AIF-C01 Applications of Foundation Models Practice Test 10 — 15 Questions

Question 1

A financial services company is deploying a foundation model on Amazon Bedrock to generate compliance reports from internal audit logs. The model must not output any personally identifiable information (PII). They have configured a Bedrock Guardrail with sensitive information filters set to the 'HIGH' sensitivity level. During testing in a staging environment, testers still observed PII being occasionally generated in the report outputs. The guardrail did not block these instances because the PII was embedded in a context that the guardrail's pattern matching did not catch (e.g., structured JSON data with embedded names). The company requires a solution that minimizes latency and cost, as they process thousands of reports daily. They cannot afford to increase inference time significantly due to strict SLAs. They also want to avoid re-engineering the entire solution. Which additional step should they take to effectively eliminate PII leakage while maintaining performance?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Implement a post-processing Lambda function that uses Amazon Comprehend's PII detection to scan and redact any PII from the model output before returning it.. Amazon Comprehend's PII detection API can be invoked as a post-processing step to scan and redact PII from the model output without requiring any changes to the model or guardrail configuration. This approach adds minimal latency (typically under 100ms per request) and cost per API call is low, making it suitable for high-throughput scenarios. It directly addresses the guardrail's failure to catch PII embedded in structured contexts like JSON, as Comprehend uses machine learning models that can identify PII even when it's not in plain text patterns.

Answer

Add a prompt instruction to the model to never output PII, with few-shot examples of non-PII outputs.

Answer

Fine-tune the foundation model on a dataset that excludes PII.

Answer

Increase the guardrail sensitivity to 'MAXIMUM'.

Question 2

A developer is trying to invoke the Claude v2 model in Amazon Bedrock from a Lambda function. The Lambda function's IAM role has the following policy attached:

{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": "bedrock:InvokeModel",
      "Resource": "*"
    }
  ]
}

When the Lambda function runs, it receives the error shown in the exhibit. Which additional step is most likely needed to resolve this issue?

Accepted Answer

Request access to the Anthropic Claude model through the Amazon Bedrock console.. Amazon Bedrock requires explicit user access approval for third-party foundation models like Anthropic Claude before they can be invoked. Even with a valid IAM policy allowing bedrock:InvokeModel on all resources, the model itself must be granted access via the Bedrock console's 'Model access' section. Without this step, the API returns an access denied error regardless of IAM permissions.

Answer

Change the AWS region to one where Claude v2 is available.

Answer

Use a different model ID such as 'anthropic.claude-v1'.

Answer

Add a condition to the IAM policy to specify the model ARN.

Question 3

A financial services company has built an internal assistant on Amazon Bedrock using Anthropic Claude 3 Sonnet. Employees ask questions that require retrieving the latest internal policy documents, which are updated frequently and stored in Amazon S3. The company wants the assistant to answer with accurate, up-to-date citations without retraining the model. Which approach should they implement?

Accepted Answer

Use Retrieval Augmented Generation (RAG) by creating a knowledge base in Amazon Bedrock that indexes the S3 documents.. Grounding a foundation model in frequently changing internal documents is best achieved with Retrieval Augmented Generation. An Amazon Bedrock knowledge base ingests the S3 content, chunks and embeds it, and retrieves relevant passages at inference time so the model can answer with current, citable information. This avoids retraining and keeps responses accurate as policies evolve.

Answer

Increase the temperature parameter to encourage the model to generate more detailed and specific policy answers.

Answer

Enable model invocation logging in Amazon Bedrock to capture requests and responses for later review.

Answer

Fine-tune the Claude 3 Sonnet model on the policy documents using Amazon Bedrock custom models.

Question 4

A media company uses Amazon Bedrock to generate short product descriptions. They notice that outputs vary in tone and sometimes include unwanted promotional claims. They want consistent, brand-aligned results while keeping the same foundation model. Which action best addresses this requirement?

Accepted Answer

Use a prompt template that specifies brand voice, required structure, and forbidden claims, and reuse it for every generation request.. Consistent brand-aligned generation is achieved by controlling the instructions the model receives. A reusable prompt template that defines voice, structure, and forbidden claims gives the model explicit guidance on every request, producing more uniform output. Guardrails, sampling parameters, and larger models can support quality but do not by themselves encode brand style and claim restrictions.

Answer

Switch to a larger foundation model with more parameters to improve output quality and consistency.

Answer

Create a guardrail in Amazon Bedrock with a denied topics policy and a word filter for prohibited promotional terms.

Answer

Lower the top-p value to 0.1 so the model only considers the most probable tokens.

