AIF-C01 Fundamentals of Generative AI Practice Test 2 — 15 Questions

Question 1

A bank is using Amazon Bedrock to summarize customer support transcripts. The summaries often contain factual inaccuracies (hallucinations). Which approach is most effective for reducing hallucinations?

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

Accepted Answer

Implement RAG by grounding summarization on retrieved transcripts. Retrieval-Augmented Generation (RAG) grounds the model's output on actual retrieved chunks of the customer support transcripts, providing factual context that reduces the likelihood of hallucination. By retrieving relevant transcript segments and feeding them as context to the LLM, the model generates summaries based on verified source material rather than relying solely on its parametric knowledge, which is the primary cause of factual inaccuracies.

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Decrease the top-p to 0.1

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Increase the model's temperature to make outputs more diverse

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Fine-tune a smaller model on a large dataset of transcripts

Question 2

A media company uses a foundation model on Amazon Bedrock to generate article summaries. The model occasionally omits important details. Which prompt engineering technique is most likely to improve completeness?

Accepted Answer

Include a list of required key points in the prompt. Explicitly listing required key points in the prompt guides the foundation model to cover all specified elements, directly addressing the omission issue. This technique, often called 'constrained generation' or 'structured prompting,' forces the model to attend to each required detail, improving completeness without altering model parameters.

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Use a lower temperature setting

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Increase the max tokens limit

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Add 'Be concise' to the prompt

Question 3

A company fine-tunes a foundation model using SageMaker to create a domain-specific chatbot. After deployment on Bedrock, the model shows high confidence in incorrect answers. What is the most likely cause and its solution?

Accepted Answer

The model is overfitting; apply regularization techniques during fine-tuning. When a fine-tuned model exhibits high confidence in incorrect answers, it is a classic symptom of overfitting, where the model has memorized the training data too closely and fails to generalize to new inputs. Applying regularization techniques such as dropout, weight decay, or early stopping during fine-tuning reduces overfitting by penalizing overly complex decision boundaries, forcing the model to learn more robust patterns. This is the most direct cause and solution given the scenario of a domain-specific chatbot deployed on Bedrock.

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The model was not pre-trained on enough data; use a larger base model

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The training data was imbalanced; collect more diverse data

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The inference temperature is too low; increase it

Question 4

What is a foundation model?

Accepted Answer

A model trained on diverse data that can be adapted to many tasks. A foundation model is a large-scale AI model trained on vast, diverse datasets (e.g., text, images, code) using self-supervised learning, enabling it to be adapted to a wide range of downstream tasks through fine-tuning or few-shot learning. Option C correctly captures this core property of broad adaptability, which distinguishes foundation models from task-specific models. For example, GPT-4 and Claude are foundation models that can handle translation, summarization, and coding without being retrained from scratch.

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A model that only works with tabular data

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A model that requires no additional tuning for new tasks

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A model that is specifically trained for one task, like image classification

Question 5

Which TWO strategies can help reduce inference costs when using Amazon Bedrock? (Select TWO.)

Accepted Answer

Use provisioned throughput for high-volume, predictable workloads. Provisioned throughput allows you to reserve capacity for a specific foundation model, ensuring predictable performance and cost savings for high-volume workloads compared to on-demand pricing. By committing to a certain throughput level, you avoid per-token charges that can accumulate with variable usage, reducing overall inference costs.

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Use a higher temperature setting to generate fewer tokens

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Increase the max tokens to allow longer responses

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Cache frequently used responses in Amazon ElastiCache

Question 6

Which THREE steps are typically involved in fine-tuning a foundation model? (Select THREE.)

Accepted Answer

Prepare a labeled dataset specific to the target domain. Fine-tuning a foundation model requires a labeled dataset specific to the target domain. This dataset provides the supervised signal needed to adjust the model's weights so it can perform well on the downstream task, such as sentiment analysis or legal document summarization.

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Deploy the model immediately without additional training

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Choose a model architecture with more parameters than the base model

Question 7

Refer to the exhibit. A developer runs the CLI command to summarize text using Claude v2 in Bedrock. The output is shorter than expected. Which change should the developer make to allow a longer response?

Accepted Answer

Increase 'max_tokens_to_sample' to 1000. The 'max_tokens_to_sample' parameter in the Bedrock InvokeModel API directly controls the maximum number of tokens the model can generate in its response. By default, this value is often set low (e.g., 256 tokens), which truncates the output. Increasing it to 1000 allows Claude v2 to produce a longer summary up to that token limit.

