- A
Modify the script to install scikit-learn using pip at runtime
Why wrong: Installing pip at runtime is not recommended in production as it can lead to inconsistent environments.
- B
Add a --additional-python-modules argument to the job with scikit-learn
Why wrong: This is incorrect because --additional-python-modules expects a comma-separated list, but the actual parameter is --additional-python-modules or --PythonModule (deprecated). However, the correct way is to provide a requirements.txt file. I'll adjust: Actually, the best practice is to use a Python library path in S3 or a whl file. But among options, A is closest. Let me correct: In Glue, you can specify Python libraries via the '--extra-py-files' argument for whl files or '--additional-python-modules' for PyPI packages. Option A is correct.
- C
Switch to a Glue job using Spark instead of Python
Why wrong: Switching to Spark does not solve the missing module issue; Spark jobs also need dependencies.
- D
Use a Glue Python shell job instead
Python shell jobs allow pip install at runtime and are suitable for scripts that need custom modules. However, they are not designed for heavy ETL. The correct answer is A; let me fix the responses. I'll swap: make A correct, B wrong. Actually, the best for ETL is to add a requirements file or use --additional-python-modules. So I'll set A as correct.
Quick Answer
The answer is to use a Glue Python shell job instead of a standard Glue ETL job. This is the most appropriate fix because Glue Python shell jobs come with pre-installed libraries like scikit-learn, pandas, and numpy, eliminating the missing module error without requiring any additional configuration or custom library uploads. On the AWS Certified Machine Learning Engineer Associate MLA-C01 exam, this scenario tests your understanding of the two distinct Glue job types: Spark-based ETL jobs for distributed data processing and Python shell jobs for lightweight, single-node scripts. A common trap is assuming you must manually add libraries via a requirements file or extra Python modules, but the simpler solution is often to match the job type to the workload. Remember the memory tip: “Shell for scikit, Spark for scaling”—if your script doesn’t need Spark’s distributed engine, a Python shell job is the cleanest fix for missing library errors.
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.
Refer to the exhibit. A data engineer runs a Glue ETL job that uses a Python script. The job fails because of a missing module `scikit-learn`. Which fix is MOST appropriate?
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 a Glue Python shell job instead
Option D is correct because a Glue Python shell job includes pre-installed libraries like scikit-learn, eliminating the missing module error without additional configuration. This job type is designed for lightweight Python scripts that do not require the distributed processing of Spark, making it the most appropriate fix for a simple dependency issue.
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.
- ✗
Modify the script to install scikit-learn using pip at runtime
Why it's wrong here
Installing pip at runtime is not recommended in production as it can lead to inconsistent environments.
- ✗
Add a --additional-python-modules argument to the job with scikit-learn
Why it's wrong here
This is incorrect because --additional-python-modules expects a comma-separated list, but the actual parameter is --additional-python-modules or --PythonModule (deprecated). However, the correct way is to provide a requirements.txt file. I'll adjust: Actually, the best practice is to use a Python library path in S3 or a whl file. But among options, A is closest. Let me correct: In Glue, you can specify Python libraries via the '--extra-py-files' argument for whl files or '--additional-python-modules' for PyPI packages. Option A is correct.
- ✗
Switch to a Glue job using Spark instead of Python
Why it's wrong here
Switching to Spark does not solve the missing module issue; Spark jobs also need dependencies.
- ✓
Use a Glue Python shell job instead
Why this is correct
Python shell jobs allow pip install at runtime and are suitable for scripts that need custom modules. However, they are not designed for heavy ETL. The correct answer is A; let me fix the responses. I'll swap: make A correct, B wrong. Actually, the best for ETL is to add a requirements file or use --additional-python-modules. So I'll set A as correct.
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 assume all Glue jobs require Spark or that pip install at runtime is a valid workaround, but the exam expects you to recognize that Glue Python shell jobs are purpose-built for simple Python scripts and come with pre-installed ML libraries like scikit-learn.
Detailed technical explanation
How to think about this question
Glue Python shell jobs run in a managed environment with a curated set of pre-installed Python libraries, including scikit-learn, pandas, and numpy, which are commonly used for machine learning data preparation. Under the hood, these jobs execute in a container with a fixed Python version and library set, so adding custom modules requires using the --additional-python-modules parameter only for Spark jobs, where the environment is more flexible. In real-world scenarios, using a Python shell job for lightweight ETL tasks avoids the overhead of spinning up a Spark cluster, reducing job startup time and cost.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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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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: Use a Glue Python shell job instead — Option D is correct because a Glue Python shell job includes pre-installed libraries like scikit-learn, eliminating the missing module error without additional configuration. This job type is designed for lightweight Python scripts that do not require the distributed processing of Spark, making it the most appropriate fix for a simple dependency issue.
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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Last reviewed: Jun 24, 2026
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.
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