The answer is to use Amazon EMR with Spark to convert the data to Parquet and store it in S3, employing spot instances for task nodes. This approach minimizes both processing time and cost because converting gzip-compressed CSV to Parquet leverages columnar storage and predicate pushdown, drastically reducing scan volume and improving query performance, while spot instances for task nodes cut compute costs by up to 90% without affecting reliability for fault-tolerant Spark jobs. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of cost-optimized data pipeline design for large-scale log processing, often appearing as a trap where candidates choose expensive on-demand instances or fail to recognize Parquet’s compression and efficiency advantages over CSV. Remember the key pairing: Parquet for performance, spot instances for price—think “Parquet and Spot, cost you not.”
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. 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 team is building a data pipeline to process terabytes of log data daily using Amazon EMR. The data arrives in 5-minute windows and must be available for querying within 30 minutes. The data is originally in gzip-compressed CSV files. Which approach will minimize processing time and cost?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "minimum / minimize"
Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Amazon EMR with Spark to convert data to Parquet and store in S3, using spot instances for task nodes.
Option B is correct because converting gzip-compressed CSV to Parquet reduces storage size and improves query performance due to columnar storage and predicate pushdown. Using spot instances for task nodes significantly lowers compute cost, while the 30-minute SLA is achievable with Spark on EMR processing 5-minute windows of data.
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 EMR with Spark to convert data to Parquet and use on-demand instances.
Why it's wrong here
On-demand instances are more expensive.
✓
Use Amazon EMR with Spark to convert data to Parquet and store in S3, using spot instances for task nodes.
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use AWS Glue to convert data to gzip-compressed CSV and query with Athena.
Why it's wrong here
CSV is not optimal for Athena performance.
✗
Use Amazon EMR with Hive to transform data to compressed CSV and store in S3.
Why it's wrong here
CSV still incurs full scan costs.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may overlook the cost savings of spot instances for transient, fault-tolerant workloads, or assume that any compression (like gzip CSV) is sufficient for performance, ignoring the benefits of columnar formats like Parquet for analytical queries.
Detailed technical explanation
How to think about this question
Parquet uses columnar storage with efficient compression (e.g., Snappy, Gzip) and predicate pushdown, allowing Athena or Spark to read only relevant columns, reducing I/O. Spot instances can be interrupted with a 2-minute warning, but Spark's RDD lineage and checkpointing enable fault-tolerant recovery, making them cost-effective for batch ETL jobs. The 30-minute SLA is feasible because Spark can process multiple 5-minute windows in parallel using micro-batch or streaming approaches.
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 Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use Amazon EMR with Spark to convert data to Parquet and store in S3, using spot instances for task nodes. — Option B is correct because converting gzip-compressed CSV to Parquet reduces storage size and improves query performance due to columnar storage and predicate pushdown. Using spot instances for task nodes significantly lowers compute cost, while the 30-minute SLA is achievable with Spark on EMR processing 5-minute windows of data.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 →
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 MLS-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 MLS-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.