You've learned how distributed processing works and how Spark coordinates clusters with Drivers and Executors. Now let's practice recognizing these concepts in action.
Engagement Message
Ready to test your understanding of how Spark makes distributed processing seamless?
Type
Multiple Choice
Practice Question
A data scientist writes a Spark program to analyze customer purchase data. Which component is responsible for reading the code and deciding how to split the work across the cluster?
A. The Executor nodes B. The Driver program C. The cluster manager D. The data storage system
Suggested Answers
- A
- B - Correct
- C
- D
Type
Sort Into Boxes
Practice Question
Sort these Spark responsibilities into the correct component:
Labels
- First Box Label: Driver
- Second Box Label: Executor
First Box Items
- Plans tasks
- Coordinates cluster
- Manages program
Second Box Items
- Processes data
- Runs transformations
- Executes jobs
Type
Swipe Left or Right
Practice Question
Match each scenario to the core distributed processing concept it demonstrates:
Labels
- Left Label: Parallel Processing
- Right Label: Data Locality
Left Label Items
- Five computers each analyzing different months of sales data simultaneously
- Multiple machines counting words in separate document sections at the same time
- Several nodes processing different customer segments concurrently
- Cluster handling distinct geographic regions in parallel
Right Label Items
- Processing files stored locally on each machine rather than transferring over network
- Keeping related customer data on the same node to avoid shuffling
- Storing frequently accessed data close to processing units
- Co-locating compute and storage for faster access
Type
Multiple Choice
Practice Question
A Spark job processes 1TB of log data across 20 Executors. If one Executor fails halfway through processing, what happens next?
A. The entire job fails and must be restarted from the beginning B. The remaining Executors continue, and Spark reassigns the failed work C. The job pauses until the failed Executor is manually restarted D. Data is lost from the failed Executor and results will be incomplete
Suggested Answers
- A
- B - Correct
- C
- D
Type
Fill In The Blanks
Markdown With Blanks
Fill in the blanks about Spark's coordination model:
The [[blank:Driver]] runs your main program and breaks it into smaller tasks. These tasks are distributed to [[blank:Executors]] across the cluster, where the actual data processing happens.
Suggested Answers
- Driver
- Executors
- Cluster
- Nodes
Type
Multiple Choice
Practice Question
Which scenario best demonstrates the advantage of Spark's automatic coordination over manual cluster management?
A. A developer writes complex networking code to distribute tasks manually B. A data scientist writes simple operations and Spark handles distribution C. An administrator configures each machine in the cluster individually D. A programmer manages task failures and retries with custom error handling
Suggested Answers
- A
- B - Correct
- C
- D
Type
Multiple Choice
Practice Question
Your company needs to process daily sales reports from 50 stores. Each store's data is 2GB. Which approach demonstrates proper distributed processing principles?
A. One computer processes all 50 stores sequentially, taking 50 times longer B. One computer processes larger batches of 10 stores at once C. Fifty computers each process one store's data simultaneously D. Ten computers process all data on one machine, then copy results
Suggested Answers
- A
- B
- C - Correct
- D