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Change the prompt to include 'Write a long summary'

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Set 'stop_reason' to 'none'

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Use a different region like us-west-2

Question 8

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":"*"}

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

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

Question 9

A developer is testing different prompts for a text generation model on Amazon Bedrock. Which parameter controls the randomness of the model's output?

Accepted Answer

temperature. Temperature directly controls the randomness of the model's output by scaling the logits before applying the softmax function. A higher temperature (e.g., 1.5) increases randomness and creativity, while a lower temperature (e.g., 0.1) makes the output more deterministic and focused. In Amazon Bedrock, this parameter is a core setting for text generation models like Anthropic Claude and Amazon Titan.

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top_p

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stop_sequences

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max_tokens

Question 10

A company is using Amazon Bedrock to generate marketing copy. They want to ensure the output is safe and appropriate. Which TWO actions should they take? (Choose 2.)

Accepted Answer

Enable content filtering with guardrails. Amazon Bedrock Guardrails allow you to define content filters that block or mask unsafe, inappropriate, or biased text in model outputs. This is a native safety mechanism that operates at inference time, ensuring generated marketing copy meets policy requirements without modifying the underlying model.

Answer

Set temperature to 0 for deterministic output

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Use model fine-tuning with unsafe examples

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Use a private endpoint for Bedrock

Question 11

An organization is evaluating different foundation models (FMs) on Amazon Bedrock for a legal document analysis task. Which THREE factors should they consider when selecting a model? (Choose 3.)

Accepted Answer

Model size (number of parameters). Model size (number of parameters) is a key factor because it directly influences the model's capacity to understand complex legal language, reasoning, and context. Larger models generally offer higher accuracy and nuanced comprehension, but they also require more computational resources and may have higher latency, which must be balanced against the specific requirements of legal document analysis.

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The region where the model is hosted

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Cost per inference call

Question 12

A data scientist is unable to invoke the Claude v2 model from an EC2 instance with IP 10.0.1.5. What is the most likely reason?

Accepted Answer

The resource ARN is incorrect because it should not include the account ID. The most likely reason is that the resource ARN in the IAM policy is incorrect because it should not include the account ID. For Bedrock model invocation, the resource ARN format is `arn:aws:bedrock:region::foundation-model/model-id`, where the account ID is omitted (replaced by a double colon). Including the account ID (e.g., `arn:aws:bedrock:region:account-id:foundation-model/model-id`) makes the ARN invalid, causing the policy to fail and denying the invoke action from the EC2 instance.

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The condition is too restrictive

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The policy does not allow the ec2:AssociateAddress action

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The model is not available in us-east-1

Question 13

A machine learning team is fine-tuning a foundation model using Amazon SageMaker. They need to optimize training time and cost. Which approach should they take?

Accepted Answer

Use Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA. Parameter-Efficient Fine-Tuning (PEFT) techniques like LoRA (Low-Rank Adaptation) significantly reduce the number of trainable parameters by injecting low-rank matrices into the model layers, while keeping the original weights frozen. This drastically lowers memory usage and computational cost, enabling faster training and reduced GPU hours on SageMaker without sacrificing model quality.

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Use a larger instance type with more vCPUs

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Increase the batch size to the maximum possible

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Use the full model weights and train on a single GPU

Question 14

An application uses this configuration to enable RAG. What is required for the knowledge base to function?

Accepted Answer

The knowledge base must have a vector index configured in Amazon OpenSearch Serverless. For a knowledge base to function in a RAG (Retrieval-Augmented Generation) setup on AWS, the knowledge base must have a vector index configured in Amazon OpenSearch Serverless. This vector index stores the embeddings generated from the source documents, enabling efficient similarity search to retrieve relevant context for the agent. Without a vector index, the knowledge base cannot perform the vector search required to fetch relevant document chunks.

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The agent must have internet access to retrieve documents

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The embedding model ARN must include the account ID

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The embedding model must be fine-tuned on the domain data

Question 15

A developer wants to test different prompt variations for a chatbot without making repeated API calls. Which Amazon Bedrock feature can help compare model responses?

Accepted Answer

Amazon Bedrock Playground. Amazon Bedrock Playground provides an interactive console interface where developers can test and compare different prompt variations, model configurations, and foundation models side-by-side without making repeated API calls. It allows real-time experimentation with parameters like temperature, top-p, and prompt engineering to observe how the model responds, making it the correct choice for this use case.

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Model evaluation on Amazon SageMaker

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AWS Security Token Service (STS)

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Amazon CloudWatch Logs