A company stores its AI datasets in Amazon S3 buckets. The company wants to share the S3 buckets with its business partners. The company needs to avoid accidentally sharing sensitive data.
Which AWS service should the company use to discover sensitive data in the dataset?
A. Amazon Kendra
B. Amazon Macie
C. Amazon Textract
D. AWS Data Exchange
Show Answer
Correct Answer: B
Explanation: Amazon Macie is the AWS service that uses machine learning and pattern matching to discover, classify, and help protect sensitive data stored in Amazon S3. It is specifically designed to identify sensitive information before data is shared. Amazon Kendra is for enterprise search, Amazon Textract extracts text from documents, and AWS Data Exchange is for subscribing to and publishing datasets.
Question 74
A company has deployed an AI application in production on AWS. The application’s responses have become less accurate over time.
The company needs a solution to send alerts when the application performance drifts.
Which AWS service or feature will meet this requirement?
A. Amazon Augmented AI (Amazon A2I)
B. Amazon SageMaker Model Monitor
C. Amazon Rekognition
D. AWS Trusted Advisor
Show Answer
Correct Answer: B
Explanation: Amazon SageMaker Model Monitor is designed to monitor models deployed in production, detect data quality and model performance drift, and integrate with Amazon CloudWatch to generate alerts when drift or degradation is detected. Amazon A2I provides human review workflows, Amazon Rekognition is a prebuilt AI service, and AWS Trusted Advisor does not monitor ML model performance.
Question 75
An education company wants to build a private tutor application. The application will give users the ability to enter text or provide a picture of a question. The application will respond with a written answer and an explanation of the written answer.
Which model type meets these requirements?
A. Computer vision model
B. Multimodal LLM
C. Diffusion model
D. Text-to-speech model
Show Answer
Correct Answer: B
Explanation: A multimodal large language model can accept both text and image inputs and generate written answers with explanations. A computer vision model primarily analyzes images, a diffusion model generates media such as images, and a text-to-speech model converts text into audio.
Question 76
A company trains image and text generation models on Amazon SageMaker AI. The company releases the models by using Amazon Bedrock. The company must retain a tamper-proof, queryable record of every API call from SageMaker AI, Amazon Bedrock, and AWS Identity and Access Management (IAM).
Which AWS service will meet these requirements?
A. AWS Trusted Advisor
B. Amazon Macie
C. AWS CloudTrail Lake
D. Amazon Inspector
Show Answer
Correct Answer: C
Explanation: AWS CloudTrail Lake provides immutable, queryable event data stores for AWS API activity, including calls from Amazon SageMaker AI, Amazon Bedrock, and IAM. It is designed for long-term retention, auditing, compliance, and SQL-based querying of CloudTrail events.
Question 77
HOTSPOT
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A company wants to customize a foundation model (FM). The company wants to understand the customization methods and data types that are available.
Select the correct customization method from the following list for each description. Select each customization method one time.
Show Answer
Correct Answer: Fine-tuning
Continued pre-training
Distillation
Explanation: Fine-tuning uses labeled data for task-specific improvement. Continued pre-training uses unlabeled domain data to adapt the foundation model. Distillation transfers knowledge from a larger teacher model to a smaller student model.
Question 78
A company wants to increase employee productivity by using a generative AI solution to write code to test software applications.
Which solution will meet these requirements with the LEAST operational effort?
A. Amazon Q Business
B. Amazon Bedrock Agents
C. Amazon Q Developer
D. Amazon SageMaker Clarify
Show Answer
Correct Answer: C
Explanation: Amazon Q Developer is designed to help developers generate, explain, and transform code, including creating tests and improving software development productivity with minimal operational effort. Amazon Q Business is for enterprise knowledge assistants, Bedrock Agents orchestrate workflows rather than provide coding assistance, and SageMaker Clarify is for ML bias and explainability.
Question 79
A company wants to use large language models (LLMs) to create a chatbot. The chatbot will assist customers with product inquiries, order tracking, and returns. The chatbot must be able to process text inputs and image inputs to generate responses.
Which AWS service meets these requirements?
A. Amazon Bedrock
B. Amazon Comprehend
C. Amazon Q
D. Amazon Rekognition
Show Answer
Correct Answer: A
Explanation: Amazon Bedrock is the AWS service for building generative AI applications with foundation models, including multimodal models that can accept both text and image inputs and generate conversational responses. Amazon Comprehend is for NLP analysis, Amazon Q is an AI assistant/productivity service, and Amazon Rekognition focuses on image and video analysis rather than multimodal chatbot generation.
Question 80
A company is building a generative AI application to help customers make travel reservations. The application will process customer requests and invoke the appropriate API calls to complete reservation transactions.
Which Amazon Bedrock resource will meet these requirements?
A. Agents
B. Intelligent prompt routing
C. Knowledge Bases
D. Guardrails
Show Answer
Correct Answer: A
Explanation: Amazon Bedrock Agents are designed to interpret user requests, orchestrate multi-step workflows, and invoke external APIs or AWS Lambda-backed actions to complete tasks such as making travel reservations. Intelligent prompt routing selects models, Knowledge Bases provide retrieval over enterprise data, and Guardrails enforce safety and policy controls rather than executing transactions.
Question 81
A company is using Amazon SageMaker AI to develop AI/ML solutions. The company must use only approved data for model training. The AI/ML solutions must comply with company policy and ethical guidelines.
Which solution will meet these requirements?
A. Amazon SageMaker Catalog
B. Amazon SageMaker Clarify
C. Amazon SageMaker Model Registry
D. Amazon SageMaker Model Cards
Show Answer
Correct Answer: A
Explanation: Amazon SageMaker Catalog is designed for data discovery, governance, and access control, helping organizations ensure that only approved and governed datasets are used for model training. This directly satisfies the requirement to use approved training data while supporting compliance with organizational policies. SageMaker Clarify focuses on bias and explainability, Model Registry manages model versions and approvals after training, and Model Cards document model details and governance but do not control which datasets are used for training.
Question 82
An ecommerce company is developing an AI application that categorizes product images and extracts specifications. The application will use a high-quality labeled dataset to customize a foundation model (FM) to generate accurate responses.
Which ML technique will meet these requirements by using Amazon Bedrock?
A. Apply continued pre-training
B. Create an agent
C. Perform fine-tuning
D. Develop prompt engineering
Show Answer
Correct Answer: C
Explanation: Fine-tuning uses labeled input-output examples to adapt a foundation model to a specific task, such as classifying product images and extracting specifications, improving task-specific accuracy. Continued pre-training is for domain adaptation with unlabeled text, agents orchestrate workflows, and prompt engineering does not update model weights.
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