AIF-C01 Practice Test 40 — 15 Questions

Question 1

During a SageMaker training job, the data scientist observes that the loss is not decreasing after the initial few epochs. The model is a deep neural network with ReLU activations. Which hyperparameter adjustment is most likely to help?

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

Accepted Answer

Reduce the learning rate. When loss plateaus after a few epochs with ReLU activations, the model is likely stuck in a region where gradients are small (e.g., near a local minimum or plateau). Reducing the learning rate allows the optimizer to take smaller steps, which can help it navigate out of flat regions and continue decreasing the loss. This is a standard technique to improve convergence when training stalls.

Answer

Increase the number of epochs

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Increase the learning rate

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Decrease the batch size

Question 2

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?

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 3

A developer is using the Amazon Bedrock Converse API to build a multi-turn conversational AI. They need to send a user message along with system instructions and previous conversation history. How should they structure the API request to include both system prompt and message history?

Accepted Answer

Include system prompt in the 'system' parameter and user/assistant messages in the 'messages' array. The Amazon Bedrock Converse API separates system prompts from the conversation history. System prompts are passed in the dedicated 'system' parameter, while user and assistant messages are placed in the 'messages' array. This design allows the model to distinguish persistent instructions from the ongoing dialogue, ensuring system-level guidance is not treated as part of the conversation context.

Answer

Include system prompt as a user message in the 'messages' array

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Include system prompt as an assistant message in the 'messages' array

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Include system prompt in the 'inferenceConfig' parameter

Question 4

A financial services company is deploying a generative AI chatbot to assist customers with account inquiries. The company wants to ensure the chatbot does not generate biased or harmful responses. Which combination of AWS services and practices should the company implement to monitor and mitigate these risks?

Accepted Answer

Use Amazon SageMaker Clarify to detect bias in model outputs and implement a human-in-the-loop workflow with Amazon A2I to review flagged responses.. Amazon SageMaker Clarify is specifically designed to detect bias in machine learning models and their outputs, while Amazon Augmented AI (A2I) enables a human-in-the-loop workflow to review flagged responses. This combination directly addresses the requirement to monitor and mitigate biased or harmful responses from a generative AI chatbot, ensuring responsible AI practices.

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Configure the chatbot to use a pre-trained model from SageMaker JumpStart and disable logging to avoid storing sensitive customer data.

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Use Amazon Rekognition to analyze chat logs for biased language and automatically block responses with a confidence score above 90%.

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Deploy Amazon Lex with built-in sentiment analysis to detect negative customer emotions and automatically escalate to a human agent.

Question 5

A startup wants to generate high-quality images from text descriptions using Amazon Bedrock. They need to create realistic images of products for an e-commerce catalog. Which model provider should they choose?

Accepted Answer

Stability AI. Stability AI is the correct choice because it specializes in image generation models, such as Stable Diffusion, which are designed to produce high-quality, photorealistic images from text prompts. Amazon Bedrock offers Stability AI's models for text-to-image tasks, making it ideal for generating realistic product images for an e-commerce catalog.

Answer

Anthropic

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Mistral AI

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Cohere

Question 6

Refer to the exhibit. A SageMaker training job fails with an 'AccessDenied' error when trying to read files from the S3 bucket 'my-training-data'. The IAM role used by the training job has the policy shown. What is the most likely reason for the failure?

Accepted Answer

The policy does not include the s3:ListBucket action. The IAM policy grants s3:GetObject but not s3:ListBucket. When a SageMaker training job reads files from S3, the SageMaker SDK or framework (e.g., TensorFlow, PyTorch) often performs a ListBucket call first to enumerate objects in the prefix. Without s3:ListBucket, the SDK cannot discover the files, resulting in an AccessDenied error even though GetObject is allowed.

Answer

The bucket policy requires encryption in transit

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The training job is using the wrong AWS region

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The policy does not include the s3:PutObject action

Question 7

A data scientist is using Amazon SageMaker to train a deep learning model for image classification. The training job is taking too long. The dataset consists of 100,000 images stored in Amazon S3. Which action can the data scientist take to reduce training time without modifying the model architecture?

Accepted Answer

Use a GPU instance type for training.. GPU instances are specifically designed for parallel processing of matrix operations, which are fundamental to deep learning training. By switching to a GPU instance type (e.g., p3 or p4d families) in SageMaker, the data scientist can significantly accelerate the training of the image classification model without altering the model architecture, as the dataset of 100,000 images benefits from GPU's massive parallelism for forward and backward passes.

Answer

Convert images to CSV format before training.

Answer

Enable checkpointing to save intermediate models.

Answer

Reduce the number of training epochs.

Question 8

Which TWO of the following are best practices for preparing training data for a machine learning model?

