- A
Use a broadcast join if one of the tables is small enough.
Avoids shuffling small table.
- B
Use a salted join key to distribute skewed keys across partitions.
Reduces memory pressure from hot keys.
- C
Increase the number of DPUs for the Glue job.
Why wrong: General increase may not address skew.
- D
Repartition the data on the join key before the join operation.
Why wrong: Does not address skew; may worsen.
- E
Split the transformation into multiple Glue job steps to reduce per-step memory.
Reduces memory per stage.
Question 905 of 1,786
DEA-C01 Data Ingestion and Transformation Practice Question
This DEA-C01 practice question tests your understanding of data ingestion and transformation. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
A company uses AWS Glue to run ETL jobs that transform data from Amazon S3 (Parquet) into a denormalized format for Amazon Redshift. The Glue job uses the DynamicFrame API. The job is failing with a 'MemoryError' when performing a join operation. The data is skewed on the join key. Which THREE actions can reduce memory usage and improve job stability? (Choose 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
✓
Use a broadcast join if one of the tables is small enough.
Option A is correct because a broadcast join (using `join` with `broadcast` hint or `DynamicFrame.join(..., transformation_ctx='...')` with broadcast enabled) avoids shuffling the larger table across the cluster by copying the small table to every executor. This eliminates the memory pressure from skewed key distribution during the shuffle phase, reducing the risk of a MemoryError.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Use a broadcast join if one of the tables is small enough.
Why this is correct
Avoids shuffling small table.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Use a salted join key to distribute skewed keys across partitions.
Why this is correct
Reduces memory pressure from hot keys.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Increase the number of DPUs for the Glue job.
Why it's wrong here
General increase may not address skew.
- ✗
Repartition the data on the join key before the join operation.
Why it's wrong here
Does not address skew; may worsen.
- ✓
Split the transformation into multiple Glue job steps to reduce per-step memory.
Why this is correct
Reduces memory per stage.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume increasing resources (DPUs) or repartitioning will fix memory issues, but they fail to recognize that data skew on the join key is the root cause, which requires skew-aware techniques like salting or broadcast joins.
Detailed technical explanation
How to think about this question
In AWS Glue, DynamicFrames use Apache Spark under the hood, and a standard join triggers a full shuffle where all records with the same key are sent to the same executor. A salted join key (Option B) adds a random prefix to the key to distribute records across partitions, then a second join removes the salt, effectively balancing the load. Broadcast joins (Option A) leverage Spark's broadcast variable to avoid shuffle entirely for small tables (typically < 100 MB after compression). Splitting into multiple steps (Option E) reduces per-step memory by allowing intermediate data to be persisted to disk or S3, preventing a single stage from holding all data in memory.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this DEA-C01 question test?
Data Ingestion and Transformation — This question tests Data Ingestion and Transformation — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a broadcast join if one of the tables is small enough. — Option A is correct because a broadcast join (using `join` with `broadcast` hint or `DynamicFrame.join(..., transformation_ctx='...')` with broadcast enabled) avoids shuffling the larger table across the cluster by copying the small table to every executor. This eliminates the memory pressure from skewed key distribution during the shuffle phase, reducing the risk of a MemoryError.
What should I do if I get this DEA-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jul 4, 2026
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