AIF-C01 Applications of Foundation Models — Timed 40 Questions

Question 1

A financial services company is using Amazon Bedrock to generate investment summaries. They want to ensure that the model outputs are factually accurate and based on the latest market data. Which combination of services should they use to achieve this? (Select TWO)

40 minute time limit — choose an answer to begin.

Accepted Answer

Amazon Aurora with the pgvector extension. Amazon Aurora with the pgvector extension (Option D) enables storing and querying vector embeddings directly within a PostgreSQL-compatible database, which is essential for Retrieval-Augmented Generation (RAG). When combined with Amazon Bedrock Knowledge Bases (Option E), it allows the company to retrieve the most current market data as vector embeddings, ensuring the generated investment summaries are grounded in factual, up-to-date information rather than relying solely on the model's static training data.

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Amazon SageMaker Ground Truth for data labeling

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Amazon DynamoDB as the knowledge base store

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Amazon Kendra for indexing the knowledge base

Question 2

A company uses Amazon Bedrock to generate product descriptions. They want to ensure the outputs consistently follow a specific brand tone (professional yet friendly). They have a small set of example descriptions (few-shot examples) but do not want to fine-tune the model. Which strategy best achieves consistent tone without modifying the base model?

Accepted Answer

Use a system prompt that defines the brand tone and include few-shot examples in the prompt.. Using a system prompt and few-shot examples in the prompt template (Option B) provides explicit guidance to the model at inference time, shaping the tone without any model updates. Option D (retrieval-augmented generation) is for incorporating external knowledge, not tone. Option C (prompt chaining) adds complexity and may not directly enforce tone. Option A (fine-tuning) requires modifying model weights, which is not desired.

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Fine-tune the model on a dataset of product descriptions that exemplify the desired tone.

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Implement prompt chaining by breaking the task into multiple steps, each with its own prompt.

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Use retrieval-augmented generation (RAG) to pull example descriptions from a database and prepend them to the prompt.

Question 3

A company wants to use a foundation model to classify customer feedback into positive, neutral, negative. They have a small labeled dataset. What approach yields best results?

Accepted Answer

Fine-tune a foundation model on their dataset. Fine-tuning a foundation model on a small labeled dataset allows the model to adapt its pre-trained knowledge specifically to the company's sentiment classification task, achieving higher accuracy than zero-shot or generic API approaches. Fine-tuning adjusts the model's weights using the labeled examples, making it sensitive to domain-specific language and nuance in customer feedback, which is critical for a three-class sentiment task.

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Use a pre-built sentiment analysis API

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Fine-tune a foundation model on their dataset

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Use zero-shot classification

Question 4

A developer wants to quickly experiment with multiple foundation models using a single API. Which service provides this capability?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides a single API to access and experiment with multiple foundation models from leading AI providers like AI21 Labs, Anthropic, Cohere, Meta, Stability AI, and Amazon itself. This allows developers to quickly test different models without managing underlying infrastructure or learning separate APIs for each provider.

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AWS Lambda

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Amazon Bedrock

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Amazon SageMaker Studio

Question 5

Which FOUR of the following are benefits of using Amazon Bedrock for foundation models?

Accepted Answer

Ability to fine-tune models. Amazon Bedrock offers several key benefits for working with foundation models. It provides access to multiple models from different providers via a single API (B), includes built-in monitoring and governance features (D), and operates on a serverless infrastructure (E). Additionally, Bedrock supports fine-tuning of foundation models using your own data, allowing customization for specific tasks (A). However, it does not guarantee output accuracy, as model outputs can vary and require validation.

Answer

Guaranteed output accuracy

Question 6

A company uses Amazon SageMaker JumpStart to deploy a foundation model. They want to fine-tune the model on their own dataset. Which SageMaker capability should they use?

Accepted Answer

SageMaker Studio Classic. SageMaker Studio Classic provides a fully integrated development environment (IDE) for machine learning, including built-in support for fine-tuning foundation models deployed via JumpStart. It allows you to access JumpStart model hubs, configure training jobs with custom datasets, and manage the fine-tuning process directly from the Studio interface, making it the correct choice for this task.

