1. A company is developing an ML model to make loan approvals. The company must implement a solution to detect bias in the model. The company must also be able to explain the model’s predictions.
Which solution will meet these requirements?
A. Amazon SageMaker Clarify
B. Amazon SageMaker Data Wrangler
C. Amazon SageMaker Model Cards
D. AWS AI Service Cards
Answer
A
2. A company has developed a generative text summarization model by using Amazon Bedrock. The company will use Amazon Bedrock automatic model evaluation capabilities.
Which metric should the company use to evaluate the accuracy of the model?
A. Area Under the ROC Curve (AUC) score
B. F1 score
C. BERTScore
D. Real world knowledge (RWK) score
Answer
C
3. An AI practitioner wants to predict the classification of flowers based on petal length, petal width, sepal length, and sepal width.
Which algorithm meets these requirements?
A. K-nearest neighbors (k-NN)
B. K-mean
C. Autoregressive Integrated Moving Average (ARIMA)
D. Linear regression
Answer
A
4. A company is using custom models in Amazon Bedrock for a generative AI application. The company wants to use a company managed encryption key to encrypt the model artifacts that the model customization jobs create.
Which AWS service meets these requirements?
A. AWS Key Management Service (AWS KMS)
B. Amazon Inspector
C. Amazon Macie
D. AWS Secrets Manager
Answer
A
5. A company wants to use large language models (LLMs) to produce code from natural language code comments.
Which LLM feature meets these requirements?
A. Text summarization
B. Text generation
C. Text completion
D. Text classification
Answer
B
6. A company is introducing a mobile app that helps users learn foreign languages. The app makes text more coherent by calling a large language model (LLM). The company collected a diverse dataset of text and supplemented the dataset with examples of more readable versions. The company wants the LLM output to resemble the provided examples.
Which metric should the company use to assess whether the LLM meets these requirements?
A. Value of the loss function
B. Semantic robustness
C. Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score
D. Latency of the text generation
Answer
C
7. A company notices that its foundation model (FM) generates images that are unrelated to the prompts. The company wants to modify the prompt techniques to decrease unrelated images.
Which solution meets these requirements?
A. Use zero-shot prompts.
B. Use negative prompts.
C. Use positive prompts.
D. Use ambiguous prompts.
Answer
B
8. A company wants to use a large language model (LLM) to generate concise, feature-specific descriptions for the company’s products.
Which prompt engineering technique meets these requirements?
A. Create one prompt that covers all products. Edit the responses to make the responses more specific, concise, and tailored to each product.
B. Create prompts for each product category that highlight the key features. Include the desired output format and length for each prompt response.
C. Include a diverse range of product features in each prompt to generate creative and unique descriptions.
D. Provide detailed, product-specific prompts to ensure precise and customized descriptions.
Answer
B
9. A company is developing an ML model to predict customer churn. The model performs well on the training dataset but does not accurately predict churn for new data.
Which solution will resolve this issue?
A. Decrease the regularization parameter to increase model complexity.
B. Increase the regularization parameter to decrease model complexity.
C. Add more features to the input data.
D. Train the model for more epochs.
Answer
B
10. A financial institution is building an AI solution to make loan approval decisions by using a foundation model (FM). For security and audit purposes, the company needs the AI solution’s decisions to be explainable.
Which factor relates to the explainability of the AI solution’s decisions?
A. Model complexity
B. Training time
C. Number of hyperparameters
D. Deployment time
Answer
A