends each customer one bill a month for all of that month's purchases. Which kind of processing is this an example of?
    أظهر الإجابة

    الإجابة: C. Batch processing

    In batch processing, records are collected and stored, then processed together as a group, such as a monthly bill.

  12. السؤال 12 من 20

    Which statement correctly compares stream processing with batch processing?
    أظهر الإجابة

    الإجابة: A. Stream latency is usually seconds or less; batch latency is usually hours

    Stream processing works on each event as it arrives, with latency in seconds or milliseconds. Batch latency is typically a few hours.

  13. السؤال 13 من 20

    A streaming query calculates the average temperature over the last 30 seconds, again and again. What is the query using?
    أظهر الإجابة

    الإجابة: C. A rolling time window

    Stream processing usually sees only recent data or a rolling time window, and it suits simple calculations such as rolling averages.

  14. السؤال 14 من 20

    Which service is a data ingestion service similar to Azure Event Hubs but optimized for event data from Internet of things (IoT) devices?
    أظهر الإجابة

    الإجابة: D. Azure IoT Hub

    Azure IoT Hub is like Event Hubs, but it is optimized to manage event data from IoT devices.

  15. السؤال 15 من 20

    Which Azure service is a PaaS option for streaming jobs that read from a source, apply a perpetual query and write the results to an output?
    أظهر الإجابة

    الإجابة: A. Azure Stream Analytics

    Azure Stream Analytics runs streaming jobs with a perpetual query, and it suits standalone or hybrid streaming outside Fabric.

  16. السؤال 16 من 20

    In Microsoft Fabric Real-Time Intelligence, an Eventhouse stores time-series and event data. Which query language do you use with it?
    أظهر الإجابة

    الإجابة: D. KQL (Kusto Query Language)

    An Eventhouse is optimized for time-series and event data and is queried with KQL, a language built for fast log and telemetry analysis.

  17. السؤال 17 من 20

    Which part of Fabric Real-Time Intelligence triggers automated actions when streaming data meets conditions you set?
    أظهر الإجابة

    الإجابة: B. Activator

    Activator turns insights into actions by firing alerts or actions when the data meets defined conditions.

  18. السؤال 18 من 20

    What is the usual Power BI workflow?
    أظهر الإجابة

    الإجابة: C. Build in Power BI Desktop, publish to the Power BI service, then share

    Analysts build in Power BI Desktop, publish to the Power BI service for sharing and scheduled refresh, and users consume reports in a browser or on the phone app.

  19. السؤال 19 من 20

    In a Power BI semantic model, users view sales by year and then drill down to months and days. What makes this possible?
    أظهر الإجابة

    الإجابة: B. A hierarchy in the time dimension

    Hierarchies let you drill up or down through aggregated values at different levels of a dimension, such as year, month and day.

  20. السؤال 20 من 20

    A report must show how marketing spend relates to revenue for each campaign, to see if there is a correlation. Which visual fits best?
    أظهر الإجابة

    الإجابة: C. A scatter plot

    Scatter plots compare two numeric measures and help you see a relationship or correlation between them.

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