The answer is to use AWS Glue DynamicFrame to repartition the data and write it as Parquet. This is correct because DynamicFrames offer built-in optimizations for columnar formats like Parquet, enabling predicate pushdown during queries and efficient compression that reduces storage costs, while the `repartition()` method controls output file size and count for better downstream performance. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of how to convert CSV to Parquet and partition by date within a Glue ETL job, often appearing as a distractor against using Spark DataFrames or manual partitioning—the key trap is forgetting that DynamicFrames handle schema evolution and partitioning more flexibly for ML pipelines. Memory tip: think “DynamicFrames for dynamic partitioning”—they automatically manage partition keys like date columns when writing to S3, so you avoid extra steps.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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 is using AWS Glue to prepare data for a machine learning pipeline. The source data is in an Amazon S3 bucket in CSV format. The data scientist wants to convert the data to Parquet format and partition it by date. Which AWS Glue feature should be used to optimize the data for query performance and reduce storage costs?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use AWS Glue DynamicFrame to repartition the data and write it as Parquet.
Option B is correct because AWS Glue DynamicFrames provide built-in optimizations for writing data in columnar formats like Parquet, which improves query performance through predicate pushdown and compression, and reduces storage costs by using efficient encoding. The DynamicFrame's `repartition()` method allows you to control the number of output files, and writing as Parquet directly from Glue avoids intermediate conversions, making it the most efficient choice for this task.
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 Amazon Athena to convert the data to JSON format and store it in S3.
Why it's wrong here
Athena is a query service, not a data transformation service.
✓
Use AWS Glue DynamicFrame to repartition the data and write it as Parquet.
Why this is correct
DynamicFrame supports efficient partitioning and columnar format conversion.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use AWS Glue to convert the data to Apache Hive format.
Why it's wrong here
Hive format is not a standard file format; Parquet is columnar and efficient.
✗
Use Apache Spark DataFrame to write the data as CSV with Snappy compression.
Why it's wrong here
CSV is not columnar and does not optimize query performance as well as Parquet.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'file format' with 'query engine' (e.g., Hive) or choose a format like JSON that is human-readable but inefficient for analytics, missing that Parquet is the industry standard for performance and cost in data lakes.
Detailed technical explanation
How to think about this question
Parquet uses columnar storage with techniques like dictionary encoding, run-length encoding, and bit packing, which can reduce storage by up to 75% compared to CSV for typical data. AWS Glue DynamicFrames leverage the underlying Apache Spark engine but add AWS-specific optimizations like automatic pushdown of predicates and partition pruning when writing to S3, which is critical for date-partitioned datasets. A real-world scenario where this matters is when querying billions of rows with Athena or Redshift Spectrum, where Parquet's min/max statistics per row group can skip entire files that don't match the query filter.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Data Preparation for Machine Learning — This question tests Data Preparation for Machine Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use AWS Glue DynamicFrame to repartition the data and write it as Parquet. — Option B is correct because AWS Glue DynamicFrames provide built-in optimizations for writing data in columnar formats like Parquet, which improves query performance through predicate pushdown and compression, and reduces storage costs by using efficient encoding. The DynamicFrame's `repartition()` method allows you to control the number of output files, and writing as Parquet directly from Glue avoids intermediate conversions, making it the most efficient choice for this task.
What should I do if I get this MLA-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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Question Discussion
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