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NEW QUESTION # 52
A medical company is customizing a foundation model (FM) for diagnostic purposes. The company needs the model to be transparent and explainable to meet regulatory requirements.
Which solution will meet these requirements?
- A. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
- B. Gather more data. Use Amazon Rekognition to add custom labels to the data.
- C. Encrypt and secure training data by using Amazon Macie.
- D. Configure the security and compliance by using Amazon Inspector.
Answer: A
NEW QUESTION # 53
A company is developing a new model to predict the prices of specific items. The model performed well on the training dataset. When the company deployed the model to production, the model's performance decreased significantly.
What should the company do to mitigate this problem?
- A. Increase the volume of data that is used in training.
- B. Add hyperparameters to the model.
- C. Reduce the volume of data that is used in training.
- D. Increase the model training time.
Answer: A
NEW QUESTION # 54
A company uses a foundation model (FM) from Amazon Bedrock for an AI search tool. The company wants to fine-tune the model to be more accurate by using the company's data.
Which strategy will successfully fine-tune the model?
- A. Prepare the training dataset by creating a .txt file that contains multiple lines in .csv format.
- B. Purchase Provisioned Throughput for Amazon Bedrock.
- C. Provide labeled data with the prompt field and the completion field.
- D. Train the model on journals and textbooks.
Answer: C
NEW QUESTION # 55
A company is using an Amazon Bedrock base model to summarize documents for an internal use case. The company trained a custom model to improve the summarization quality.
Which action must the company take to use the custom model through Amazon Bedrock?
- A. Grant access to the custom model in Amazon Bedrock.
- B. Deploy the custom model in an Amazon SageMaker endpoint for real-time inference.
- C. Register the model with the Amazon SageMaker Model Registry.
- D. Purchase Provisioned Throughput for the custom model.
Answer: B
NEW QUESTION # 56
An e-commerce company wants to build a solution to determine customer sentiments based on written customer reviews of products.
Which AWS services meet these requirements? (Select TWO.)
- A. Amazon Polly
- B. Amazon Rekognition
- C. Amazon Bedrock
- D. Amazon Comprehend
- E. Amazon Lex
Answer: C,D
NEW QUESTION # 57
A company wants to create a chatbot by using a foundation model (FM) on Amazon Bedrock. The FM needs to access encrypted data that is stored in an Amazon S3 bucket.
The data is encrypted with Amazon S3 managed keys (SSE-S3).
The FM encounters a failure when attempting to access the S3 bucket data.
Which solution will meet these requirements?
- A. Use prompt engineering techniques to tell the model to look for information in Amazon S3.
- B. Set the access permissions for the S3 buckets to allow public access to enable access over the internet.
- C. Ensure that the S3 data does not contain sensitive information.
- D. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
Answer: D
NEW QUESTION # 58
What are tokens in the context of generative AI models?
- A. Tokens are the specific prompts or instructions given to a generative AI model to generate output.
- B. Tokens are the mathematical representations of words or concepts used in generative AI models.
- C. Tokens are the pre-trained weights of a generative AI model that are fine-tuned for specific tasks.
- D. Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units.
Answer: D
NEW QUESTION # 59
How can companies use large language models (LLMs) securely on Amazon Bedrock?
- A. Enable Amazon Bedrock automatic model evaluation jobs.
- B. Enable AWS Audit Manager for automatic model evaluation jobs.
- C. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.
- D. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.
Answer: C
NEW QUESTION # 60
A company needs to choose a model from Amazon Bedrock to use internally.
The company must identify a model that generates responses in a style that the company's employees prefer.
What should the company do to meet these requirements?
- A. Use public model leaderboards to identify the model.
- B. Evaluate the models by using a human workforce and custom prompt datasets.
- C. Evaluate the models by using built-in prompt datasets.
- D. Use the model InvocationLatency runtime metrics in Amazon CloudWatch when trying models.
Answer: B
NEW QUESTION # 61
A company wants to develop a large language model (LLM) application by using Amazon Bedrock and customer data that is uploaded to Amazon S3. The company's security policy states that each team can access data for only the team's own customers.
Which solution will meet these requirements?
- A. Create an Amazon Bedrock custom service role for each team that has access to only the team's customer data.
- B. Redact personal data in Amazon S3. Update the S3 bucket policy to allow team access to customer data.
- C. Create one Amazon Bedrock role that has full Amazon S3 access. Create IAM roles for each team that have access to only each team's customer folders.
