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DP-750 · Practice set 5 of 11

Selecting Azure Databricks Compute Types: 10 practice questions

10 questions · Untimed · Free

10 free DP-750 practice questions on Selecting Azure Databricks Compute Types, with an explanation for every answer. Untimed. The full mock exam and the timed version are in the app.

Set 5 · Selecting Azure Databricks Compute Types · 10 questions Read the lesson

  1. Question 1 of 10

    Which compute type is designed for SQL queries, analytics, and business intelligence?

    1. AA SQL warehouse
    2. BA classic job cluster
    3. CAn instance pool
    4. DA dedicated all-purpose cluster
    Show the answer

    A SQL warehouse is the workload-specific compute surface for analytics and BI; job and all-purpose clusters serve different execution patterns, while a pool supplies instances rather than executing SQL itself.

    Next → 1 / 10

  2. Question 2 of 10

    Which compute type is intended for automated workflows and terminates after its tasks finish?

    1. AShared all-purpose compute
    2. BAn instance pool
    3. CA pro SQL warehouse
    4. DJob compute for automated workflow execution
    Show the answer

    Job compute aligns the cluster lifecycle with an automated run, which avoids retaining idle compute after the tasks complete; shared compute, warehouses, and pools have different purposes.

    Next → 2 / 10

  3. Question 3 of 10

    Which access mode isolates each user's code when multiple users share one classic cluster?

    1. ASingle-node cluster mode
    2. BClassic compute in Standard access mode
    3. CServerless performance mode
    4. DDedicated access mode
    Show the answer

    Standard access mode is the shared-compute choice because Lakeguard isolates multiple users on one classic cluster; Dedicated assigns the resource to one user or group.

    Next → 3 / 10

  4. Question 4 of 10

    A notebook workload uses RDD APIs. Which compute selection supports that requirement?

    1. AClassic compute with Dedicated access mode
    2. BA serverless SQL warehouse with Photon
    3. CClassic compute with Standard access mode
    4. DServerless notebook compute with automatic scaling
    Show the answer

    RDD APIs require the machine-level capabilities of classic compute with Dedicated access mode; serverless notebooks and Standard shared compute do not provide that support.

    Next → 4 / 10

  5. Question 5 of 10

    Which serverless characteristic most directly reduces infrastructure-management effort?

    1. AAzure Databricks allocates and scales the resources
    2. BThe customer selects and patches each virtual machine
    3. CThe compute runs as visible virtual machines in the customer's subscription
    4. DA fixed worker count remains active between workloads
    Show the answer

    Serverless shifts resource allocation and scaling to Azure Databricks, unlike classic compute where the customer chooses and manages cluster configuration.

    Next → 5 / 10

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  7. Question 6 of 10

    A SQL workload requires connectivity through a custom network to an on-premises database. Which warehouse type fits this constraint?

    1. AA classic SQL warehouse for basic exploration
    2. BA serverless SQL warehouse
    3. CServerless notebook compute
    4. DA pro SQL warehouse with custom networking
    Show the answer

    A pro SQL warehouse retains Photon and Predictive IO while supporting custom networking; serverless warehouses do not provide that custom network integration.

    Next → 6 / 10

  8. Question 7 of 10

    What distinguishes classic compute from serverless compute for runtime management?

    1. AClassic compute lets the user select and manage a Databricks Runtime version
    2. BClassic compute automatically uses a versionless runtime
    3. CServerless compute requires the user to schedule runtime upgrades
    4. DServerless compute exposes each underlying virtual machine for runtime installation
    Show the answer

    Classic compute provides explicit runtime selection, whereas serverless uses an automatically upgraded versionless runtime and hides infrastructure management.

    Next → 7 / 10

  9. Question 8 of 10

    A team runs infrequent Python ETL jobs. Each run should start quickly, scale without cluster sizing, and release resources afterward. The code uses no RDD APIs, JARs, or custom Spark settings. Which selection best fits?

    1. AServerless job compute
    2. BShared classic all-purpose compute in Standard mode
    3. CClassic job compute attached to an instance pool
    4. DA serverless SQL warehouse for the Python tasks
    Show the answer

    Both serverless and classic job compute could automate the workflow, but serverless wins the stated tradeoff because it removes sizing and infrastructure work while starting quickly for a supported Python workload.

    Next → 8 / 10

  10. Question 9 of 10

    Several engineers must collaborate on one cluster to control cost. Their Python and SQL notebooks do not use RDDs, GPUs, R, or custom containers, and user workloads must remain isolated. Which choice best fits?

    1. AClassic compute dedicated to the engineering group
    2. BClassic compute with Standard access mode
    3. CA pro SQL warehouse for all notebook work
    4. DClassic compute dedicated to one engineer
    Show the answer

    Two of these options share one cluster, so the decision turns on isolation. Standard access mode is the one that lets several users work on the same classic cluster at once with Lakeguard keeping each person's code isolated from the others. Dedicated access can assign compute to one person or group without requiring machine-level features. Group members do not get the code isolation this team needs.

    Next → 9 / 10

  11. Question 10 of 10

    A production notebook trains a neural network with GPU instances and also uses R code. The team needs to choose the runtime version and keep the resources in its Azure subscription. Which compute choice satisfies all constraints?

    1. AClassic compute with Dedicated access mode
    2. BClassic compute with Standard access mode
    3. CA serverless SQL warehouse with dynamic scaling
    4. DServerless notebook compute with automatic runtime upgrades
    Show the answer

    Classic Dedicated compute supports GPU and R workloads, exposes runtime selection, and runs in the customer's Azure subscription; the other choices fail at least one capability or ownership constraint.

    Next → 10 / 10

  12. You’ve finished this set

    That’s 10 questions on Selecting Azure Databricks Compute Types. In the app the ones you miss come back exactly when you’re about to forget them.

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