- A
One-hot encoded feature vectors stored in CSV
Why wrong: One-hot encoding is not required; BlazingText uses embeddings.
- B
JSON lines with a 'text' and 'label' field
Why wrong: BlazingText supervised mode requires a specific text format, not JSON.
- C
Tokenized words separated by spaces, with text and labels combined in a single line (e.g., '__label__positive great product')
BlazingText expects this format for supervised learning.
- D
TFRecord files with sequence features
Why wrong: TFRecord is supported but the supervised mode has a simpler text format requirement.
Question 16 of 1,000
MLA-C01 Data Preparation for Machine Learning Practice Question
This MLA-C01 practice question tests your understanding of data preparation for machine learning. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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.
A team is building a machine learning model for natural language processing using SageMaker BlazingText. The data preparation step must format the training data correctly. What format does BlazingText require for supervised text classification?
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
✓
Tokenized words separated by spaces, with text and labels combined in a single line (e.g., '__label__positive great product')
BlazingText for supervised text classification expects the training data in a specific format where each line contains the text and its labels, with labels prefixed by '__label__'. This format allows BlazingText to efficiently parse and process the data for training the word2vec or classification model without additional preprocessing. Option C correctly describes this format, where the label and text are space-separated on a single line.
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.
- ✗
One-hot encoded feature vectors stored in CSV
Why it's wrong here
One-hot encoding is not required; BlazingText uses embeddings.
- ✗
JSON lines with a 'text' and 'label' field
Why it's wrong here
BlazingText supervised mode requires a specific text format, not JSON.
- ✓
Tokenized words separated by spaces, with text and labels combined in a single line (e.g., '__label__positive great product')
Why this is correct
BlazingText expects this format for supervised learning.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
TFRecord files with sequence features
Why it's wrong here
TFRecord is supported but the supervised mode has a simpler text format requirement.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse the JSON lines format (used by other SageMaker algorithms like BlazingText for Word2Vec or built-in Text Classification) with the specific '__label__' prefix format required for BlazingText's supervised text classification, leading them to select option B.
Detailed technical explanation
How to think about this question
BlazingText uses a modified version of the word2vec architecture (skip-gram and CBOW) and extends it to supervised text classification by treating each line as a document with labels. The '__label__' prefix is a convention inherited from fastText, allowing the model to distinguish labels from words during training. In practice, for multi-label classification, multiple labels can be included on the same line (e.g., '__label__positive __label__urgent great product'), and BlazingText will learn to predict all applicable labels.
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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Data Preparation for Machine Learning — study guide chapter
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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: Tokenized words separated by spaces, with text and labels combined in a single line (e.g., '__label__positive great product') — BlazingText for supervised text classification expects the training data in a specific format where each line contains the text and its labels, with labels prefixed by '__label__'. This format allows BlazingText to efficiently parse and process the data for training the word2vec or classification model without additional preprocessing. Option C correctly describes this format, where the label and text are space-separated on a single line.
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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