MLA-C01 Data Preparation for Machine Learning Practice Question

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?

⚠ Common exam trap

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.

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

✓

The `age` feature type should be `Integral`, not `String`.

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.

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.
✗
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.

Go deeper

MLA-C01 Data Preparation for Machine Learning Practice Question

Exhibit

Why each option matters

Option-by-option breakdown

Related to this question