- D. Create a custom service role that has Amazon S3 access. Ask teams to specify the customer name on each Amazon Bedrock request.
Answer: A
NEW QUESTION # 62
An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.
Which solution meets these requirements with the LEAST implementation effort?
- A. Summarize the response text depending on the age of the user so that younger users receive shorter responses.
- B. Add a role description to the prompt context that instructs the model of the age range that the response should target.
- C. Use chain-of-thought reasoning to deduce the correct style and complexity for a response suitable for that user.
- D. Fine-tune the model by using additional training data that is representative of the various age ranges that the application will support.
Answer: B
NEW QUESTION # 63
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. Neural networks
- B. Linear regression
- C. Logistic regression
- D. Decision trees
Answer: D
NEW QUESTION # 64
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?
- A. Convert audio files to text documents by using ML models.
- B. Enable voice commands for coding and providing natural language search.
- C. Run an application without provisioning or managing servers.
- D. Create software snippets, reference tracking, and open-source license tracking.
Answer: B
NEW QUESTION # 65
An AI practitioner trained a custom model on Amazon Bedrock by using a training dataset that contains confidential data. The AI practitioner wants to ensure that the custom model does not generate inference responses based on confidential data.
How should the AI practitioner prevent responses based on confidential data?
- A. Mask the confidential data in the inference responses by using dynamic data masking.
- B. Delete the custom model. Remove the confidential data from the training dataset. Retrain the custom model.
- C. Encrypt the confidential data in the inference responses by using Amazon SageMaker.
- D. Encrypt the confidential data in the custom model by using AWS Key Management Service (AWS KMS).
Answer: B
NEW QUESTION # 66
A loan company is building a generative AI-based solution to offer new applicants discounts based on specific business criteria. 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. Ensure that the model's inference time is within the accepted limits.
- B. Detect imbalances or disparities in the data.
- C. Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate.
- D. Evaluate the model's behavior so that the company can provide transparency to stakeholders.
- E. Ensure that the model runs frequently.
Answer: B,D
Explanation:
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NEW QUESTION # 67
A company wants to use a large language model (LLM) to develop a conversational agent. The company needs to prevent the LLM from being manipulated with common prompt engineering techniques to perform undesirable actions or expose sensitive information.
Which action will reduce these risks?
- A. Create a prompt template that teaches the LLM to detect attack patterns.
- B. Avoid using LLMs that are not listed in Amazon SageMaker.
- C. Decrease the number of input tokens on invocations of the LLM.
- D. Increase the temperature parameter on invocation requests to the LLM.
Answer: A
NEW QUESTION # 68
Which option is a use case for generative AI models?
- A. Improving network security by using intrusion detection systems
- B. Analyzing financial data to forecast stock market trends
- C. Enhancing database performance by using optimized indexing
- D. Creating photorealistic images from text descriptions for digital marketing
Answer: D
NEW QUESTION # 69
An AI company periodically evaluates its systems and processes with the help of independent software vendors (ISVs). The company needs to receive email message notifications when an ISV's compliance reports become available.
Which AWS service can the company use to meet this requirement?
- A. AWS Audit Manager
- B. AWS Data Exchange
- C. AWS Trusted Advisor
- D. AWS Artifact
Answer: D
NEW QUESTION # 70
A digital devices company wants to predict customer demand for memory hardware. The company does not have coding experience or knowledge of ML algorithms and needs to develop a data-driven predictive model.
The company needs to perform analysis on internal data and external data.
Which solution will meet these requirements?
- A. Store the data in Amazon S3. Create ML models and demand forecast predictions by using Amazon SageMaker built-in algorithms that use the data from Amazon S3.
- B. Import the data into Amazon SageMaker Data Wrangler. Create ML models and demand forecast predictions by using SageMaker built-in algorithms.
- C. Import the data into Amazon SageMaker Data Wrangler. Build ML models and demand forecast predictions by using an Amazon Personalize Trending-Now recipe.
- D. Import the data into Amazon SageMaker Canvas. Build ML models and demand forecast predictions by selecting the values in the data from SageMaker Canvas.
Answer: D
Explanation:
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NEW QUESTION # 71
Which functionality does Amazon SageMaker Clarify provide?
- A. Documents critical details about ML models
- B. Monitors the quality of ML models in production
- C. Integrates a Retrieval Augmented Generation (RAG) workflow
- D. Identifies potential bias during data preparation
Answer: D
NEW QUESTION # 72
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