- A
Use AWS Glue to group records by session_id and aggregate event_types into a list per session. Then apply a mapping function to standardize event_type names.
Why wrong: Grouping by session loses the sequential order of events, which is critical for sequence-based models.
- B
Use AWS Glue to drop exact duplicate rows (all columns identical). Then apply a mapping function to standardize event_type to a controlled vocabulary (e.g., 'click', 'add_to_cart', 'purchase').
Deduplication removes redundant records, and mapping standardizes event_type, both essential for clean sequence data.
- C
Use AWS Glue to drop duplicate records based on all columns. Then drop the event_type column and use only numeric features for training.
Why wrong: Dropping event_type removes the core interaction type, which is essential for recommendation.
- D
Use AWS Glue to impute event_type with the mode for records with inconsistent values. Then drop duplicate records based on user_id, product_id, and timestamp.
Why wrong: Mode imputation on event_type is not appropriate because it would assign a potentially incorrect event type to many records.
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
An e-commerce company is building a recommendation system using user interaction data stored in Amazon DynamoDB. The data includes user_id, product_id, timestamp, event_type (click, add_to_cart, purchase), and session_id. The data science team exports the data to Amazon S3 as JSON files. During preprocessing, they discover that the 'event_type' field contains inconsistent values due to logging errors: 'Click', 'click', 'CLICK', and 'clck' all appear. Also, there are duplicate records where the same user_id, product_id, and timestamp appear multiple times with the same event_type. The team wants to use AWS Glue to clean the data for training a sequence-based recommendation model. Which set of actions should they perform?
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 AWS Glue to drop exact duplicate rows (all columns identical). Then apply a mapping function to standardize event_type to a controlled vocabulary (e.g., 'click', 'add_to_cart', 'purchase').
Option B is correct because it addresses both data quality issues: first, dropping exact duplicate rows (all columns identical) removes redundant records that would bias the sequence model; second, standardizing event_type to a controlled vocabulary ensures consistent categorical input for ML training. AWS Glue's DynamicFrame with DropDuplicates and Map transformations are the appropriate tools for this ETL task.
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 AWS Glue to group records by session_id and aggregate event_types into a list per session. Then apply a mapping function to standardize event_type names.
Why it's wrong here
Grouping by session loses the sequential order of events, which is critical for sequence-based models.
- ✓
Use AWS Glue to drop exact duplicate rows (all columns identical). Then apply a mapping function to standardize event_type to a controlled vocabulary (e.g., 'click', 'add_to_cart', 'purchase').
Why this is correct
Deduplication removes redundant records, and mapping standardizes event_type, both essential for clean sequence data.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use AWS Glue to drop duplicate records based on all columns. Then drop the event_type column and use only numeric features for training.
Why it's wrong here
Dropping event_type removes the core interaction type, which is essential for recommendation.
- ✗
Use AWS Glue to impute event_type with the mode for records with inconsistent values. Then drop duplicate records based on user_id, product_id, and timestamp.
Why it's wrong here
Mode imputation on event_type is not appropriate because it would assign a potentially incorrect event type to many records.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may think grouping by session_id is necessary for sequence modeling, but the question asks for cleaning steps, not feature engineering—duplicate removal and standardization must come first to avoid propagating errors into the sequence aggregation.
Detailed technical explanation
How to think about this question
Under the hood, AWS Glue uses Apache Spark's DataFrame API; DropDuplicates with no column list defaults to all columns, which is equivalent to SQL's DISTINCT. The Map transformation in Glue (via ApplyMapping or a custom Python UDF) allows regex-based normalization of event_type strings, which is essential because inconsistent casing and typos like 'clck' would otherwise create sparsely populated categories in the ML model's embedding layer. For sequence models, preserving the exact timestamp order per session is critical, so aggregation should be done after cleaning, not before.
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.
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 AWS Glue to drop exact duplicate rows (all columns identical). Then apply a mapping function to standardize event_type to a controlled vocabulary (e.g., 'click', 'add_to_cart', 'purchase'). — Option B is correct because it addresses both data quality issues: first, dropping exact duplicate rows (all columns identical) removes redundant records that would bias the sequence model; second, standardizing event_type to a controlled vocabulary ensures consistent categorical input for ML training. AWS Glue's DynamicFrame with DropDuplicates and Map transformations are the appropriate tools for this ETL task.
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
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