MLA-C01 Data Preparation for Machine Learning Practice Question
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?
⚠ Common exam trap
It's easy for candidates to 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.
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.
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.
- ✗
TFRecord files with sequence features
Why it's wrong here
TFRecord files with sequence features are designed for TensorFlow-based models requiring serialised data with variable-length sequences, but BlazingText for supervised classification expects a plain-text file with one labelled sentence per line in the format `__label__<class> <text>`. This option tempts because TFRecord is a common high-performance format for deep learning pipelines, and would be correct for TensorFlow models such as BERT or LSTM classifiers that consume sequence features.
Go deeper
Related to this question
About these practice questions
This MLA-C01 question is part of Courseiva's 835-question bank — original exam-style content with full explanations and wrong-answer analysis, never real exam questions or exam dumps. Learn why practice questions differ from exam dumps →
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLA-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLA-C01 exam.