The answer is to run MSCK REPAIR TABLE to load existing partitions into the metastore, store data in a columnar format like Parquet, and ensure the S3 data is organized with Hive-style partitioning. These three actions are necessary for efficient Amazon Athena queries on partitioned data because they directly reduce the amount of data scanned and improve query performance. MSCK REPAIR TABLE synchronizes the Glue Data Catalog with new partitions in S3, preventing Athena from missing data, while Parquet’s columnar storage allows Athena to read only the required columns, minimizing I/O and enabling predicate pushdown for faster filtering. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this tests your understanding of Athena’s cost-optimization and metadata management—a common trap is forgetting to run MSCK REPAIR after adding new partitions, leading to incomplete results. Remember the mnemonic: “Partition, Parquet, Repair” to recall the three pillars of efficient Athena queries.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 data engineer is using Amazon Athena to query a partitioned dataset stored in S3. Which THREE actions are necessary to ensure the queries can access the data and run efficiently?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Store the underlying data in a columnar format like Parquet
Storing data in a columnar format like Parquet reduces the amount of data scanned by Athena because it reads only the columns required by the query, not entire rows. This directly lowers query cost and improves performance, especially on large datasets, as Parquet also supports compression and predicate pushdown.
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.
✓
Store the underlying data in a columnar format like Parquet
Why this is correct
Columnar storage improves scan efficiency.
Related concept
Read the scenario before looking for a memorised answer.
✗
Create an AWS Glue DataBrew recipe to transform the data
Why it's wrong here
DataBrew is not necessary for Athena queries.
✗
Add each partition manually using ALTER TABLE ADD PARTITION
Why it's wrong here
Manual addition is not scalable for many partitions.
✓
Enable partition projection on the table for automated partition management
Why this is correct
Partition projection reduces need for manual partition maintenance.
Related concept
Read the scenario before looking for a memorised answer.
✓
Run MSCK REPAIR TABLE to load existing partitions into the metastore
Why this is correct
This command adds partitions to the table's metadata.
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 confuse data preparation tools (DataBrew) with query optimization techniques, or they assume manual partition management (ALTER TABLE ADD PARTITION) is required when automated methods like MSCK REPAIR TABLE or partition projection are the correct and efficient approaches for Athena.
Detailed technical explanation
How to think about this question
Parquet uses a columnar storage layout that stores data by column rather than by row, enabling Athena to skip entire columns not referenced in a query via predicate pushdown and column pruning. Partition projection (Option D) eliminates the need for metadata scans by defining partition patterns in the table definition, allowing Athena to compute partition locations at query time without calling the Glue Data Catalog, which dramatically reduces latency for high-cardinality partitions. MSCK REPAIR TABLE (Option E) scans the S3 location for new partitions matching the table's partition schema and adds them to the Glue Data Catalog, but it can be slow for thousands of partitions; partition projection is preferred for dynamic or large-scale datasets.
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: Store the underlying data in a columnar format like Parquet — Storing data in a columnar format like Parquet reduces the amount of data scanned by Athena because it reads only the columns required by the query, not entire rows. This directly lowers query cost and improves performance, especially on large datasets, as Parquet also supports compression and predicate pushdown.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data engineer is optimizing Amazon Athena queries on large datasets stored in S3 for machine learning data preparation. Which THREE practices improve query performance?
hard
✓ A.Partition the data by a frequently filtered column, such as date
B.Use uncompressed CSV files for simplicity
C.Partition the data by every column to maximize filtering
✓ D.Store data in columnar formats like Parquet or ORC
✓ E.Compress the data with Snappy or gzip
Why A: Partitioning by a frequently filtered column, such as date, allows Athena to use partition pruning. When a query includes a filter on the partition column, Athena can skip entire directories of data in S3, drastically reducing the amount of data scanned and improving query performance while also lowering cost.
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.