AIF-C01 Fundamentals of Generative AI — 10 Questions

Question 1

A company is using Amazon SageMaker JumpStart to deploy a pre-trained text generation model. After deployment, the model produces slow inference responses. Which action is most likely to improve inference latency?

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

Accepted Answer

Deploy the model on a more powerful instance type with higher GPU memory.. Deploying the model on a more powerful instance type with higher GPU memory directly addresses the computational bottleneck causing slow inference. A larger GPU provides more CUDA cores and memory bandwidth, enabling faster matrix operations and reducing the time per forward pass for the pre-trained text generation model.

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Quantize the model weights to FP16 or INT8.

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Fine-tune the model on a smaller dataset.

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Increase the batch size for inference requests.

Question 2

A company is building a chatbot using Amazon Bedrock. They want to ensure the model's responses are grounded in their internal knowledge base and avoid generating information outside that scope. Which feature should they use?

Accepted Answer

Amazon Bedrock Knowledge Bases. Amazon Bedrock Knowledge Bases is the correct feature because it allows you to connect a foundation model (FM) to your internal data sources, such as documents or databases, and use Retrieval Augmented Generation (RAG) to ground responses in that specific knowledge. This ensures the chatbot only generates information from the provided knowledge base, preventing hallucinations or out-of-scope content.

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Agents for Amazon Bedrock

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Model Evaluation on Amazon Bedrock

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Guardrails for Amazon Bedrock

Question 3

A company is using Amazon Bedrock to generate creative marketing copy. They want to reduce the randomness of the output while maintaining diversity. Which TWO parameters should they adjust?

Accepted Answer

Decrease the top_p value. Option E (Decrease the temperature) is correct because temperature scales the randomness of token sampling in Amazon Bedrock; lowering it makes the probability distribution sharper, so the model favors high-probability tokens and produces more deterministic, less random output. Option D (Decrease the top_p value) is correct because top_p (nucleus sampling) restricts generation to the smallest set of tokens whose cumulative probability exceeds p; reducing p narrows that candidate pool, cutting off low-probability tokens and reducing randomness while still allowing varied word choices within the nucleus, which preserves diversity. Option A (Increase the temperature) is wrong because raising temperature flattens the distribution and increases randomness, the opposite of the goal. Option B (Increase the max token count) is wrong because max tokens only caps output length and does not affect sampling randomness. Option C (Increase the top_k value) is wrong because a larger top_k widens the candidate token set, increasing rather than reducing randomness.

Answer

Increase the temperature

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Increase the max token count

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Increase the top_k value

Question 4

A developer attached this IAM policy to a role used by an application that invokes Claude v2 in us-east-1. The application receives an access denied error. What is the MOST likely cause?

Accepted Answer

The Deny statement is blocking requests because the condition does not match the resource ARN's region. The Deny statement uses a `StringNotEquals` condition on `aws:RequestedRegion` set to `us-east-1`. This means the Deny applies to any request where the requested region is NOT `us-east-1`. Since the resource ARN in the Deny statement is `arn:aws:bedrock:us-east-1::foundation-model/anthropic.claude-v2`, the condition does not match the resource's region (the resource ARN itself is in us-east-1), but the Deny is triggered when the request is made to a different region, blocking the call. The application is likely invoking the model from a region other than us-east-1, causing the Deny to take effect.

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The Allow statement does not include a condition on the region

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The Deny statement uses StringNotEquals instead of StringEquals

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The resource ARN in the Allow statement is incorrect

Question 5

A financial services company is deploying a generative AI model on Amazon SageMaker for real-time fraud detection. The model, a fine-tuned Llama 2 7B, must respond to transaction requests within 500 milliseconds. The team has deployed the model using a SageMaker real-time endpoint with a single ml.g5.2xlarge instance. During load testing, the endpoint achieves an average latency of 450 ms at 10 requests per second (RPS), but the latency spikes to over 2 seconds at 20 RPS. The team needs to maintain sub-500 ms latency at up to 50 RPS. The model is too large to fit on a single GPU, so they are using CPU instances. They considered using a larger instance type but want to minimize cost. What should the team do to meet the latency requirement cost-effectively?

