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Amazon AIF-C01 Sample Questions
Question # 1Which task represents a practical use case to apply a regression model?
A. Suggest a genre of music for a listener from a list of genres.
B. Cluster movies based on movie ratings and viewers.
C. Use historical data to predict future temperatures in a specific city.
D. Create a picture that shows a specific object.
Question # 2
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention. The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.Which solution meets these requirements?
A. Set a low limit on the number of tokens the FM can produce.
B. Use batch inferencing to process detailed responses.
C. Experiment and refine the prompt until the FM produces the desired responses.
D. Define a higher number for the temperature parameter.
Question # 3
A company creates video content. The company wants to use generative AI to generate new creative content and to reduce video creation time. Which solution will meet these requirements in the MOST operationally efficient way?
A. Use the Amazon Titan Image Generator model on Amazon Bedrock to generate intermediate images.
Use video editing software to create videos.
B. Use the Amazon Nova Canvas model on Amazon Bedrock to generate intermediate images. Use video
editing software to create videos.
C. Use the Amazon Nova Reel model on Amazon Bedrock to generate videos.
D. Use the Amazon Nova Pro model on Amazon Bedrock to generate videos.
Question # 4
An online media streaming company wants to give its customers the ability to perform natural language-based image search and filtering. The company needs a vector database that can help with similarity searches and nearest neighbor queries.Which AWS service meets these requirements?
A. Amazon Comprehend
B. Amazon Personalize
C. Amazon Polly
D. Amazon OpenSearch Service
Question # 5
A company is developing an ML model to predict heart disease risk. The model uses patient data, such as age, cholesterol, blood pressure, smoking status, and exercise habits. The dataset includes a target value that indicates whether a patient has heart disease.Which ML technique will meet these requirements?
A. Unsupervised learning
B. Supervised learning
C. Reinforcement learning
D. Semi-supervised learning
Question # 6
Which AWS service or feature can help an AI development team quickly deploy and consume a foundation model (FM) within the team's VPC?
A. Amazon Personalize
B. Amazon SageMaker JumpStart
C. PartyRock, an Amazon Bedrock Playground
D. Amazon SageMaker endpoints
Question # 7
A company needs to log all requests made to its Amazon Bedrock API. The company must retain the logs securely for 5 years at the lowest possible cost.Which combination of AWS service and storage class meets these requirements? (Select TWO.)
A. AWS CloudTrail
B. Amazon CloudWatch
C. AWS Audit Manager
D. Amazon S3 Intelligent-Tiering
E. Amazon S3 Standard
Question # 8
A social media company wants to use a large language model (LLM) to summarize messages. The company has chosen a few LLMs that are available on Amazon SageMaker JumpStart. The company wants to compare the generated output toxicity of these models.Which strategy gives the company the ability to evaluate the LLMs with the LEAST operational overhead?
A. Crowd-sourced evaluation
B. Automatic model evaluation
C. Model evaluation with human workers
D. Reinforcement learning from human feedback (RLHF)
Question # 9
Which option is a benefit of using Amazon SageMaker Model Cards to document AI models?
A. Providing a visually appealing summary of a model's capabilities.
B. Standardizing information about a model's purpose, performance, and limitations.
C. Reducing the overall computational requirements of a model.
D. Physically storing models for archival purposes.
Question # 10
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.Which prompt engineering strategy meets these requirements?
A. Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.
B. Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
C. Provide the new text passage to be classified without any additional context or examples.
D. Provide the new text passage with a few examples of unrelated tasks, such as text summarization or
question answering
Question # 11
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt.Which adjustment to an inference parameter should the company make to meet these requirements?
A. Decrease the temperature value
B. Increase the temperature value
C. Decrease the length of output tokens
D. Increase the maximum generation length
Question # 12
A company wants to keep its foundation model (FM) relevant by using the most recent data. The company wants to implement a model training strategy that includes regular updates to the FM.Which solution meets these requirements?
A. Batch learning
B. Continuous pre-training
C. Static training
D. Latent training
Question # 13
An airline company wants to build a conversational AI assistant to answer customer questions about flight schedules, booking, and payments. The company wants to use large language models (LLMs) and a knowledge base to create a text-based chatbot interface.Which solution will meet these requirements with the LEAST development effort?
A. Train models on Amazon SageMaker Autopilot.
B. Develop a Retrieval Augmented Generation (RAG) agent by using Amazon Bedrock.
C. Create a Python application by using Amazon Q Developer.
D. Fine-tune models on Amazon SageMaker Jumpstart.
Question # 14
A company wants to build an ML model by using Amazon SageMaker. The company needs to share and manage variables for model development across multiple teams.Which SageMaker feature meets these requirements?
A. Amazon SageMaker Feature Store
B. Amazon SageMaker Data Wrangler
C. Amazon SageMaker Clarify
D. Amazon SageMaker Model Cards
Question # 15
A company has implemented a generative AI solution to create personalized exercise routines for premium subscription users. The company offers free basic subscriptions and paid premium subscriptions. The company wants to evaluate the AI solution's return on investment over time.
A. The average revenue per user (ARPU) over the past month
B. The number of daily interactions by basic subscription users
C. The conversion rate and the customer retention rate
D. The decrease in the number of premium customer queries and issue volume
Question # 16
Which AWS service makes foundation models (FMs) available to help users build and scale generative AI applications?
A. Amazon Q Developer
B. Amazon Bedrock
C. Amazon Kendra
D. Amazon Comprehend
Question # 17
Which AW5 service makes foundation models (FMs) available to help users build and scale generative AI applications?
A. Amazon Q Developer
B. Amazon Bedrock
C. Amazon Kendra
D. Amazon Comprehend
Question # 18
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. Purchase Provisioned Throughput for the custom model.
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. Grant access to the custom model in Amazon Bedrock.
Question # 19
A company wants to build an ML model to detect abnormal patterns in sensor data. The company does not have labeled data for training. Which ML method will meet these requirements?
A. Linear regression
B. Classification
C. Decision tree
D. Autoencoders
Question # 20
In which stage of the generative AI model lifecycle are tests performed to examine the model's accuracy?
A. Deployment
B. Data selection
C. Fine-tuning
D. Evaluation