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SageMaker Managed Spot Training

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SageMaker Canvas

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SageMaker Autopilot

Question 7

A company wants to use a pre-trained foundation model for sentiment analysis without any customization. Which Amazon Machine Learning service provides access to foundation models via API?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed service that provides access to a wide range of pre-trained foundation models (FMs) from leading AI providers like Anthropic, Meta, and Amazon via a unified API. For sentiment analysis, you can invoke an FM such as Anthropic Claude or Meta Llama directly through the Bedrock API without any customization, making it the correct choice for this use case.

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Amazon Textract

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Amazon Comprehend

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Amazon Rekognition

Question 8

A data scientist runs the above AWS CLI command and receives the error. What is the most likely cause?

Accepted Answer

The model is not supported in the current AWS region.. The error occurs because the foundation model specified in the AWS CLI command is not available in the current AWS region. AWS Bedrock and other AI services have region-specific model availability; invoking a model that hasn't been deployed or enabled in that region results in a validation or access error. Option D correctly identifies this regional restriction as the most likely cause.

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The IAM role does not have permissions.

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The model is being updated.

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The model is being deprecated.

Question 9

A developer is using Amazon Bedrock with the Claude model for text summarization. The output sometimes includes inaccurate information. What is the best practice to reduce hallucinations?

Accepted Answer

Use retrieval augmented generation. Retrieval Augmented Generation (RAG) grounds the model's output in external, authoritative knowledge sources by retrieving relevant documents and injecting them into the prompt context. This directly reduces hallucinations because the model generates summaries based on factual retrieved data rather than relying solely on its parametric memory, which is the primary source of inaccuracies in text summarization tasks.

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Use a larger model

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Increase temperature

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Decrease max tokens

Question 10

Which TWO of the following are benefits of using Amazon Bedrock for foundation models compared to managing your own infrastructure? (Select TWO.)

Accepted Answer

Access to multiple foundation models. Amazon Bedrock is a fully managed service that provides access to multiple foundation models (FMs) from providers like AI21 Labs, Anthropic, Cohere, Meta, and Stability AI through a single API. This eliminates the need to manage separate infrastructure for each model, making option C correct because Bedrock offers a unified access point to a diverse set of FMs without requiring you to host or maintain them yourself.

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Higher throughput for custom models

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Built-in content moderation

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Full control over model weights

Question 11

A healthcare company is deploying a conversational AI using a foundation model on Amazon Bedrock for patient triage. The application must minimize hallucinations and ensure factual accuracy. Which combination of techniques should the team implement?

Accepted Answer

Implement Retrieval-Augmented Generation (RAG) using a knowledge base on Amazon Bedrock and a system prompt demanding factual responses.. RAG retrieves relevant information from a knowledge base, grounding the model's responses in verified facts, which minimizes hallucinations. Additionally, a system prompt demanding factual responses further constrains the model to rely on retrieved context. Option B is incorrect because fine-tuning alone does not guarantee factual accuracy and can still produce hallucinations if the training data contains inaccuracies. Option C is incorrect because RLHF aligns responses with human preferences but does not directly ground responses in factual sources. Option D is incorrect because reducing maxTokens only limits response length, not factual accuracy.

Answer

Fine-tune the model on a large dataset of medical transcripts and deploy with default parameters.

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Use reinforcement learning from human feedback (RLHF) on the deployed model.

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Set the maxTokens to a low value to force shorter, more focused answers.

Question 12

A financial services company is evaluating Amazon Bedrock for a compliance application that requires explainable AI decisions. The model's output must be auditable and traceable to specific reasoning. Which Bedrock feature should they use to meet this requirement?

Accepted Answer

Enable model invocation logging in Amazon Bedrock and store logs in Amazon S3.. Model invocation logging in Amazon Bedrock captures the full request and response payload for each inference call, including the input prompt and the model's generated output. By storing these logs in Amazon S3, the financial services company can audit and trace the model's reasoning for compliance, as the logs provide a verifiable record of what was sent to the model and what it returned, enabling explainability and traceability.

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Create a knowledge base with financial regulations to guide the model.

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Fine-tune a custom model on regulatory documents to improve reasoning.

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Amazon Bedrock Guardrails to filter sensitive content.

Question 13

A company wants to automatically summarize customer support tickets into a short paragraph. Which AWS service is MOST appropriate for this task?

