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MLS-C01 Modeling Practice Question

A company is building a sentiment analysis model using Amazon SageMaker BlazingText. The training data consists of 100,000 product reviews. The data scientist wants to use the Word2Vec algorithm to generate word embeddings. Which configuration is required to use the continuous bag-of-words (CBOW) architecture?

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

✓

Set the mode parameter to 'cbow'.

In BlazingText, the 'mode' parameter controls the training objective. Setting 'mode' to 'cbow' enables the continuous bag-of-words architecture. 'skipgram' is for skip-gram. 'batch_skipgram' is for large-scale skip-gram. 'supervised' is for text classification.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • ✗

    Set the mode parameter to 'supervised'.

    Why it's wrong here

    'supervised' is used for text classification, not for learning embeddings.

  • ✗

    Set the mode parameter to 'batch_skipgram'.

    Why it's wrong here

    'batch_skipgram' is for distributed skip-gram training.

  • ✓

    Set the mode parameter to 'cbow'.

    Why this is correct

    The 'cbow' mode enables the continuous bag-of-words architecture in BlazingText.

  • ✗

    Set the mode parameter to 'skipgram'.

    Why it's wrong here

    'skipgram' is for skip-gram architecture, not CBOW.

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Written by Johnson Ajibi, MSc IT Security

Senior Network & Security Engineer · founder of Courseiva

This MLS-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 MLS-C01 exam.