Question 5

A solutions architect needs to build a generative AI application that can invoke foundation models from Amazon and third-party providers through a single, unified API without managing any infrastructure. The architect wants the fastest path to a working prototype using AWS-native tooling. Which AWS service should the architect choose?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is purpose-built for invoking foundation models from Amazon and third-party providers through one API, with no infrastructure to manage. SageMaker AI offers flexibility but adds operational overhead, while Comprehend and Polly serve narrow NLP and speech use cases rather than generative model invocation. The unified, serverless model access makes Bedrock the correct choice for a fast prototype.

Answer

Amazon SageMaker AI

Answer

Amazon Polly

Answer

Amazon Comprehend

Question 6

A media company uses Amazon Bedrock to generate personalized news summaries. They notice that summaries sometimes include details not present in the source articles. They want to reduce these hallucinations without retraining the model. Which approach should they use?

Accepted Answer

Use Retrieval Augmented Generation (RAG) by retrieving relevant passages from the source articles and including them in the prompt.. Retrieval Augmented Generation supplies the model with relevant excerpts from the source articles at inference time, so the generated summary is conditioned on actual content rather than the model's internal knowledge. This reduces fabricated details without retraining. Other options either increase randomness, rely on model size, or truncate output, none of which ground the response in the provided material.

Answer

Decrease the maximum token length in the InvokeModel request.

Answer

Switch to a larger foundation model with more parameters.

Answer

Increase the model's temperature setting to encourage more creative outputs.

Question 7

A developer is building a retrieval-augmented generation (RAG) assistant on Amazon Bedrock. The assistant must answer questions about internal policy documents that change frequently, and answers must cite the source passages. The developer wants a managed capability that handles chunking, embedding, and retrieval so the application code stays minimal. Which Amazon Bedrock feature should the developer use?

Accepted Answer

Amazon Bedrock knowledge bases. Amazon Bedrock knowledge bases are the managed RAG capability that handles ingestion, chunking, embedding, vector storage, and retrieval, enabling grounded answers with source citations while keeping application code small. Evaluation, Guardrails, and Agents address quality measurement, content safety, and task orchestration respectively, none of which deliver the required retrieval pipeline on their own.

Answer

Amazon Bedrock model evaluation

Answer

Amazon Bedrock Agents

Answer

Amazon Bedrock Guardrails

Question 8

A financial services firm uses Amazon Bedrock with Anthropic Claude to analyze earnings call transcripts. They need the model to output results in a strict JSON schema for downstream processing. The model occasionally returns prose or invalid JSON. Which Amazon Bedrock feature should they use to enforce the output structure?

Accepted Answer

Converse API with a tool definition that specifies the JSON schema as input parameters.. The Converse API's tool use capability lets you define a tool whose input schema is the desired JSON structure. The model then returns arguments conforming to that schema when it invokes the tool, providing reliable structured output. Guardrails, logging, and sampling parameters do not enforce a schema, so they cannot guarantee valid JSON for downstream processing.

Answer

Increase the top_p value to 1.0 to make the output more deterministic.

Answer

Guardrails for Amazon Bedrock with a denied topics policy.

Answer

Model invocation logging to Amazon CloudWatch.

Question 9

A company wants its Amazon Bedrock application to always answer in a formal tone, never discuss competitors, and never reveal internal project codenames. The controls must apply consistently to every request and response without changing the underlying model. Which Amazon Bedrock capability should the company configure?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails enforce content policies such as denied topics, word filters, and sensitive information detection on inputs and outputs independently of the model, so the same rules apply across every request. Sampling parameters, throughput reservations, and context window size influence generation behavior or capacity but cannot block topics or enforce tone consistently.

Answer

A higher temperature setting

Answer

Provisioned Throughput

Answer

A larger context window

Question 10

A retail company wants to build an application that uses a foundation model on Amazon Bedrock to answer customer questions about product availability. They need the model to access real-time inventory data from their internal database and perform actions such as reserving an item. Which TWO capabilities should they implement to achieve this? (Choose two.)

Accepted Answer

Define an action group in the agent that maps to an API schema for the inventory and reservation operations.. Amazon Bedrock Agents orchestrate tasks by invoking action groups, which are defined with OpenAPI schemas and backed by Lambda functions. This allows the agent to query real-time inventory and perform reservations. RAG over static documents, temperature changes, and logging do not provide live data access or transactional capabilities, so they cannot satisfy the requirements.

Answer

Enable model invocation logging to capture inventory queries.

