A company uses an Amazon Bedrock foundation model (FM) to summarize documents for an internal use case. The company trained a custom model in Amazon Bedrock to improve the quality of the model's summarizations. The company needs a solution to use the customized model on Amazon Bedrock.
Which solution will meet this requirement?
A. Purchase Provisioned Throughput for the custom model.
B. Deploy the custom model in an Amazon SageMaker AI endpoint for real-time inference.
C. Register the model with the Amazon SageMaker Model Registry.
D. Update the approval status of the model version to Approved.
Show Answer
Correct Answer: A
Explanation: Amazon Bedrock custom models require Provisioned Throughput to be purchased before they can be invoked for inference. SageMaker endpoints and Model Registry are not required to deploy or use a Bedrock-customized model, and changing a model approval status is a SageMaker Model Registry concept rather than a Bedrock deployment requirement.
Question 85
Which term is the speed at which a pre-trained foundation model (FM) processes requests and delivers output?
A. Model size
B. Inference latency
C. Context window
D. Fine-tuning
Show Answer
Correct Answer: B
Explanation: Inference latency is the time or speed with which a deployed pre-trained foundation model processes an input request and returns an output. Model size refers to the number of parameters, context window is the maximum input/output token capacity, and fine-tuning is the process of adapting a model to a specific task.
Question 86
Which AI technique combines large language models (LLMs) with external knowledge bases to improve response accuracy?
A. Reinforcement learning (RL)
B. Natural language processing (NLP)
C. Retrieval Augmented Generation (RAG)
D. Transfer learning
Show Answer
Correct Answer: C
Explanation: Retrieval Augmented Generation (RAG) combines a large language model with an external knowledge base or document retrieval system. The model retrieves relevant information at inference time and uses it to generate more accurate, up-to-date, and grounded responses, reducing hallucinations.
Question 87
A company is building a custom AI solution in Amazon SageMaker Studio to analyze financial transactions for fraudulent activity in real time. The company needs to ensure that the connectivity from SageMaker Studio to Amazon Bedrock traverses the company’s VPC.
Which solution meets these requirements?
A. Configure AWS Identity and Access Management (IAM) roles and policies for SageMaker Studio to access Amazon Bedrock.
B. Configure Amazon Macie to proxy requests from SageMaker Studio to Amazon Bedrock.
C. Configure AWS PrivateLink endpoints for the Amazon Bedrock API endpoints in the VPC that SageMaker Studio is connected to.
D. Configure a new VPC for the Amazon Bedrock usage. Register the VPCs as peers.
Show Answer
Correct Answer: C
Explanation: AWS PrivateLink provides interface VPC endpoints for supported AWS services, including Amazon Bedrock, allowing SageMaker Studio resources in a VPC to access the Bedrock API over private network paths without traversing the public internet. IAM controls authorization but not network path, Macie is unrelated to proxying Bedrock requests, and VPC peering does not provide private access to a managed AWS service endpoint.
Question 88
A company has a team of AI practitioners that builds and maintains AI applications in an AWS account. The company must keep records of the actions that each AI practitioner takes in the AWS account for audit purposes.
Which AWS service will meet these requirements?
A. AWS CloudTrail
B. AWS Config
C. AWS Audit Manager
D. AWS Trusted Advisor
Show Answer
Correct Answer: A
Explanation: AWS CloudTrail records API activity and user actions in an AWS account, including who performed an action, when it occurred, and what resources were affected. It is the AWS service designed to provide an audit trail of account activity. AWS Config tracks resource configuration changes, AWS Audit Manager helps collect evidence for compliance assessments, and AWS Trusted Advisor provides best practice recommendations.
Question 89
A company is using AI to build a toy recommendation website that suggests toys based on a customer’s interests and age. The company notices that the AI tends to suggest stereotypically gendered toys.
Which AWS service or feature should the company use to investigate the bias?
A. Amazon Rekognition
B. Amazon Q Developer
C. Amazon Comprehend
D. Amazon SageMaker Clarify
Show Answer
Correct Answer: D
Explanation: Amazon SageMaker Clarify is designed to detect and investigate bias in datasets and machine learning models, and to help explain model predictions. It is the AWS service specifically intended for identifying issues such as gender bias in AI recommendations.
Question 90
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: Temperature controls randomness and creativity in generated text: increasing it generally produces more creative and varied outputs. Response length (maximum tokens) controls how long the generated text can be: decreasing it results in shorter outputs. Therefore, increasing the temperature while decreasing the response length best matches the requirement for creative, short marketing emails.
Question 91
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 that lets users discover, evaluate, and select from multiple foundation models (FMs) from Amazon and third-party providers for generative AI applications. Amazon Personalize is for recommendations, Amazon Q Developer is an AI coding assistant, and Amazon Rekognition is for image and video analysis.
Question 92
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: MLOps best practices emphasize the operational lifecycle of machine learning models, including deployment, monitoring, detecting drift, and maintaining model performance in production. Using a pre-trained open source model does not eliminate the need for continuous monitoring. The other options are model development tasks that are not universally required when deploying a pre-trained model.
Question 93
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: Conversion rate measures the percentage of users who complete a desired business action, such as making a purchase after receiving personalized recommendations. It is a direct metric for evaluating the AI solution's impact on sales revenue. Cross-domain performance, solution efficiency, and user satisfaction do not directly measure revenue impact.
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