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DP-203 Develop data processing Practice Question

Which THREE considerations are important when designing a data processing solution using Azure Databricks for ETL workloads? (Select three.)

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

Auto-scaling of compute resources

Auto-scaling of compute resources ensures that the cluster can adjust to workload demands, optimizing cost and performance. Option B is correct because Delta Lake provides ACID transactions, schema enforcement, and optimization features that enhance data reliability in ETL pipelines. Option E is correct because proper cluster configuration and autoscaling are essential for managing resources efficiently. Option C is incorrect because Azure Databricks uses a pay-as-you-go model, not a fixed pricing model, making it more flexible. Option D is incorrect because minimum network bandwidth is not a typical design consideration for Databricks ETL workloads, as network performance is generally sufficient.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • Auto-scaling of compute resources

    Why this is correct

    Auto-scaling helps handle variable workloads efficiently.

  • Use of Delta Lake for data reliability

    Why this is correct

    Delta Lake provides ACID transactions, schema enforcement, and performance optimizations.

  • Fixed pricing model

    Why it's wrong here

    Azure Databricks uses per-second billing, not fixed pricing.

  • Minimum network bandwidth

    Why it's wrong here

    Network bandwidth is not typically a primary design consideration for Databricks ETL.

  • Cluster configuration and autoscaling

    Why this is correct

    Proper cluster sizing and autoscaling are crucial for performance and cost.

Visual reference

R1 R2 R3 R4 10 100 10 100 OSPF picks R1→R2→R4 (cost 20) over R1→R3→R4 (cost 200)

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