AIF-C01 Fundamentals of Generative AI Practice Test 5 — 15 Questions

Question 1

A startup is building an AI-powered code assistant using a large language model (LLM). They want to ensure the model generates syntactically correct code and avoids security vulnerabilities. Which technique should they prioritize?

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

Accepted Answer

Augment prompts with few-shot examples of secure coding practices and unit tests. Few-shot prompting with examples of secure coding practices and unit tests directly shapes the model's output distribution toward syntactically valid, security-conscious code by conditioning on concrete in-context demonstrations. This is a prompt-engineering technique that requires no retraining and can be updated instantly as new vulnerability patterns emerge. It simultaneously addresses both stated goals: syntactic correctness (via code examples) and security (via secure-coding exemplars).

Answer

Deploy the model with max tokens set to 4096

Answer

Fine-tune the model on a large corpus of open-source code

Answer

Use chain-of-thought prompting to explain reasoning before code generation

Question 2

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?

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.

Answer

Decrease the top-p to 0.1

Answer

Increase the model's temperature to make outputs more diverse

Answer

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

Question 3

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.

Answer

Use a lower temperature setting

Answer

Increase the max tokens limit

Answer

Add 'Be concise' to the prompt

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.

Answer

A model that only works with tabular data

Answer

A model that requires no additional tuning for new tasks

Answer

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. Option C is correct because Provisioned Throughput on Amazon Bedrock lets you purchase dedicated model units at a fixed hourly rate for steady, high-volume, predictable inference traffic, which is typically cheaper than paying on-demand per-token prices once utilization is high. Option E is correct because choosing a smaller foundation model variant (for example, a mini or lite version of a model family) reduces the number of parameters and compute required per inference, directly lowering the per-token or per-request cost while often remaining sufficient for the task. Option A is not correct because temperature controls randomness of sampling, not the number of tokens generated, so raising it does not reduce cost and may even increase output variability. Option B is not correct because increasing max tokens allows longer responses, which increases the number of output tokens billed and therefore raises cost. Option D is not correct because Amazon ElastiCache is a caching service, not a native Bedrock cost-reduction strategy, and caching model responses is not one of the two intended Bedrock cost-optimization approaches in this scenario.

Answer

Use a higher temperature setting to generate fewer tokens

Answer

Increase the max tokens to allow longer responses

Answer

Cache frequently used responses in Amazon ElastiCache

Question 6

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 7

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 begins with selecting an appropriate pre-trained foundation model as the starting point (D), since the whole point of fine-tuning is to adapt existing general-purpose weights rather than train from scratch. Next, you must prepare a labeled dataset specific to the target domain (B), because supervised fine-tuning requires task-relevant input-output pairs to steer the model toward the desired behavior. Then you train the model on that domain dataset with a lower learning rate (C), which is standard practice to avoid catastrophic forgetting and to gently nudge the pre-trained weights instead of overwriting them. Option A is incorrect because deploying the model immediately without additional training is the opposite of fine-tuning—it describes using the base model as-is. Option E is incorrect because fine-tuning does not require choosing an architecture with more parameters than the base model; you typically fine-tune the same pre-trained architecture, and increasing parameter count is not a fine-tuning step.

Answer

Deploy the model immediately without additional training

Answer

Choose a model architecture with more parameters than the base model

Question 8

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.

Answer

Change the prompt to include 'Write a long summary'

Answer

Set 'stop_reason' to 'none'

Answer

Use a different region like us-west-2

Question 9

Refer to the exhibit. A company sets up a knowledge base for a customer support chatbot using Amazon Bedrock. Users report that the chatbot misses relevant details from long documents. Which change to the data source configuration would most likely improve retrieval?

Accepted Answer

Change chunking strategy to SEMANTIC. Semantic chunking groups text based on meaning rather than fixed token counts, preserving the natural boundaries of concepts and paragraphs. This ensures that relevant details from long documents remain intact within a single chunk, improving retrieval accuracy for the chatbot.

Answer

Increase the chunk size in FIXED_SIZE chunking

Answer

Add more documents to the S3 bucket

Answer

Change the embedding model to a larger one

Question 10

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 11

A company wants to build a generative AI application that can summarize customer support tickets. They need to ensure the model stays up-to-date with the latest product documentation without retraining. Which AWS service would best support this requirement?

Accepted Answer

Amazon Bedrock with Retrieval Augmented Generation (RAG). Amazon Bedrock with Retrieval Augmented Generation (RAG) is the correct choice because it allows the generative AI model to access and incorporate the latest product documentation from an external knowledge base without retraining. RAG works by retrieving relevant document chunks at inference time and injecting them into the model's context, ensuring responses reflect current information. This directly meets the requirement for staying up-to-date with evolving documentation while avoiding the cost and latency of full model retraining.

Answer

Amazon Comprehend

Answer

Amazon Rekognition

Answer

Amazon SageMaker Ground Truth

Question 12

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.

Answer

top_p

Answer

stop_sequences

Answer

max_tokens

Question 13

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. Option A is correct because Amazon Bedrock Guardrails provide configurable content filters (for hate, violence, sexual, insults, misconduct, and prompt-attack categories) that detect and block unsafe or inappropriate model output, directly addressing the goal of safe marketing copy. Option E is correct because implementing human review of all generated content adds a human-in-the-loop control that catches context-dependent or brand-inappropriate material that automated filters may miss, ensuring appropriateness before publication. Option B is incorrect because temperature controls randomness/creativity of sampling, not safety; setting it to 0 only makes output more deterministic and does nothing to filter harmful content. Option C is incorrect because fine-tuning with unsafe examples would teach the model unsafe patterns, worsening rather than improving safety. Option D is incorrect because a private endpoint (VPC endpoint/PrivateLink) only secures network connectivity to Bedrock and does not evaluate or filter the safety of generated text.

Answer

Set temperature to 0 for deterministic output

Answer

Use model fine-tuning with unsafe examples

Answer

Use a private endpoint for Bedrock

Question 14

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.

Answer

Use a larger instance type with more vCPUs

Answer

Increase the batch size to the maximum possible

Answer

Use the full model weights and train on a single GPU

Question 15

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.

Answer

The agent must have internet access to retrieve documents

Answer

The embedding model ARN must include the account ID

Answer

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