Accepted Answer

Handle missing values by imputing or removing them.. Handling missing values is a critical data preprocessing step. Missing data can introduce bias or cause algorithms to fail. Imputation (e.g., using mean, median, or model-based methods) or removal of rows/columns with missing values ensures the dataset is complete and suitable for training, preventing errors during model fitting.

Answer

Remove all outliers to improve model robustness.

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Use the entire dataset for training to maximize data usage.

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Avoid shuffling the data to preserve original order.

Question 9

A data science team uses Amazon SageMaker to train models. To comply with SOC 2, they must ensure that access to training data is logged, that the data is encrypted at rest, and that model training jobs are isolated from each other. Which THREE actions should they take? (Choose three.)

Accepted Answer

Enable server-side encryption on the S3 bucket containing training data using SSE-KMS.. Enabling server-side encryption on the S3 bucket containing training data using SSE-KMS ensures data at rest is encrypted, which is a direct requirement for SOC 2 compliance. SSE-KMS provides envelope encryption with a customer-managed AWS KMS key, allowing fine-grained access control and audit trails for the encryption keys.

Answer

Enable Amazon Inspector to scan training instances for vulnerabilities.

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Use SageMaker Debugger to monitor training jobs.

Question 10

A company is deploying a generative AI application using Amazon Bedrock and needs to optimize costs. They expect variable traffic with occasional high spikes. Which TWO strategies would help reduce costs while maintaining performance?

Accepted Answer

Enable model caching to serve repeated prompts without re-inference. Model caching reduces cost for repeated queries, and batch inference is cheaper for non-real-time workloads. Provisioned throughput is expensive and not cost-optimal for variable traffic. Fine-tuning and using the largest model increase costs.

Answer

Always use the largest available foundation model for quality

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Use provisioned throughput to handle spikes

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Fine-tune a larger model for better accuracy

Question 11

A healthcare startup is using Amazon SageMaker to train a model on patient data. They need to ensure that the training data does not contain any personally identifiable information (PII) before being used. Which AWS service can automatically detect and report PII in the data stored in S3?

Accepted Answer

Amazon Macie. Amazon Macie uses machine learning to automatically discover, classify, and protect sensitive data in S3. It can detect PII such as names, addresses, and health information.

Answer

Amazon Rekognition

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AWS Glue DataBrew

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Amazon Comprehend Medical

Question 12

A startup wants to generate product descriptions from a few keywords using a foundation model. They need a fully managed serverless solution that requires no infrastructure setup. Which AWS service should they use?

Accepted Answer

Amazon Bedrock. Amazon Bedrock is a fully managed serverless service that provides access to foundation models (FMs) from leading AI providers via a simple API, making it ideal for generating product descriptions from keywords without any infrastructure management. It directly supports generative AI tasks like text generation, unlike other AWS services that focus on different ML or NLP capabilities.

Answer

Amazon SageMaker

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Amazon Comprehend

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AWS Lambda

Question 13

Refer to the exhibit. A developer deploys this CloudFormation stack but the agent fails to query the knowledge base. What is a likely cause?

Accepted Answer

The agent role does not have permissions to invoke the knowledge base. The agent role must have an IAM policy that grants the `bedrock:Retrieve` and `bedrock:RetrieveAndGenerate` permissions on the knowledge base. Without these permissions, the agent cannot invoke the knowledge base, even if the KnowledgeBaseId is correctly passed and the embedding model is available.

Answer

The KnowledgeBaseId is not passed correctly

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The embedding model is not available in the region

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The OpenSearch collection type should be SEARCH not VECTORSEARCH

Question 14

A company is using Amazon Bedrock to build a text-to-SQL application. They want to ensure that the generated SQL queries are valid and safe. Which approach is BEST?

Accepted Answer

Configure a guardrail to filter and validate the generated SQL. Amazon Bedrock guardrails provide a native, configurable mechanism to filter and validate model outputs, including SQL queries, against defined policies such as regex patterns, denied topics, and content filters. This approach directly addresses both validity and safety without requiring additional model training or external validation services, making it the most integrated and efficient solution for ensuring generated SQL is syntactically correct and free of harmful operations like DROP or DELETE.

Answer

Fine-tune the model on a dataset of valid SQL queries

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Use a separate model to validate the SQL after generation

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Limit the max_tokens to 50 to reduce complexity

Question 15

A company uses Amazon Bedrock to deploy a foundation model for a real-time chat application. Users report that responses are slow. Which optimization is MOST likely to reduce latency without degrading quality?

Accepted Answer

Enable response streaming using the Converse API or InvokeModelWithResponseStream. Enabling response streaming with the Converse API or InvokeModelWithResponseStream allows the model to send tokens to the client as they are generated, rather than waiting for the full response. This reduces the user's perceived latency because the first token appears much sooner, even though the total generation time remains similar. This optimization directly addresses the real-time chat requirement without degrading output quality.

Answer

Use a larger model variant to improve inference speed

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Increase the temperature to make the model generate faster

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Switch from a text generation model to an embedding model