- A
Use Amazon EMR with Spark and store the processed data in HDFS.
Why wrong: EMR requires cluster management and is more complex; storing in HDFS is less durable than S3.
- B
Use SageMaker Processing with a Spark container to distribute the encoding job.
SageMaker Processing with Spark provides distributed processing and is cost-effective for large datasets.
- C
Use a SageMaker notebook instance with scikit-learn to perform the encoding in memory.
Why wrong: A single notebook instance may run out of memory with 100M rows.
- D
Use AWS Glue ETL jobs to perform the encoding and store the result in S3.
Why wrong: Glue is serverless but can be more expensive than SageMaker Processing for this workload.
Question 858 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. 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 company is building a recommendation system using Amazon SageMaker. The training data includes user-item interactions stored in a DataFrame with over 100 million rows. The data scientist wants to perform feature engineering, including one-hot encoding of categorical features with high cardinality. Which approach is MOST cost-effective and scalable?
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 SageMaker Processing with a Spark container to distribute the encoding job.
Option B is correct because SageMaker Processing with a Spark container allows distributed execution of one-hot encoding on high-cardinality categorical features across a managed cluster, scaling horizontally to handle over 100 million rows without manual infrastructure management. This approach is cost-effective as you pay only for the processing time, and it integrates natively with SageMaker for seamless data pipeline orchestration.
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 and store the processed data in HDFS.
Why it's wrong here
EMR requires cluster management and is more complex; storing in HDFS is less durable than S3.
- ✓
Use SageMaker Processing with a Spark container to distribute the encoding job.
Why this is correct
SageMaker Processing with Spark provides distributed processing and is cost-effective for large datasets.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use a SageMaker notebook instance with scikit-learn to perform the encoding in memory.
Why it's wrong here
A single notebook instance may run out of memory with 100M rows.
- ✗
Use AWS Glue ETL jobs to perform the encoding and store the result in S3.
Why it's wrong here
Glue is serverless but can be more expensive than SageMaker Processing for this workload.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often choose AWS Glue (Option D) assuming it is the most scalable serverless option, but SageMaker Processing with Spark is more cost-effective and purpose-built for ML feature engineering within the SageMaker ecosystem, avoiding Glue's higher per-DPU costs and slower job startup times for large datasets.
Detailed technical explanation
How to think about this question
SageMaker Processing with a Spark container leverages Apache Spark's distributed DataFrame operations to partition the 100 million rows across multiple workers, performing one-hot encoding via `StringIndexer` and `OneHotEncoderEstimator` in parallel, which avoids memory bottlenecks. The processed data is written directly to S3, enabling cost-effective storage and easy access for downstream SageMaker training jobs, while the Spark container automatically handles shuffle operations and broadcast joins for high-cardinality features.
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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use SageMaker Processing with a Spark container to distribute the encoding job. — Option B is correct because SageMaker Processing with a Spark container allows distributed execution of one-hot encoding on high-cardinality categorical features across a managed cluster, scaling horizontally to handle over 100 million rows without manual infrastructure management. This approach is cost-effective as you pay only for the processing time, and it integrates natively with SageMaker for seamless data pipeline orchestration.
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.
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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