Amazon AIF-C01 Question Answers

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Question # 1

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. Fine-tune the model by using additional training data that is representative of the various age ranges that the application will support.
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. Summarize the response text depending on the age of the user so that younger users receive shorter responses.

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Question # 2

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

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Question # 3

A social media company wants to use a large language model (LLM) for content moderation. The company wants to evaluate the LLM outputs for bias and potential discrimination against specific groups or individuals. Which data source should the company use to evaluate the LLM outputs with the LEAST administrative effort? 
 

A. User-generated content
B. Moderation logs
C. Content moderation guidelines
D. Benchmark datasets

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Question # 4

How can companies use large language models (LLMs) securely on Amazon Bedrock? 
 

A. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.
B. Enable AWS Audit Manager for automatic model evaluation jobs.
C. Enable Amazon Bedrock automatic model evaluation jobs.
D. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.

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Question # 5

A company wants to create an application by using Amazon Bedrock. The company has a limited budget and prefers flexibility without long-term commitment. Which Amazon Bedrock pricing model meets these requirements? 
 

A. On-Demand
B. Model customization
C. Provisioned Throughput
D. Spot Instance

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Question # 6

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. Configure the security and compliance by using Amazon Inspector.
B. Generate simple metrics, reports, and examples by using Amazon SageMaker Clarify.
C. Encrypt and secure training data by using Amazon Macie.
D. Gather more data. Use Amazon Rekognition to add custom labels to the data.

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Question # 7

An AI practitioner is building a model to generate images of humans in various professions. The AI practitioner discovered that the input data is biased and that specific attributes affect the image generation and create bias in the model. Which technique will solve the problem? 

A. Data augmentation for imbalanced classes
B. Model monitoring for class distribution
C. Retrieval Augmented Generation (RAG)
D. Watermark detection for images

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Question # 8

A student at a university is copying content from generative AI to write essays. Which challenge of responsible generative AI does this scenario represent? 
 

A. Toxicity
B. Hallucinations
C. Plagiarism
D. Privacy

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Question # 9

A law firm wants to build an AI application by using large language models (LLMs). The application will read legal documents and extract key points from the documents. Which solution meets these requirements? 
 

A. Build an automatic named entity recognition system.
B. Create a recommendation engine.
C. Develop a summarization chatbot.
D. Develop a multi-language translation system.

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Question # 10

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 Lex
B. Amazon Comprehend
C. Amazon Polly
D. Amazon Bedrock
E. Amazon Rekognition

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Question # 11

A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language. Which solution will align the LLM response quality with the company's expectations? 
 

A. Adjust the prompt.
B. Choose an LLM of a different size.
C. Increase the temperature.
D. Increase the Top K value.

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Question # 12

A company is using few-shot prompting on a base model that is hosted on Amazon Bedrock. The model currently uses 10 examples in the prompt. The model is invoked once daily and is performing well. The company wants to lower the monthly cost. Which solution will meet these requirements? 
 

A. Customize the model by using fine-tuning.
B. Decrease the number of tokens in the prompt.
C. Increase the number of tokens in the prompt.
D. Use Provisioned Throughput.

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Question # 13

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. Reduce the volume of data that is used in training.
B. Add hyperparameters to the model.
C. Increase the volume of data that is used in training.
D. Increase the model training time.

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Question # 14

A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months. Which AWS solution should the company use to automate the generation of graphs? 
 

A. Amazon Q in Amazon EC2
B. Amazon Q Developer
C. Amazon Q in Amazon QuickSight
D. Amazon Q in AWS Chatbot

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Question # 15

A company is building a large language model (LLM) question answering chatbot. The company wants to decrease the number of actions call center employees need to take to respond to customer questions. Which business objective should the company use to evaluate the effect of the LLM chatbot? 
 

A. Website engagement rate
B. Average call duration
C. Corporate social responsibility
D. Regulatory compliance

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Question # 16

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. Ensure that the role that Amazon Bedrock assumes has permission to decrypt data with the correct encryption key.
B. Set the access permissions for the S3 buckets to allow public access to enable accessover the internet.
C. Use prompt engineering techniques to tell the model to look for information in AmazonS3.
D. Ensure that the S3 data does not contain sensitive information.

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Question # 17

A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to know how much information can fit into one prompt. Which consideration will inform the company's decision? 
 

