AIF-C01 Applications of Foundation Models Practice Test 9 — 15 Questions

Question 1

Which parameter controls the randomness of generated text in a foundation model?

Accepted Answer

temperature. Temperature is the correct parameter because it directly controls the randomness of token sampling in a foundation model. A lower temperature (e.g., 0.1) makes the model more deterministic by concentrating probability mass on the most likely tokens, while a higher temperature (e.g., 1.5) flattens the probability distribution, increasing the likelihood of less probable tokens and thus generating more diverse or creative outputs.

Answer

top_p

Answer

stop sequences

Answer

max_tokens

Question 2

Which THREE are best practices for ensuring generated content complies with corporate brand guidelines when using Amazon Bedrock?

Accepted Answer

Implement guardrails to restrict tone, topics, and language. Amazon Bedrock Guardrails allow you to define policies that restrict the model's output to specific tones, topics, and language, ensuring alignment with corporate brand guidelines. By configuring denied topics and content filters, you can prevent the model from generating off-brand or inappropriate content, directly enforcing compliance at the inference layer.

Answer

Increase the temperature for more creative outputs

Answer

Use random prompts to test variability

Question 3

Which TWO AWS services can be used together to build a chatbot that leverages a foundation model for natural language understanding?

Accepted Answer

Amazon Lex. Amazon Lex provides the conversational interface and natural language understanding (NLU) to interpret user intents and manage dialog, while Amazon Bedrock gives access to foundation models (FMs) for advanced natural language generation and understanding. Together, Lex can route utterances to a Bedrock FM via a Lambda function or direct integration, enabling a chatbot that leverages a pre-trained FM for richer responses.

Answer

Amazon Rekognition

Answer

Amazon Polly

Answer

AWS Glue

Question 4

Refer to the exhibit. You receive this response from Amazon Bedrock. What is the most likely cause of the incomplete information?

Accepted Answer

The max_tokens limit was reached. The response from Amazon Bedrock shows an incomplete sentence that cuts off mid-thought, which is a classic symptom of hitting the max_tokens limit. When the generated output reaches the specified maximum number of tokens, the model stops generating immediately, resulting in truncated text. This is the most likely cause because the output is syntactically incomplete but otherwise coherent up to the cutoff point.

Answer

The prompt was too short

Answer

The temperature was too high

Answer

The model lacks knowledge about capitals

Question 5

Refer to the exhibit. You are trying to invoke a foundation model via Amazon Bedrock but receive this error. What should you do to resolve it?

Accepted Answer

Request model access in the Bedrock console. The error indicates that the user has not been granted access to the specific foundation model in Amazon Bedrock. Even with valid IAM permissions, each model requires explicit access approval via the Bedrock console's 'Model access' section. Option B is correct because requesting model access there provisions the necessary service-level authorization.

Answer

Increase the service quota for Bedrock

Answer

Attach the AmazonBedrockFullAccess IAM policy

Answer

Use a different AWS Region

Question 6

A startup uses Amazon Bedrock with a provisioned throughput to generate product images. They now have unpredictable traffic and want to reduce costs. What should they do?

Accepted Answer

Switch to on-demand mode in Amazon Bedrock.. On-demand mode in Amazon Bedrock allows you to pay per inference request without committing to a provisioned throughput, making it ideal for unpredictable traffic patterns. This eliminates the cost of idle capacity while still providing access to the same foundation models. Option D directly addresses the need to reduce costs when traffic is variable.

Answer

Switch to batch inference using Amazon Bedrock.

Answer

Keep the provisioned throughput but reduce the number of units.

Answer

Use a different model or service like Amazon SageMaker with spot instances.

Question 7

A financial services company needs to use a foundation model for sensitive data analysis. They require that all data remains within a VPC and no data leaves the AWS network. Which solution should they choose?

Accepted Answer

Use Amazon Bedrock with a custom model and VPC endpoints.. Amazon Bedrock with a custom model and VPC endpoints ensures that all data remains within the VPC and never traverses the public internet, meeting the requirement for sensitive data analysis. VPC endpoints (AWS PrivateLink) allow private connectivity to Bedrock, and a custom model can be deployed within the VPC for inference, keeping data entirely within the AWS network.

Answer

Use Amazon Comprehend with VPC.

Answer

Use Amazon Bedrock with a public endpoint.

Answer

Use Amazon SageMaker with a VPC-only real-time endpoint hosting a foundation model.

Question 8

Which TWO of the following are benefits of using Amazon Bedrock for building applications with foundation models?