Accepted Answer

Use a SageMaker multi-model endpoint with multiple ml.g5.xlarge instances and auto scaling. A SageMaker multi-model endpoint (MME) allows multiple model replicas to be hosted on a fleet of instances, enabling horizontal scaling to handle increased throughput. By using multiple ml.g5.xlarge instances with auto scaling, the team can distribute the 50 RPS load across several instances, keeping per-instance latency low while minimizing cost compared to a single larger instance. This approach also leverages the fact that the model is too large for a single GPU but can be efficiently served on CPU instances with proper load distribution.

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Upgrade to a single ml.g5.4xlarge instance

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Attach an Amazon Elastic Inference accelerator to the existing instance

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Use SageMaker Serverless Inference to automatically scale

Question 6

Which TWO factors are most important when selecting a foundation model for a sentiment analysis task? (Choose 2)

Accepted Answer

Composition of the model's training data (domain, language). Option A is correct because the composition of the model's training data—its domain and language coverage—directly determines how well the model's learned representations align with the vocabulary, tone, and idioms of the sentiment-bearing text you will feed it, so a model pretrained on matching domains and languages will yield far more accurate sentiment classifications. Option D is correct because model size (number of parameters) is a primary driver of the model's capacity to capture nuanced sentiment, sarcasm, and contextual polarity; larger models generally achieve higher accuracy on complex sentiment tasks, though they demand more compute. Option B is not a primary selection factor because API pricing per invocation is a cost/operational concern, not a determinant of whether the model can perform sentiment analysis well. Option C is not primary because inference latency affects throughput and user experience but does not govern the quality of sentiment predictions. Option E is irrelevant since the color scheme of documentation has no bearing on model capability or task performance.

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API pricing per invocation

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Inference latency

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Color scheme of the model's documentation

Question 7

A user has this IAM policy and attempts to invoke the model in the us-west-2 region. They receive an AccessDenied error. What is the reason?

Accepted Answer

The policy does not allow the us-west-2 region. The policy explicitly denies access to the 'us-west-2' region via the 'Deny' effect with a condition on 'aws:RequestedRegion'. Since the user is attempting to invoke the model in us-west-2, the request matches the deny condition, resulting in an AccessDenied error. The policy does not grant any explicit allow for that region, so the default implicit deny combined with the explicit deny blocks the action.

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The resource ARN is malformed

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The action 'bedrock:InvokeModel' is incorrect

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The model ID is case-sensitive

Question 8

A company is building a customer service chatbot using Amazon Bedrock. Which component of a foundation model determines the creativity and randomness of the generated responses?

Accepted Answer

Temperature. In Amazon Bedrock, the temperature parameter controls the creativity and randomness of a foundation model's generated responses. A higher temperature (e.g., 1.0) makes the output more diverse and creative, while a lower temperature (e.g., 0.2) makes it more deterministic and focused. This directly influences the randomness of token selection.

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Prompt template

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Max tokens

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Top-p

Question 9

A data scientist is deploying a fine-tuned Mistral model on Amazon Bedrock. After deployment, inference latency is too high for real-time applications. Which configuration change can reduce latency without significantly impacting output quality?

Accepted Answer

Reduce max tokens from 1024 to 256. Reducing max tokens from 1024 to 256 directly decreases the number of tokens the model generates per request, which lowers the computational load and memory usage during inference. Since the model only processes up to the specified token limit, this reduces latency proportionally while preserving output quality for short-form responses, as the model's core reasoning and generation capabilities remain unchanged.

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Switch to a larger model variant

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Decrease the top-p to 0.5

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Increase the temperature to 0.9

Question 10

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