Section 1 - Instruction

You've learned about partitioning, caching, and broadcasting for Spark performance optimization. Now let's practice applying these techniques to real scenarios.

Engagement Message

Ready to become a performance tuning expert?

Section 2 - Practice

Type

Multiple Choice

Practice Question

A data engineer notices their Spark job is running slowly. The Spark UI shows that 90% of the processing time is spent moving data between nodes. What's the most likely cause?

A. Too much data caching consuming memory B. Excessive data shuffling across the cluster C. Insufficient broadcasting of reference tables D. Poor partition distribution across executors

Suggested Answers

  • A
  • B - Correct
  • C
  • D
Section 3 - Practice

Type

Sort Into Boxes

Practice Question

Sort these scenarios into the correct optimization strategy:

Labels

  • First Box Label: Caching
  • Second Box Label: Broadcasting

First Box Items

  • Reused DataFrame
  • Repeated operations
  • Multiple queries

Second Box Items

  • Small lookup table
  • Reference data
  • Dimension table
Section 4 - Practice

Type

Swipe Left or Right

Practice Question

Match each performance problem with its most effective solution:

Labels

  • Left Label: Partitioning
  • Right Label: Broadcasting

Left Label Items

  • Data filtering frequently by date column but poor query performance
  • Join operations causing excessive data movement across cluster
  • Regional analysis queries scanning entire dataset
  • Customer-specific processing requiring data shuffling

Right Label Items

  • Small product catalog joined to large transaction dataset
  • Country codes lookup table used across all partitions
  • Static reference data needed by every processing task
  • Small dimension table referenced frequently
Section 5 - Practice

Type

Multiple Choice

Practice Question

A pipeline processes customer transactions and repeatedly filters by transaction date, joins with customer data, and calculates metrics. The same customer dataset is used in multiple operations. What optimization should be applied?

A. Partition the transaction data by customer ID B. Cache the customer dataset in memory C. Broadcast the transaction data to all nodes D. Increase the number of partitions for customer data

Suggested Answers

  • A
  • B - Correct
  • C
  • D
Section 6 - Practice

Type

Fill In The Blanks

Markdown With Blanks

Fill in the blanks about partition optimization:

When you have [[blank:too few]] partitions on a large cluster, you underutilize resources. When you have [[blank:too many]] partitions, you create coordination overhead.

Suggested Answers

  • too few
  • too many
  • optimal
  • cached
Section 7 - Practice

Type

Multiple Choice

Practice Question

Which scenario demonstrates proper use of broadcasting?

A. Broadcasting a 10GB customer transaction dataset to all nodes B. Broadcasting a 50MB product catalog used in joins across all partitions C. Broadcasting frequently changing data that updates hourly D. Broadcasting temporary intermediate results between processing stages

Suggested Answers

  • A
  • B - Correct
  • C
  • D
Section 8 - Practice

Type

Multiple Choice

Practice Question

A Spark job processes sales data partitioned by store location. The job frequently filters by date ranges and joins with a small product catalog. Which combination of optimizations would be most effective?

A. Partition by date, cache sales data, broadcast product catalog B. Partition by product, cache product catalog, broadcast sales data C. Partition by date only, no caching or broadcasting needed D. Cache all datasets, partition by customer, broadcast everything

Suggested Answers

  • A - Correct
  • B
  • C
  • D
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