hardMultiple SelectObjective-mapped
MLA-C01 Practice Question: A data scientist is building a text…
A data scientist is building a text classification model using Amazon SageMaker. The dataset is stored as a CSV file in Amazon S3. The scientist wants to use the SageMaker built-in BlazingText algorithm. Which of the following steps are required to prepare the data for training? (Choose TWO.)
⚠ Common exam trap
A common mix-up: candidates assume general NLP preprocessing (like tokenization or stop word removal) is always required, but BlazingText is designed to handle raw text and expects a specific line format, not preprocessed vectors.
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
✓
Convert the CSV file to the format of a single file with one instance per line.
BlazingText expects input data in a single file where each line represents one training instance. This is a specific requirement of the algorithm's input format, not a general SageMaker practice. The CSV file must be converted to this line-per-instance format for BlazingText to process it correctly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Convert the text to one-hot encoded vectors.
Why it's wrong here
Not required for BlazingText.
- ✗
Tokenize and remove stop words from the text.
Why it's wrong here
BlazingText can handle raw text.
- ✓
Convert the CSV file to the format of a single file with one instance per line.
Why this is correct
BlazingText expects a single file with one instance per line.
- ✗
Upload the data to an Amazon SageMaker notebook instance.
Why it's wrong here
Data can stay in S3.
- ✓
Ensure each line in the training file contains a single text instance with the label prefixed by '__label__'.
Why this is correct
Required format for BlazingText.
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 |
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