Accepted Answer

Amazon Bedrock. Amazon Bedrock provides access to foundation models (FMs) from providers like Anthropic and AI21 that excel at natural language generation tasks, including summarization. By invoking a model such as Claude or Jurassic-2 via Bedrock's API, you can pass the customer support ticket text and receive a concise paragraph summary. This makes Bedrock the most appropriate service for generative summarization.

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Amazon Rekognition

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Amazon Polly

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Amazon Comprehend

Question 14

A generative AI application occasionally produces factually incorrect responses. The team has already tried prompt engineering and increasing the temperature parameter. Which next step is MOST effective to improve factual accuracy?

Accepted Answer

Implement a Retrieval Augmented Generation (RAG) pipeline. Retrieval Augmented Generation (RAG) is the most effective next step because it grounds the model's responses in a verified external knowledge base, directly addressing factual inaccuracies without requiring retraining. Unlike prompt engineering or temperature adjustments, RAG provides real-time access to authoritative documents, reducing hallucinations by constraining the model's output to retrieved evidence.

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Use a larger foundation model

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Fine-tune the model on company data

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Reduce the temperature to 0

Question 15

A healthcare company processes patient records using a foundation model on Amazon Bedrock. They must ensure no patient data is used to improve the base model. What is the MOST effective configuration?

Accepted Answer

Disable model improvement logging in the Bedrock service settings. Disabling model improvement logging in Amazon Bedrock service settings explicitly prevents AWS from using customer data (including patient records) to improve the base foundation model. This is a dedicated privacy control that ensures compliance with healthcare regulations like HIPAA, as it stops any data from being sent back for model training or fine-tuning.

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Create a VPC endpoint for Bedrock

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Set a data retention policy to store logs for only 30 days

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Enable data encryption at rest and in transit

Question 16

Which TWO factors are MOST important when selecting a foundation model for a text summarization task? (Choose two.)

Accepted Answer

Maximum output length (max tokens). The maximum output length (max tokens) directly determines how much text the model can generate in a single response. For text summarization, you need to ensure the model can produce a summary of the required length—whether it's a single sentence or a multi-paragraph abstract. If the max output is too small, the summary will be truncated or incomplete, failing the task.

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Model creation date

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Model training cost

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Image support

Question 17

Which TWO actions can help reduce bias in a foundation model’s outputs? (Choose two.)

Accepted Answer

Fine-tune the model on a balanced, representative dataset. Fine-tuning on a balanced, representative dataset (Option A) directly addresses bias by ensuring the model learns from data that reflects diverse perspectives and reduces overrepresentation of any group. Careful prompt engineering with neutral wording (Option B) mitigates bias at inference time by avoiding leading or stereotypical language that could skew the model's output. Both actions target the root causes of bias in foundation model outputs.

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Restrict model access to a subset of users

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Increase temperature to add randomness

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Use a larger foundation model

Question 18

Which THREE practices are recommended for responsible AI when deploying foundation models? (Choose three.)

Accepted Answer

Include human review for high-stakes decisions. High-stakes decisions (e.g., medical diagnosis, loan approvals) require human oversight to catch errors, edge cases, and ethical violations that the model cannot self-correct. This aligns with the responsible AI principle of accountability, ensuring a human-in-the-loop can override or validate the model's output before it impacts lives.

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Avoid collecting user feedback to reduce bias

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Use a black box approach to keep model internals secret

Question 19

Refer to the exhibit. This is an Amazon Bedrock invocation request for Claude. What is the purpose of the "stop_sequences" parameter?

Accepted Answer

It tells the model to stop generating when it encounters that sequence. The 'stop_sequences' parameter in Amazon Bedrock's invocation request for Claude tells the model to halt generation as soon as it encounters a specified character sequence. This allows developers to control the output format, such as stopping at a newline or a custom delimiter, ensuring the response ends exactly where intended.

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It specifies a character sequence for the model to include in its response

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It limits the number of tokens in the response

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It controls the randomness of the response

Question 20

Which pricing model does Amazon Bedrock use for foundation model inference?

Accepted Answer

Per-token. Amazon Bedrock charges for foundation model inference based on the number of tokens processed, which includes both input and output tokens. Each model has a specific per-token price, and the total cost is calculated by multiplying the token count by the model's rate. This is the standard pricing model for generative AI inference services like Bedrock.

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Per-request

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Per-hour instance

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Per-GB storage