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.
Exhibit
Refer to the exhibit. A data scientist uses the following SageMaker Feature Store feature definition (using the Boto3 SDK) to create a feature group:
```python
import boto3
sagemaker = boto3.client('sagemaker', region_name='us-east-1')
response = sagemaker.create_feature_group(
FeatureGroupName='my-feature-group',
RecordIdentifierFeatureName='customer_id',
EventTimeFeatureName='timestamp',
FeatureDefinitions=[
{'FeatureName': 'customer_id', 'FeatureType': 'String'},
{'FeatureName': 'age', 'FeatureType': 'String'},
{'FeatureName': 'income', 'FeatureType': 'Fractional'}
],
OnlineStoreConfig={'EnableOnlineStore': True},
RoleArn='arn:aws:iam::123456789012:role/SageMakerRole'
)
```
The data scientist later tries to ingest data with an 'age' column containing integer values. The ingestion fails. What is the most likely reason?
A data scientist creates a feature group as shown in the exhibit. When ingesting data with an 'age' column of integer values, the ingestion fails. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Refer to the exhibit. A data scientist uses the following SageMaker Feature Store feature definition (using the Boto3 SDK) to create a feature group:
```python
import boto3
sagemaker = boto3.client('sagemaker', region_name='us-east-1')
response = sagemaker.create_feature_group(
FeatureGroupName='my-feature-group',
RecordIdentifierFeatureName='customer_id',
EventTimeFeatureName='timestamp',
FeatureDefinitions=[
{'FeatureName': 'customer_id', 'FeatureType': 'String'},
{'FeatureName': 'age', 'FeatureType': 'String'},
{'FeatureName': 'income', 'FeatureType': 'Fractional'}
],
OnlineStoreConfig={'EnableOnlineStore': True},
RoleArn='arn:aws:iam::123456789012:role/SageMakerRole'
)
```
The data scientist later tries to ingest data with an 'age' column containing integer values. The ingestion fails. What is the most likely reason?
A
The role does not have permissions to write to the feature store.
Why wrong: While possible, the type mismatch is the direct cause.
B
The `age` feature type should be `Integral`, not `String`.
The feature type must match the ingested data type.
C
The `OnlineStoreConfig` must include a `SecurityConfig`.
Why wrong: SecurityConfig is optional for online store.
D
The `EventTimeFeatureName` is incorrectly spelled.
Why wrong: Spelling error would cause creation failure, not ingestion.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
The `age` feature type should be `Integral`, not `String`.
Option B is correct because the feature group definition specifies the 'age' column as a `String` type, but the ingested data contains integer values. Amazon SageMaker Feature Store requires that the data types of ingested records match the schema defined in the feature group. When a mismatch occurs, such as providing an integer for a string field, the ingestion fails with a type conversion error.
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.
✗
The role does not have permissions to write to the feature store.
Why it's wrong here
While possible, the type mismatch is the direct cause.
✓
The `age` feature type should be `Integral`, not `String`.
Why this is correct
The feature type must match the ingested data type.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
The `OnlineStoreConfig` must include a `SecurityConfig`.
Why it's wrong here
SecurityConfig is optional for online store.
✗
The `EventTimeFeatureName` is incorrectly spelled.
Why it's wrong here
Spelling error would cause creation failure, not ingestion.
Common exam traps
Common exam trap: answer the scenario, not the keyword
AWS often tests the distinction between schema definition and actual data types, trapping candidates who overlook that the feature group schema must exactly match the ingested data's types, not just the column names.
Detailed technical explanation
How to think about this question
Under the hood, SageMaker Feature Store uses Apache Avro or Parquet for serialization, and the schema is enforced at the record level. When a feature is defined as `String`, the ingestion pipeline expects a UTF-8 encoded string; providing an integer triggers a schema validation failure before the record is written to the offline or online store. In a real-world scenario, this often happens when data pipelines inadvertently cast numeric features as strings or vice versa, especially when ingesting from CSV files where all columns are initially parsed as strings.
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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.
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 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: The `age` feature type should be `Integral`, not `String`. — Option B is correct because the feature group definition specifies the 'age' column as a `String` type, but the ingested data contains integer values. Amazon SageMaker Feature Store requires that the data types of ingested records match the schema defined in the feature group. When a mismatch occurs, such as providing an integer for a string field, the ingestion fails with a type conversion error.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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