AIF-C01 Practice Test 44 — 15 Questions

Question 1

An organization wants to use Amazon Rekognition to analyze images of people for a security application. They must comply with GDPR. What is the best practice?

Accepted Answer

Ensure all images are anonymized before analysis. GDPR requires that personal data, including facial images, be processed lawfully and with appropriate safeguards. Anonymizing images before analysis with Amazon Rekognition ensures that the data cannot be linked back to an identifiable person, thereby reducing GDPR compliance risk. This aligns with the principle of data minimization and privacy by design.

Answer

Store images indefinitely for audit

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Use celebrity recognition

Answer

Use face detection only

Question 2

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

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The temperature was too high

Answer

The model lacks knowledge about capitals

Question 3

A developer is using the Amazon Bedrock InvokeModel API with the above request to summarize meeting notes. The response is a single word repeated many times. Which parameter is MOST likely causing this issue?

Accepted Answer

temperature set to 0. A temperature of 0 forces the model to always select the highest-probability token at each step, which can lead to repetitive loops if the most likely token repeatedly points back to itself (e.g., the same word). This deterministic behavior eliminates randomness, causing the model to get stuck in a single-word cycle rather than generating diverse or coherent text.

Answer

topP set to 0.9

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stopSequences is empty

Answer

maxTokenCount set to 100

Question 4

A healthcare startup deploys a model to predict patient readmission risk using Amazon SageMaker. After deployment, the model shows higher false-positive rates for a specific age group. What is the most responsible first step?

Accepted Answer

Use Amazon SageMaker Clarify to detect bias in predictions. Amazon SageMaker Clarify is purpose-built for detecting bias in ML models and data. It provides bias metrics (e.g., Difference in Positive Proportions in Predicted Labels, Disparate Impact) that can quantify whether the model's predictions are systematically skewed against a specific age group. This is the most responsible first step because it objectively measures the bias before any corrective action is taken.

Answer

Increase the prediction threshold for the affected group

Answer

Retrain the model with more data from the affected group

Answer

Immediately retire the model to prevent harm

Question 5

A retail company uses a recommendation system that occasionally suggests inappropriate products to minors. Which responsible AI practice should be applied?

Accepted Answer

Implement human review of flagged recommendations. The correct practice is to implement human review of flagged recommendations. This aligns with the responsible AI principle of accountability, where automated systems must have oversight mechanisms to catch and correct inappropriate outputs, especially when minors are involved. Human-in-the-loop (HITL) validation ensures that edge cases or subtle context (e.g., age-inappropriate product suggestions) are caught before they reach end users, rather than relying solely on automated filters or feedback loops.

Answer

Rely solely on user feedback to improve

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Disable the recommendation system entirely

Answer

Increase the volume of training data

Question 6

A developer invoked an Amazon Bedrock model and received the following error: 'ValidationException: 1 validation error detected: Value 'claude-instant-v1' at 'modelId' failed to satisfy constraint: Member must satisfy enum value set: [ai21.j2-mid-v1, amazon.titan-text-lite-v1, anthropic.claude-v2, ...]'. What is the likely cause?

Accepted Answer

The modelId is not part of the allowed enum of models for the account. The error message explicitly states that the value 'claude-instant-v1' at 'modelId' failed to satisfy the constraint: 'Member must satisfy enum value set'. This indicates that the modelId provided is not part of the allowed list of model identifiers that the Amazon Bedrock API accepts for the invocation request. The error is a validation error from the API itself, not a permissions or availability issue, meaning the modelId string does not match any entry in the predefined enum of supported models.

Answer

The Lambda function does not have the necessary IAM permissions

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The modelId is not available in the current AWS region

Answer

The modelId is deprecated and has been renamed

Question 7

An organization uses SageMaker JumpStart to deploy a foundation model for real-time inference. They observe high latency. What is the most effective way to reduce latency?

Accepted Answer

Compile the model with SageMaker Neo. SageMaker Neo compiles the model to optimize it for the target hardware, reducing inference latency by applying hardware-specific optimizations such as kernel fusion, quantization, and memory layout tuning. This directly addresses the high latency issue for real-time inference without changing the instance type or inference mode.

Answer

Use a larger instance with more memory

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Use batch transform instead

Answer

Enable SageMaker Inference Recommender

Question 8

A deployed model on an Amazon SageMaker endpoint is experiencing high inference latency (average 500ms) during peak hours. The model is a deep neural network with 10 million parameters. The endpoint uses a single ml.c5.xlarge instance. The company wants to reduce latency to under 200ms without retraining or changing the model architecture. Which action should they take?

Accepted Answer

Use SageMaker Neo to compile and optimize the model. SageMaker Neo compiles trained models into an optimized format for the target hardware, reducing inference latency without altering the model architecture. For a deep neural network with 10 million parameters on a CPU instance, Neo applies hardware-specific optimizations like operator fusion and memory layout tuning, which can significantly lower latency. This directly addresses the requirement to reduce latency from 500ms to under 200ms without retraining or changing the model.

