MLA-C01 Data Preparation for Machine Learning Practice Question
In SageMaker Data Wrangler, you have a flow that imports data from Amazon S3 and needs to join it with a table from Amazon Redshift. The data volumes are large (hundreds of GB). Which approach is most efficient within Data Wrangler?
⚠ Common exam trap
It's easy for candidates to assume large-scale joins must be offloaded to external services like AWS Glue or Athena, but Data Wrangler's native Redshift source and join transform are designed for this exact use case, making the direct approach the most efficient.
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
✓
Import the Redshift table directly using a Data Wrangler source step and apply a join transform
SageMaker Data Wrangler natively supports Amazon Redshift as a source via a direct connection, allowing you to import the Redshift table as a source step and then apply a join transform within the same visual flow. This approach avoids unnecessary data movement or intermediate exports, which is critical for hundreds of GB of data, as it leverages Data Wrangler's optimized in-memory and Spark-based processing to perform the join efficiently.
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 Amazon Athena federated query to join in place and import the result
Why it's wrong here
Athena federated query can join but Data Wrangler does not directly support it as a source.
- ✗
Export the Redshift table to S3 as Parquet, then import both datasets into Data Wrangler and join
Why it's wrong here
Exporting the Redshift table to S3 as Parquet and then joining within Data Wrangler introduces an unnecessary intermediate export step, which adds latency and storage costs for hundreds of GB of data. This approach is tempting because it simplifies the flow by keeping all data in S3, and would be correct if the Redshift table were small or if you needed to reuse the exported Parquet file for multiple downstream transformations without querying Redshift repeatedly.
- ✗
Use AWS Glue to join the datasets and output to S3, then import the joined result into Data Wrangler
Why it's wrong here
This adds an extra service, increasing complexity and potential costs.
- ✓
Import the Redshift table directly using a Data Wrangler source step and apply a join transform
Why this is correct
Data Wrangler can connect to Redshift natively and perform joins efficiently.
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 |
Go deeper
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