MLS-C01 Data Engineering Practice Question
A company runs a critical data pipeline using Apache Spark on Amazon EMR. The pipeline reads data from Amazon S3, performs complex transformations, and writes results back to S3. The job runs every hour and must complete within 30 minutes. Recently, the job has been taking longer and occasionally failing due to executor losses. The team suspects memory pressure. Which action should the team take to improve stability and performance without increasing cost?
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
✓
Enable Spark dynamic allocation to adjust executors based on workload.
Enabling Spark dynamic allocation allows the cluster to automatically scale the number of executors up and down based on the workload. This helps alleviate memory pressure by releasing idle executors and requesting additional executors only when needed, improving resource utilization without increasing overall cluster cost. Option A is incorrect because simply increasing spark.executor.memory may cause YARN container failures if the instance memory is exceeded, and does not address the root cause of executor losses. Option B is incorrect because adding core nodes increases cost and may not resolve memory pressure if the issue is inefficient resource allocation. Option C is incorrect because decreasing shuffle partitions reduces parallelism and can increase memory per task, potentially worsening memory pressure and prolonging job runtime.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Increase the spark.executor.memory setting to allocate more memory per executor.
Why it's wrong here
If the instance does not have enough memory, this can cause container failures.
- ✗
Increase the number of core nodes in the EMR cluster.
Why it's wrong here
Adding nodes increases cost.
- ✗
Decrease the number of shuffle partitions (spark.sql.shuffle.partitions) to reduce overhead.
Why it's wrong here
Fewer partitions can lead to larger partitions and more memory pressure.
- ✓
Enable Spark dynamic allocation to adjust executors based on workload.
Why this is correct
Dynamic allocation helps utilize resources efficiently and prevents over-allocation.
Visual reference
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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