AIF-C01 Practice Test 4 — 10 Questions

Question 1

A company is using Amazon Bedrock's Converse API to build a conversational agent. They want the agent to maintain context across multiple turns. The agent should also be able to call external APIs to retrieve real-time data when needed. Which combination of features should they use?

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

Accepted Answer

Use the Converse API with system prompts and tool configuration. The Converse API is designed for multi-turn conversations and natively supports system prompts to set agent behavior and tool configuration for invoking external APIs. Option C correctly combines these features to maintain context across turns and enable real-time data retrieval via tool calls, which is exactly what the scenario requires.

Answer

Use the Converse API with streaming enabled and set max tokens to 4096

Answer

Use the InvokeModel API with a conversational prompt template and tool use configuration

Answer

Use the InvokeModel API with a system prompt and store conversation history client-side

Question 2

Which of the following is a key advantage of using a diffusion model for image generation compared to a GAN?

Accepted Answer

Diffusion models produce more diverse and higher-quality images with stable training. Diffusion models offer a key advantage over GANs because they are trained with a stable, non-adversarial objective—denoising score matching—which avoids the mode collapse and training instability common in GANs. This leads to more diverse outputs and, with sufficient steps, higher-quality images that can rival or exceed GANs, especially in large-scale text-to-image tasks.

Answer

Diffusion models generate images faster than GANs during inference

Answer

Diffusion models are inherently conditional and do not require labels

Answer

Diffusion models require less training data than GANs

Question 3

What is the main purpose of a system prompt in a large language model?

Accepted Answer

To define the high-level instructions that set the model's behavior and persona. The system prompt in a large language model (LLM) defines the high-level instructions that set the model's behavior, persona, and constraints for the entire conversation. Unlike user prompts, which are task-specific, the system prompt establishes the model's role (e.g., 'You are a helpful assistant') and governs how it interprets all subsequent interactions, ensuring consistent and aligned outputs.

Answer

To increase the temperature for more creative responses

Answer

To provide an example of the desired output format

Answer

To list all possible tokens to be used in the response

Question 4

A data scientist is using Amazon Bedrock to generate product descriptions. They notice the output is often repetitive and lacks creativity. Which combination of parameter adjustments is MOST likely to produce more diverse and less repetitive output?

Accepted Answer

Increase temperature and increase top-p. Increasing temperature raises the probability of sampling lower-probability tokens, which increases randomness and diversity. Increasing top-p (nucleus sampling) expands the set of tokens considered for sampling, further reducing repetitiveness. Together, these adjustments encourage the model to explore a wider range of possible continuations, producing more creative and less repetitive output.

Answer

Decrease temperature and decrease top-p

Answer

Decrease temperature and increase top-p

Answer

Increase temperature and decrease top-p

Question 5

A developer needs to ensure that a generative AI application on Amazon Bedrock does not produce harmful or inappropriate content. Which feature should they configure?

Accepted Answer

Guardrails for Amazon Bedrock. Guardrails for Amazon Bedrock is the correct feature because it allows developers to define policies that filter and block harmful or inappropriate content in both user inputs and model outputs. This includes configurable thresholds for topics, content filters (e.g., hate, insults, sexual, violence), and denied topics, ensuring the generative AI application adheres to safety and responsible AI requirements.

Answer

Provisioned Throughput

Answer

Model Invocation Logging

Answer

Knowledge Bases for Amazon Bedrock

Question 6

Refer to the exhibit. A developer receives an error when trying to invoke the Claude Instant model from an application. The application uses the IAM role 'MyAppRole'. Which IAM policy statement should be added to the role to resolve the error?

Accepted Answer

{"Effect":"Allow","Action":"bedrock:InvokeModel","Resource":"arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-instant-v1"}. The error occurs when the application tries to invoke the Claude Instant model via the Bedrock InvokeModel API, and the IAM role 'MyAppRole' lacks the necessary permission. The required action is 'bedrock:InvokeModel', and the resource must be scoped to the specific foundation model ARN, which for Claude Instant in us-east-1 is 'arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-instant-v1'. This grants least-privilege access to invoke exactly that model.

Answer

{"Effect":"Allow","Action":"bedrock:GetFoundationModel","Resource":"*"}

Answer

{"Effect":"Allow","Action":"bedrock:InvokeModel","Resource":"arn:aws:bedrock:us-east-1::foundation-model/*"}

Answer

{"Effect":"Allow","Action":"bedrock:*","Resource":"*"}

Question 7

Which TWO practices help ensure responsible AI when deploying generative AI applications? (Select TWO.)

Accepted Answer

Implement guardrails to filter harmful or inappropriate content. Option D is correct because implementing guardrails—such as content filters, prompt shields, and safety classifiers—directly mitigates the risk of generative AI producing harmful, offensive, or inappropriate content, which is a core requirement of responsible AI deployment. Option E is correct because continuously monitoring model outputs for bias and drift ensures that the system remains fair and accurate as data distributions and user behavior change over time, enabling timely remediation. Option A is incorrect because removing content filters increases the risk of harmful outputs and violates responsible AI principles rather than ensuring them. Option B is incorrect because increasing model size at the cost of interpretability reduces transparency and explainability, which are key responsible AI goals. Option C is incorrect because using only synthetic data does not by itself guarantee privacy or fairness and can introduce its own biases and quality issues.

Answer

Deploy the model without any content filters to maximize creativity

Answer

Increase model size to improve accuracy at the expense of interpretability

Answer

Use only synthetic data for training to avoid privacy issues

Question 8

A developer is building a chatbot using Amazon Bedrock and Claude. They notice that the model sometimes generates harmful or biased responses. Which AWS service can they use to implement guardrails?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is the correct choice because it is a native feature of Amazon Bedrock designed specifically to implement safety controls, content filters, and topic policies for foundation models like Claude. It allows developers to define denied topics, filter harmful content (e.g., hate speech, violence), and redact sensitive information, directly addressing the need to prevent harmful or biased responses in a chatbot built on Bedrock.

Answer

AWS WAF

Answer

Amazon GuardDuty

Answer

AWS Shield

Question 9

A financial services company is deploying a generative AI application using Amazon Bedrock. They need to ensure that the model does not generate responses containing personally identifiable information (PII) such as credit card numbers or Social Security numbers. The company also wants to block certain topics like investment advice. Which feature should they configure?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails is the correct feature because it provides configurable safeguards to filter and block undesirable content in foundation model responses, including PII (e.g., credit card numbers, Social Security numbers) and specific topics like investment advice. It operates at the application layer within Bedrock, allowing you to define denied topics and sensitive information filters that are enforced during inference, without requiring separate infrastructure or manual post-processing.

Answer

AWS WAF

Answer

Amazon Macie

Answer

AWS Identity and Access Management (IAM) policies

Question 10

Which Amazon Titan model is specifically designed to convert text into numerical vectors for use in semantic search and Retrieval-Augmented Generation (RAG)?

Accepted Answer

Amazon Titan Embeddings. Amazon Titan Embeddings is the correct model because it is specifically designed to convert text into numerical vectors (embeddings) that capture semantic meaning. These vectors are essential for semantic search and Retrieval-Augmented Generation (RAG), where they enable similarity comparisons and efficient retrieval of relevant documents from a vector database.

Answer

Amazon Titan Text Express

Answer

Amazon Titan Multimodal Embeddings

Answer

Amazon Titan Image Generator