AIF-C01 Meets Practice Questions
The free AIF-C01: AWS Certified AI Practitioner questions that deal with meets, with answers and explanations. The full bank and the timed practice test cover every topic the exam asks about.
Question #5
A company has documents that are missing some words because of a database error. The company wants to build an ML model that can suggest potential words to fill in the missing text. Which type of model meets this requirement?
Correct answer: D
Explanation
BERT-based models are trained with a masked language modeling objective and use bidirectional context to predict missing words, which is exactly the fill-in-the-blank task described.
Question #8
A company wants to classify human genes into 20 categories based on gene characteristics. The company needs an ML algorithm to document how the inner mechanism of the model affects the output. Which ML algorithm meets these requirements?
Correct answer: A
Explanation
Decision trees are an interpretable machine learning algorithm that clearly documents the decision-making process by showing how each input feature affects the output. This transparency is particularly useful when explaining how the model arrives at a certain decision, making it suitable for classifying genes into categories. • Option A (Correct): "Decision trees": This is the correct answer because decision trees provide a clear and interpretable representation of how input features influence the model's output, making it ideal for understanding the inner mechanisms affecting predictions. • Option B: "Linear regression" is incorrect because it is used for regression tasks, not classification. • Option C: "Logistic regression" is incorrect as it does not provide the same level of interpretability in documenting decision-making processes. • Option D: "Neural networks" is incorrect because they are often considered "black boxes" and do not easily explain how they arrive at their outputs. AWS AI Practitioner References: • Interpretable Machine Learning Models on AWS: AWS supports using interpretable models, such as decision trees, for tasks that require clear documentation of how input data affects output decisions.
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