Amazon AWS Certified Foundational AIF-C01 Questions & Answers

• Question 1:

  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

  Correct Answer: A

  Amazon SageMaker Feature Store is the correct solution for sharing and managing variables (features) across multiple teams during model development.

• Question 2:

  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.

  Correct Answer: B

  Adding a role description to the prompt context is a straightforward way to instruct the generative AI model to adjust its response style based on the user's age range. This method requires minimal implementation effort as it does not involve

  additional training or complex logic.

  Option B (Correct): "Add a role description to the prompt context that instructs the model of the age range that the response should target":This is the correct answer because it involves the least implementation effort while effectively guiding

  the model to tailor responses according to the age range. Option A:"Fine-tune the model by using additional training data" is incorrect because it requires significant effort in gathering data and retraining the model. Option C:"Use chain-ofthought reasoning" is incorrect as it involves complex reasoning that may not directly address the need to adjust response style based on age.

  Option D:"Summarize the response text depending on the age of the user" is incorrect because it involves additional processing steps after generating the initial response, increasing complexity.

  AWS AI Practitioner

  References:

  Prompt Engineering Techniques on AWS:AWS recommends using prompt context effectively to guide generative models in providing tailored responses based on specific user attributes.

• 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

  Correct Answer: D

  Benchmark datasets are pre-validated datasets specifically designed to evaluate machine learning models for bias, fairness, and potential discrimination. These datasets are the most efficient tool for assessing an LLM's performance against

  known standards with minimal administrative effort.

  Option D (Correct): "Benchmark datasets":This is the correct answer because using standardized benchmark datasets allows the company to evaluate model outputs for bias with minimal administrative overhead. Option A:"User-generated

  content" is incorrect because it is unstructured and would require significant effort to analyze for bias. Option B:"Moderation logs" is incorrect because they represent historical data and do not provide a standardized basis for evaluating bias.

  Option C:"Content moderation guidelines" is incorrect because they provide qualitative criteria rather than a quantitative basis for evaluation.

  AWS AI Practitioner

  References:

  Evaluating AI Models for Bias on AWS:AWS supports using benchmark datasets to assess model fairness and detect potential bias efficiently.

• Question 4:

  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

  Correct Answer: A

  Amazon Bedrock offers an on-demand pricing model that provides flexibility without long- term commitments. This model allows companies to pay only for the resources they use, which is ideal for a limited budget and offers flexibility. Option

  A (Correct): "On-Demand":This is the correct answer because on-demand pricing allows the company to use Amazon Bedrock without any long-term commitments and to manage costs according to their budget. Option B:"Model

  customization" is a feature, not a pricing model. Option C:"Provisioned Throughput" involves reserving capacity ahead of time, which might not offer the desired flexibility and could lead to higher costs if the capacity is not fully used.

  Option D:"Spot Instance" is a pricing model for EC2 instances and does not apply to Amazon Bedrock.

  AWS AI Practitioner

  References:

  AWS Pricing Models for Flexibility:On-demand pricing is a key AWS model for services that require flexibility and no long-term commitment, ensuring cost- effectiveness for projects with variable usage patterns.

• Question 5:

  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.

  Correct Answer: A

  To securely use large language models (LLMs) on Amazon Bedrock, companies should design clear and specific prompts to avoid unintended outputs and ensure proper configuration of AWS Identity and Access Management (IAM) roles

  and policies with the principle of least privilege. This approach limits access to sensitive resources and minimizes the potential impact of security incidents. Option A (Correct): "Design clear and specific prompts. Configure AWS Identity and

  Access Management (IAM) roles and policies by using least privilege access":This is the correct answer as it directly addresses both security practices in prompt design and access management.

  Option B:"Enable AWS Audit Manager for automatic model evaluation jobs" is incorrect because Audit Manager is for compliance and auditing, not directly related to secure LLM usage.

  Option C:"Enable Amazon Bedrock automatic model evaluation jobs" is incorrect because Bedrock does not provide automatic model evaluation jobs specifically for security purposes.

  Option D:"Use Amazon CloudWatch Logs to make models explainable and to monitor for bias" is incorrect because CloudWatch Logs are used for monitoring and not directly for making models explainable or secure.

  AWS AI Practitioner References:

  Secure AI Practices on AWS:AWS recommends configuring IAM roles and using least privilege access to ensure secure usage of AI models.

• Question 6:

  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

  Correct Answer: C

  The scenario where a student copies content from generative AI to write essays represents the challenge ofplagiarismin responsible AI use.

• 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

  Correct Answer: A

  Data augmentation for imbalanced classes is the correct technique to address bias in input data affecting image generation.

• Question 8:

  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.

  Correct Answer: B

  Amazon SageMaker Clarify provides transparency and explainability for machine learning models by generating metrics, reports, and examples that help to understand model predictions. For a medical company that needs a foundation model to be transparent and explainable to meet regulatory requirements, SageMaker Clarify is the most suitable solution.

• Question 9:

  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.

  Correct Answer: B

  Decreasing the number of tokens in the prompt reduces the cost associated with using an LLM model on Amazon Bedrock, as costs are often based on the number of tokens processed by the model.

• Question 10:

  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.

  Correct Answer: A

  Adjusting the prompt is the correct solution to align the LLM outputs with the company's expectations for short, specific language responses.