- A
Use a SageMaker Spark container instead of scikit-learn
Why wrong: Switching to Spark may help, but increasing instance memory is a direct fix.
- B
Increase the instance memory size for the processing job
More memory allows larger datasets to be processed in memory.
- C
Write the output as Parquet instead of CSV
Why wrong: Changing output format does not reduce memory during processing.
- D
Standardize the features before loading into the DataFrame
Why wrong: Standardization order does not affect memory usage.
Question 589 of 1,000
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 machine learning engineer is using SageMaker Processing to run a scikit-learn preprocessing script. The script reads a CSV file from S3, applies a StandardScaler, and writes the output. The job fails with a 'MemoryError'. Which change should the engineer make to the data preparation process?
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
✓
Increase the instance memory size for the processing job
The MemoryError indicates that the processing job's instance does not have enough RAM to hold the dataset and the intermediate results of the StandardScaler (which computes mean and variance in memory). Increasing the instance memory size (Option B) directly resolves this by providing more RAM for the scikit-learn operations. SageMaker Processing jobs allow you to choose instances with larger memory, such as the r5 or r6i families, to accommodate larger datasets.
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 a SageMaker Spark container instead of scikit-learn
Why it's wrong here
Switching to Spark may help, but increasing instance memory is a direct fix.
- ✓
Increase the instance memory size for the processing job
Why this is correct
More memory allows larger datasets to be processed in memory.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Write the output as Parquet instead of CSV
Why it's wrong here
Changing output format does not reduce memory during processing.
- ✗
Standardize the features before loading into the DataFrame
Why it's wrong here
Standardization order does not affect memory usage.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse a memory error with a storage or format issue, leading them to choose Parquet (Option C) or Spark (Option A), when the actual fix is to allocate more RAM to the processing instance.
Trap categories for this question
Command / output trap
Changing output format does not reduce memory during processing.
Detailed technical explanation
How to think about this question
Under the hood, scikit-learn's StandardScaler computes `mean_` and `scale_` by calling `np.mean()` and `np.std()` on the entire feature matrix, which requires the full dataset to be loaded into memory as a NumPy array. SageMaker Processing runs the script in a single container on the chosen instance, so the instance's RAM must accommodate both the raw data and the transformed output plus any temporary copies. In real-world scenarios, memory errors often occur when processing datasets larger than ~70% of the instance's RAM due to pandas DataFrame overhead and scikit-learn's internal copies.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
Visual reference
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 |
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.
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Data Preparation for Machine Learning — study guide chapter
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FAQ
Questions learners often ask
What does this MLA-C01 question test?
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: Increase the instance memory size for the processing job — The MemoryError indicates that the processing job's instance does not have enough RAM to hold the dataset and the intermediate results of the StandardScaler (which computes mean and variance in memory). Increasing the instance memory size (Option B) directly resolves this by providing more RAM for the scikit-learn operations. SageMaker Processing jobs allow you to choose instances with larger memory, such as the r5 or r6i families, to accommodate larger datasets.
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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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 →
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Last reviewed: Jun 24, 2026
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