HOTSPOT
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A company periodically updates its product database by manually uploading digital product guides. The product guides contain text and images. The company wants to automate this task by using generative AI.
Select and order the steps from the following list to automate the database update task by using generative AI. Select each step one time.
Show Answer
Correct Answer: Step 1: Upload the digital text and image files to an Amazon S3 bucket.
Step 2: Use Amazon Nova multimodal models to process the digital text and image files.
Step 3: Insert data into the product database.
Explanation: First, store the raw product guides in S3 so they are accessible. Next, use a multimodal model to extract and structure information from text and images. Finally, write the structured output into the product database.
Question 41
A company is building a generative AI application with a foundation model (FM). The application needs to automatically generate marketing emails. The company wants the application’s output text to be creative and short in length.
Which configuration of inference parameters will meet these requirements?
A. Decrease the temperature and the response length.
B. Increase the temperature and the response length.
C. Increase the temperature and decrease the response length.
D. Decrease the temperature and increase the response length.
Show Answer
Correct Answer: C
Explanation: Creativity in generated text is increased by raising the temperature, which encourages more diverse and less deterministic word choices. Keeping the output short requires decreasing the response length (maximum tokens). Therefore, increasing the temperature while decreasing the response length best meets the requirements.
Question 42
A company is building a new generative AI chatbot. The chatbot uses an Amazon Bedrock foundation model (FM) to generate responses. During testing, the company notices that the chatbot is prone to prompt injection attacks.
What can the company do to secure the chatbot with the LEAST implementation effort?
A. Fine-tune the FM to avoid harmful responses.
B. Use Amazon Bedrock Guardrails content filters and denied topics.
C. Change the FM to a more secure FM.
D. Use chain-of-thought prompting to produce secure responses.
Show Answer
Correct Answer: B
Explanation: Amazon Bedrock Guardrails provide built-in protection against prompt injection, jailbreaks, unsafe inputs, and unsafe outputs without retraining or changing the foundation model or application logic. This makes it the least-effort and fastest way to secure the chatbot.
Question 42
Which AWS service helps select foundation models (FMs) for generative AI use cases?
A. Amazon Personalize
B. Amazon Bedrock
C. Amazon Q Developer
D. Amazon Rekognition
Show Answer
Correct Answer: B
Explanation: Amazon Bedrock is the AWS service designed to help customers discover, select, and use foundation models (FMs) from multiple providers for generative AI use cases. The other options focus on specific applications (Personalize, Rekognition) or developer assistance (Amazon Q Developer), not FM selection.
Question 43
A company is exploring Amazon Nova models in Amazon Bedrock. The company needs a multimodal model that supports multiple languages.
Which Nova model will meet these requirements MOST cost-effectively?
A. Nova Lite
B. Nova Pro
C. Nova Canvas
D. Nova Reel
Show Answer
Correct Answer: A
Explanation: The requirement is for a multimodal model with multilingual support at the lowest cost. Nova Lite is designed to support text and image inputs, handles multiple languages, and is optimized for cost efficiency. Nova Pro is more capable but higher cost, while Nova Canvas and Nova Reel are specialized for image and video generation rather than general multimodal understanding.
Question 43
A company uses an open source pre-trained model to analyze user sentiment for a newly released product.
Which action must the company perform, according to MLOps best practices?
A. Use deep learning to perform hyperparameter tuning.
B. Collect user reviews and label each review as positive or negative.
C. Continuously monitor outputs in production.
D. Perform feature engineering on the input dataset.
Show Answer
Correct Answer: C
Explanation: When using an open-source pre-trained model, MLOps best practices emphasize monitoring the model in production to detect data drift, performance degradation, bias, and unexpected behavior over time. Retraining, labeling, or feature engineering are not strictly required if the pre-trained model is used as-is, but continuous monitoring is mandatory.
Question 44
A company is using Amazon Bedrock for a generative AI solution. The solution must integrate a service with vector database storage and vector search capabilities.
Which AWS service will meet these requirements?
A. Amazon DynamoDB
B. Amazon OpenSearch Service
C. Amazon ElastiCache
D. Amazon Redshift
Show Answer
Correct Answer: B
Explanation: Amazon OpenSearch Service natively supports vector database storage and vector similarity search (k-NN/ANN) required for generative AI and RAG workloads with Amazon Bedrock. The other options do not provide native vector search capabilities.
Question 44
A company uses Amazon Bedrock to implement a generative AI solution. The AI solution provides customers with personalized product recommendations.
The company wants to evaluate the impact of the AI solution on sales revenue.
Which metric will meet these requirements?
A. Cross-domain performance
B. Solution efficiency
C. User satisfaction
D. Conversion rate
Show Answer
Correct Answer: D
Explanation: To evaluate the impact of personalized product recommendations on sales revenue, the most relevant metric is conversion rate, which measures the percentage of users who make a purchase after interacting with the recommendations. An increase in conversion rate directly reflects improved sales performance attributable to the AI solution.
Question 45
A company is using Amazon SageMaker to deploy a model that identifies if social media posts contain certain topics. The company needs to show how different input features influence model behavior.
Which SageMaker feature meets these requirements?
A. SageMaker Canvas
B. SageMaker Clarify
C. SageMaker Feature Store
D. SageMaker Ground Truth
Show Answer
Correct Answer: B
Explanation: The requirement is to show how different input features influence model behavior, which is model explainability. Amazon SageMaker Clarify provides feature attribution methods (such as SHAP values), explainability reports, and bias detection to understand how input features affect predictions during training and inference.
Question 45
A company wants to fine-tune a foundation model (FM) for a specific use case. The company needs to deploy the FM on Amazon Bedrock for internal use.
Which solution will meet these requirements?
A. Run responses that have been generated by a pre-trained FM through Amazon Bedrock Guardrails to create the custom FM.
B. Use Amazon Personalize to customize the FM with custom data.
C. Use conversational builder for Amazon Bedrock Agents to create the custom model.
D. Use Amazon SageMaker AI to customize the FM. Then, import the trained model into Amazon Bedrock.
Show Answer
Correct Answer: D
Explanation: Fine-tuning or customizing a foundation model requires a full ML training environment. Amazon SageMaker AI provides tools for training and fine-tuning models with custom data. After customization, the trained model can be imported into Amazon Bedrock for managed deployment and internal use. The other options do not perform model fine-tuning.
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