Answer

Enable automatic scaling to add more instances

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Switch to a GPU-based instance type like ml.p2.xlarge

Answer

Deploy the model on a multi-model endpoint

Question 9

A company wants to build a customer support chatbot that answers questions based on a large internal knowledge base. Which AWS service is most suitable for implementing RAG to retrieve relevant documents?

Accepted Answer

Amazon Kendra. Amazon Kendra is a highly accurate enterprise search service that can retrieve relevant documents from various sources, which can then be provided to a foundation model for generation. Lex, Connect, and Polly are not designed for document retrieval.

Answer

Amazon Lex

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Amazon Polly

Answer

Amazon Connect

Question 10

Refer to the exhibit. A developer sees this error when calling Amazon Bedrock for inference. What is the MOST likely cause and recommended solution?

Accepted Answer

The request rate exceeds the model's throughput limit; implement retries with exponential backoff. The error indicates a throttling exception from Amazon Bedrock, which occurs when the request rate exceeds the model's throughput limit. The recommended solution is to implement retries with exponential backoff to handle transient rate limits gracefully, as this aligns with AWS best practices for managing API call limits.

Answer

The model ID is incorrect; use a different model

Answer

The prompt is too long; reduce the number of tokens in the prompt

Answer

Increase the max_tokens_to_sample value

Question 11

An enterprise deploys a foundation model on Amazon Bedrock with a knowledge base. Users report that the model is returning outdated information. What is the most likely cause?

Accepted Answer

The knowledge base data source is not refreshed. When a knowledge base is attached to a foundation model on Amazon Bedrock, the model retrieves information from the data source to augment its responses. If the data source is not refreshed, the model will return outdated information even if the model itself is current. Option C directly addresses this by identifying the stale data source as the root cause.

Answer

The model was fine-tuned

Answer

The model is not the latest version

Answer

The inference parameters are incorrect

Question 12

A team is deploying a generative AI model for medical report generation. They must ensure patient data privacy and comply with HIPAA. Which AWS service feature is essential for de-identifying protected health information (PHI) before sending data to a foundation model?

Accepted Answer

Amazon Comprehend Medical. Amazon Comprehend Medical is the correct service because it is specifically designed to extract and de-identify protected health information (PHI) from unstructured medical text using natural language processing (NLP). It can detect entities such as patient names, dates, and medical record numbers, and then redact or replace them before the data is sent to a foundation model, ensuring HIPAA compliance.

Answer

AWS CloudHSM

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Amazon Macie

Answer

AWS Key Management Service (AWS KMS)

Question 13

A company uses Amazon Rekognition for facial analysis. They want to ensure the model doesn't exhibit bias based on skin tone. What should they do?

Accepted Answer

Ensure the training dataset includes diverse skin tones. Bias in facial analysis models, such as those used by Amazon Rekognition, often stems from imbalanced training data. By ensuring the training dataset includes diverse skin tones, the model learns to recognize features across all demographic groups, reducing performance disparities and promoting fairness. This directly addresses the root cause of bias in machine learning models.

Answer

Apply data augmentation to increase dataset size

Answer

Use a larger neural network

Answer

Use a pre-trained model from AWS Marketplace

Question 14

A financial services firm is deploying a loan approval model and must comply with the EU AI Act, which classifies credit scoring as a high-risk AI system. Which combination of actions is required for such high-risk systems under the regulation?

Accepted Answer

Conduct a fundamental rights impact assessment and implement a human-in-the-loop review process. The EU AI Act requires high-risk systems to have human oversight (Article 14), technical documentation, risk management, and transparency. Among the options, only the one including human review and documentation meets these requirements.

Answer

Implement Amazon Rekognition to monitor model inputs and outputs

Answer

Use SageMaker Model Monitor to detect data drift and retrain automatically

Answer

Obtain an ISO 27001 certification and deploy the model on AWS Outposts

Question 15

A company is building a chatbot using Amazon Bedrock and wants to ensure that the model generates responses consistent with its brand voice. Which technique should be used to provide the model with examples of desired responses without fine-tuning the model?

Accepted Answer

Include few-shot examples in the system prompt to demonstrate the desired tone.. Few-shot prompting allows you to provide the model with examples of desired responses directly in the system prompt, guiding the model's tone and style without modifying its underlying weights. This technique is ideal for brand voice consistency when fine-tuning is not an option, as it leverages in-context learning to influence output behavior.

Answer

Fine-tune the model on a dataset of brand-compliant conversations.

Answer

Use prompt chaining to break down the conversation into multiple steps.

Answer

Implement a Retrieval Augmented Generation (RAG) system with brand documents.