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AIF-C01 Practice Test Pdf | Valid AIF-C01 Practice Questions

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Amazon AWS Certified AI Practitioner Sample Questions (Q15-Q20):

NEW QUESTION # 15
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?

A. Decision trees
B. Linear regression
C. Neural networks
D. Logistic regression

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 Reference:
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.

NEW QUESTION # 16
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteri a. The company wants to build and use an AI model responsibly to minimize bias that could negatively affect some customers.
Which actions should the company take to meet these requirements? (Select TWO.)

A. Evaluate the model's behavior so that the company can provide transparency to stakeholders.
B. Detect imbalances or disparities in the data.
C. Ensure that the model runs frequently.
D. Ensure that the model's inference time is within the accepted limits.
E. Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.

Answer: A,B

Explanation:
To build an AI model responsibly and minimize bias, it is essential to ensure fairness and transparency throughout the model development and deployment process. This involves detecting and mitigating data imbalances and thoroughly evaluating the model's behavior to understand its impact on different groups.
Option A (Correct): "Detect imbalances or disparities in the data": This is correct because identifying and addressing data imbalances or disparities is a critical step in reducing bias. AWS provides tools like Amazon SageMaker Clarify to detect bias during data preprocessing and model training.
Option C (Correct): "Evaluate the model's behavior so that the company can provide transparency to stakeholders": This is correct because evaluating the model's behavior for fairness and accuracy is key to ensuring that stakeholders understand how the model makes decisions. Transparency is a crucial aspect of responsible AI.
Option B: "Ensure that the model runs frequently" is incorrect because the frequency of model runs does not address bias.
Option D: "Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate" is incorrect because ROUGE is a metric for evaluating the quality of text summarization models, not for minimizing bias.
Option E: "Ensure that the model's inference time is within the accepted limits" is incorrect as it relates to performance, not bias reduction.
AWS AI Practitioner Reference:
Amazon SageMaker Clarify: AWS offers tools such as SageMaker Clarify for detecting bias in datasets and models, and for understanding model behavior to ensure fairness and transparency.
Responsible AI Practices: AWS promotes responsible AI by advocating for fairness, transparency, and inclusivity in model development and deployment.

NEW QUESTION # 17
A company wants to identify harmful language in the comments section of social media posts by using an ML model. The company will not use labeled data to train the model. Which strategy should the company use to identify harmful language?

A. Use Amazon Comprehend toxicity detection.
B. Use Amazon Rekognition moderation.
C. Use Amazon Polly to monitor comments.
D. Use Amazon SageMaker AI built-in algorithms to train the model.

Answer: A

Explanation:
Amazon Comprehend toxicity detection is a managed NLP service that can analyze text for harmful or toxic language using pre-trained models-no need for labeled data or custom training.
* B is correct: Comprehend's toxicity detection API is designed for this use case, works out-of-the-box, and requires no data labeling or model training.
* A (Rekognition) is for image and video content moderation.
* C would require labeled data for training.
* D (Polly) is for text-to-speech, not content moderation.
"Amazon Comprehend can detect toxicity in text with pre-trained models, requiring no labeled training data." (Reference: Amazon Comprehend Toxicity Detection, AWS AI Practitioner Official Guide)

NEW QUESTION # 18
A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications.
Which factor will drive the inference costs?

A. Total training time
B. Temperature value
C. Amount of data used to train the LLM
D. Number of tokens consumed

Answer: D

NEW QUESTION # 19
A company has installed a security camer
a. The company uses an ML model to evaluate the security camera footage for potential thefts. The company has discovered that the model disproportionately flags people who are members of a specific ethnic group.
Which type of bias is affecting the model output?

A. Observer bias
B. Sampling bias
C. Measurement bias
D. Confirmation bias

Answer: B

NEW QUESTION # 20
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