Accepted Answer

No infrastructure management. Amazon Bedrock is a fully managed service that abstracts away the underlying infrastructure required to host and run foundation models (FMs). By using Bedrock, you do not need to provision, configure, or manage servers, GPUs, or scaling policies, which is a key benefit for developers who want to focus on building applications rather than managing infrastructure. Additionally, Bedrock provides a single API to access multiple FMs from providers like AI21 Labs, Anthropic, Cohere, Meta, and Stability AI, enabling you to choose the best model for your use case without managing separate endpoints or integrations.

Answer

Automatic model fine-tuning

Answer

Free tier for all models

Answer

Built-in image generation capability

Question 9

A data scientist is using a foundation model to summarize long documents. Which TWO of the following steps are most likely to improve the quality of the summaries?

Accepted Answer

Break the input document into chunks and summarize each chunk separately.. Foundation models have a fixed maximum context window (e.g., 4,096 tokens for GPT-3.5). By breaking a long document into smaller chunks and summarizing each independently, you avoid truncation and ensure the model can process the entire content without losing information. This chunking strategy is a standard preprocessing technique for handling documents that exceed the model's context length.

Answer

Use a high temperature parameter to increase creativity.

Answer

Use a low frequency penalty to reduce repetition.

Answer

Use a longer context length by increasing the max tokens parameter.

Question 10

A marketing team is using a foundation model to generate marketing copy. Which THREE of the following should they consider to ensure responsible and cost-effective use?

Accepted Answer

Bias mitigation to avoid unfair stereotypes. Bias mitigation is essential for responsible AI use; foundation models can perpetuate harmful stereotypes if not carefully monitored, and the marketing team must ensure their generated copy does not unfairly target or misrepresent any group. This aligns with AWS's responsible AI principles, including fairness and avoiding bias in model outputs.

Answer

Model size (number of parameters)

Answer

Latency of model inference

Question 11

A multinational corporation uses a foundation model via Amazon Bedrock to translate internal communication documents from English to multiple languages. They notice that the translations often miss company-specific jargon and acronyms, leading to confusion. The company has a glossary of approved translations for terms like 'Project Atlas' and 'Operation Synergy.' They want to improve translation accuracy quickly and with minimal effort. What approach should they take?

Accepted Answer

Use prompt engineering to include the glossary in each translation request.. Prompt engineering allows the company to inject the glossary directly into the context window of the foundation model with each translation request. This approach requires no model retraining or infrastructure changes, enabling rapid improvement by simply appending the approved translations as instructions or few-shot examples. It is the quickest and least effortful method to enforce company-specific terminology without altering the underlying model.

Answer

Use a larger foundation model that has better language understanding.

Answer

Fine-tune the foundation model on a corpus of bilingual company documents.

Answer

Switch to Amazon Translate with custom terminology.

Question 12

A company is building a chatbot using Amazon Bedrock. They want to ensure the model's responses are grounded in company-specific data and that harmful content is filtered out. Which two services or features should they use? (Choose TWO.)

Accepted Answer

Amazon Kendra. Amazon Kendra is correct because it provides a managed search service that indexes company-specific data sources, enabling the Bedrock chatbot to retrieve relevant documents and ground its responses in authoritative information. Bedrock Guardrails is correct because it allows you to define content filters and topic policies to block harmful or undesirable outputs, ensuring the chatbot adheres to safety and compliance requirements.

Answer

Bedrock Agents

Answer

Amazon Comprehend

Answer

Amazon SageMaker JumpStart

Question 13

A company uses Amazon Bedrock to generate code snippets for internal tools. They notice that the generated code often contains security vulnerabilities such as SQL injection and cross-site scripting. The security team has compiled a comprehensive list of secure coding guidelines and examples of vulnerable patterns. The development team wants to reduce vulnerabilities without significantly slowing down the code generation process. They have tried adding the guidelines to the system prompt, but the model still produces insecure code occasionally. The team is considering additional measures. Which action should they take to most effectively eliminate security vulnerabilities in the generated code?

Accepted Answer

Implement a post-processing step using Amazon CodeGuru or a similar static analysis tool to scan the generated code for vulnerabilities and reject or fix insecure code.. It introduces a deterministic, post-generation validation layer that catches vulnerabilities the model might miss. Amazon CodeGuru Reviewer or similar static analysis tools can scan generated code for patterns like SQL injection and XSS, then reject or fix insecure code without modifying the generation process itself. This approach directly addresses the security team's guidelines while maintaining generation speed, as the model's inference latency is unaffected.

Answer

Use a larger, more expensive foundation model that specializes in code generation.

Answer

Include the complete secure coding guidelines in every prompt.

Answer

Increase the temperature parameter of the foundation model to promote more diverse outputs.

Question 14

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?

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 15

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.