A. Temperature
B. Context window
C. Batch size
D. Model size

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Question # 18

What are tokens in the context of generative AI models? 
 

A. Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units.
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 specific prompts or instructions given to a generative AI model to generate output.

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Question # 19

A company is building an ML model to analyze archived data. The company must perform inference on large datasets that are multiple GBs in size. The company does not need to access the model predictions immediately. Which Amazon SageMaker inference option will meet these requirements? 
 

A. Batch transform
B. Real-time inference
C. Serverless inference
D. Asynchronous inference

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Question # 20

A company has built a chatbot that can respond to natural language questions with images. The company wants to ensure that the chatbot does not return inappropriate or unwanted images. Which solution will meet these requirements? 
 

A. Implement moderation APIs.
B. Retrain the model with a general public dataset.
C. Perform model validation.
D. Automate user feedback integration.

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Question # 21

A research company implemented a chatbot by using a foundation model (FM) from Amazon Bedrock. The chatbot searches for answers to questions from a large database of research papers. After multiple prompt engineering attempts, the company notices that the FM is performing poorly because of the complex scientific terms in the research papers. How can the company improve the performance of the chatbot? 

A. Use few-shot prompting to define how the FM can answer the questions.
B. Use domain adaptation fine-tuning to adapt the FM to complex scientific terms.
C. Change the FM inference parameters.
D. Clean the research paper data to remove complex scientific terms.

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Question # 22

An AI practitioner is using a large language model (LLM) to create content for marketing campaigns. The generated content sounds plausible and factual but is incorrect. Which problem is the LLM having? 
 

A. Data leakage
B. Hallucination
C. Overfitting
D. Underfitting

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Question # 23

A medical company deployed a disease detection model on Amazon Bedrock. To comply with privacy policies, the company wants to prevent the model from including personal patient information in its responses. The company also wants to receive notification when policy violations occur. Which solution meets these requirements? 
 

A. Use Amazon Macie to scan the model's output for sensitive data and set up alerts forpotential violations.
B. Configure AWS CloudTrail to monitor the model's responses and create alerts for anydetected personal information.
C. Use Guardrails for Amazon Bedrock to filter content. Set up Amazon CloudWatchalarms for notification of policy violations.
D. Implement Amazon SageMaker Model Monitor to detect data drift and receive alertswhen model quality degrades.

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Question # 24

A company is implementing the Amazon Titan foundation model (FM) by using Amazon Bedrock. The company needs to supplement the model by using relevant data from the company's private data sources. Which solution will meet this requirement? 
 

A. Use a different FM
B. Choose a lower temperature value
C. Create an Amazon Bedrock knowledge base
D. Enable model invocation logging

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Question # 25

A company is using the Generative AI Security Scoping Matrix to assess security responsibilities for its solutions. The company has identified four different solution scopes based on the matrix. Which solution scope gives the company the MOST ownership of security responsibilities? 
 

A. Using a third-party enterprise application that has embedded generative AI features.
B. Building an application by using an existing third-party generative AI foundation model (FM).
C. Refining an existing third-party generative AI foundation model (FM) by fine-tuning themodel by using data specific to the business.
D. Building and training a generative AI model from scratch by using specific data that acustomer owns.

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Question # 26

A company has terabytes of data in a database that the company can use for business analysis. The company wants to build an AI-based application that can build a SQL query from input text that employees provide. The employees have minimal experience with technology. Which solution meets these requirements? 
 

A. Generative pre-trained transformers (GPT)
B. Residual neural network
C. Support vector machine
D. WaveNet

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Question # 27

A company wants to develop an educational game where users answer questions such as the following: "A jar contains six red, four green, and three yellow marbles. What is the probability of choosing a green marble from the jar?" Which solution meets these requirements with the LEAST operational overhead? 
 

A. Use supervised learning to create a regression model that will predict probability.
B. Use reinforcement learning to train a model to return the probability.
C. Use code that will calculate probability by using simple rules and computations.
D. Use unsupervised learning to create a model that will estimate probability density.

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Question # 28

A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources. Which AI learning strategy provides this self-improvement capability? 
 

A. Supervised learning with a manually curated dataset of good responses and badresponses
B. Reinforcement learning with rewards for positive customer feedback
C. Unsupervised learning to find clusters of similar customer inquiries
D. Supervised learning with a continuously updated FAQ database

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