Answer

Use Retrieval Augmented Generation with a static document store containing product manuals.

Answer

Increase the model's temperature to allow more creative problem-solving.

Question 11

A financial services company uses Amazon Bedrock with the Anthropic Claude 3 Haiku model to answer employee questions about internal policies. The knowledge base is updated weekly, and the company wants the model to cite the exact source document and page number in its responses. Which approach should the company use to meet these requirements?

Accepted Answer

Use Amazon Bedrock Knowledge Bases with a vector store and enable citations in the RetrieveAndGenerate API call.. The company needs answers grounded in specific internal documents with citations. Amazon Bedrock Knowledge Bases performs retrieval-augmented generation by fetching relevant passages from a connected data source and can return citations that point to the source document and page. This directly satisfies the requirement, whereas fine-tuning, temperature adjustments, or Guardrails do not provide source attribution.

Answer

Increase the temperature setting in the InvokeModel API call to encourage the model to include source references.

Answer

Fine-tune the Anthropic Claude 3 Haiku model on the internal policy documents and deploy the custom model.

Answer

Use Amazon Bedrock Guardrails to filter responses and automatically append document citations.

Question 12

A team is deploying a foundation model on Amazon Bedrock for a customer-facing assistant. They must reduce hallucinations and keep answers grounded in approved company content while controlling inference cost. Which TWO approaches should the team implement? (Choose two.)

Accepted Answer

Configure Amazon Bedrock Guardrails contextual grounding and relevance checks to filter ungrounded responses.. Grounding answers in approved content through Bedrock knowledge bases supplies factual context, while Guardrails contextual grounding and relevance checks verify that generated responses are supported by that context. Together they reduce hallucination and keep outputs aligned with company material. Raising temperature, removing instructions, or simply choosing a larger model does not provide grounding and can increase cost or fabrication risk.

Answer

Remove all system instructions so the model relies solely on its pretrained knowledge.

Answer

Switch to the largest available foundation model to guarantee factual accuracy.

Answer

Increase the model temperature to encourage more creative and varied responses.

Question 13

A financial services company uses Amazon Bedrock with the Anthropic Claude 3 Sonnet model to answer employee questions about internal policies. The policy documents are updated frequently, and the model occasionally provides outdated or incorrect policy details. The company wants the model to base its answers on the most current authoritative documents without retraining the model. Which approach should they use?

Accepted Answer

Use Retrieval Augmented Generation (RAG) by storing policy documents in an Amazon Bedrock knowledge base and retrieving relevant passages at inference time.. Retrieval Augmented Generation with an Amazon Bedrock knowledge base retrieves relevant, current policy passages and supplies them to the model as context, so answers are grounded in authoritative documents. It avoids retraining costs and keeps pace with frequent updates, directly solving the outdated or incorrect policy detail problem.

Answer

Switch to a larger foundation model with a higher parameter count.

Answer

Fine-tune the foundation model on the updated policy documents.

Answer

Increase the model's temperature setting to encourage more creative and up-to-date answers.

Question 14

A developer wants to compare the output quality of several foundation models available in Amazon Bedrock for a text summarization task. They need to evaluate responses side by side using the same prompt. Which Amazon Bedrock feature should they use?

Accepted Answer

Amazon Bedrock model evaluation. Amazon Bedrock model evaluation provides both automatic and human-based evaluation workflows to compare foundation models on tasks like summarization. It lets you run the same prompt across models and review results side by side. Guardrails, Agents, and provisioned throughput serve different purposes such as content filtering, orchestration, and capacity reservation, so they cannot fulfill the comparison requirement.

Answer

Amazon Bedrock provisioned throughput

Answer

Amazon Bedrock Agents

Answer

Amazon Bedrock Guardrails

Question 15

A media company uses Amazon Bedrock to generate article summaries. They notice that for long articles, the model sometimes ignores instructions placed at the beginning of the prompt. The company wants to improve the model's adherence to instructions without changing the model or increasing cost significantly. Which prompt engineering technique should they apply?

Accepted Answer

Move the instructions to the end of the prompt and repeat them after the article text.. The model's tendency to overlook early instructions in long contexts is a known limitation. By moving instructions to the end of the prompt and repeating them after the article, the company places them where the model's attention is strongest. This prompt engineering technique improves adherence without retraining or increasing cost, making it the most effective solution.

Answer

Use a higher temperature value to make the model more creative in following instructions.

Answer

Split the article into smaller chunks and summarize each chunk separately, then concatenate the summaries.

Answer

Increase the maxTokenCount parameter to allow the model to process more of the article.