AIF-C01 Practice Test 41 — 15 Questions

Question 1

A company is using Amazon Bedrock to build a generative AI application. The company wants to prevent the model from generating toxic or harmful content while still allowing creative responses. Which feature should the company enable?

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

Accepted Answer

Amazon Bedrock Guardrails with content filters.. Amazon Bedrock Guardrails with content filters is the correct feature because it allows the company to define and enforce policies that block toxic or harmful content in model inputs and outputs, while still permitting creative responses within safe boundaries. This feature provides configurable thresholds for content categories like hate, insults, and sexual content, enabling precise control over model behavior without restricting overall creativity.

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AWS Key Management Service (KMS) to encrypt model responses.

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AWS Identity and Access Management (IAM) policies to restrict model output.

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Amazon CloudWatch Logs to monitor and block harmful content.

Question 2

A startup wants to build a product recommendation engine for their e-commerce platform. They have user purchase history and item metadata. They want a fully managed solution that can automatically train and deploy a recommendation model without needing to manage the underlying ML lifecycle. The solution should provide personalized recommendations based on collaborative filtering. Which AWS service should they use?

Accepted Answer

Use Amazon Personalize. Amazon Personalize is a fully managed service that enables you to build and deploy recommendation models without managing the underlying ML lifecycle. It supports collaborative filtering out of the box, using user purchase history and item metadata to generate personalized recommendations, which directly matches the startup's requirements.

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Use Amazon Kendra

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Use Amazon Lex

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Use Amazon SageMaker built-in Factorization Machines algorithm

Question 3

A media company runs batch inference jobs to generate captions for thousands of images weekly using a foundation model on Amazon Bedrock. They want to minimize costs while maintaining predictable throughput. Which pricing option should they choose?

Accepted Answer

Provisioned Throughput. Provisioned Throughput on Amazon Bedrock is the correct choice because it reserves a specified level of inference capacity for a foundation model, ensuring predictable throughput for batch workloads while offering a lower per-inference cost compared to on-demand pricing. This option is ideal for the media company's weekly batch inference jobs, as it minimizes costs by committing to a consistent volume of requests without the variability of spot instances or the higher per-request expense of on-demand inference.

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SageMaker Batch Transform

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On-demand inference

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Spot instances (EC2 Spot)

Question 4

A media company is using Amazon Bedrock to generate marketing copy with a foundation model. They want to ensure the output adheres to brand voice guidelines (e.g., friendly, professional). Which prompt engineering strategy is most effective for this requirement?

Accepted Answer

Use a system prompt that explicitly describes the brand voice and expectations.. Amazon Bedrock supports system prompts that set overarching context and behavioral guidelines for the model. By explicitly describing the brand voice (e.g., 'friendly, professional') in the system prompt, the model consistently applies these constraints across all user interactions, which is more effective than per-instruction tuning.

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Provide five example outputs in the prompt that match the desired tone.

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Include instructions like 'Do not use technical jargon' in every user prompt.

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Set the temperature parameter to a low value (e.g., 0.1) to reduce randomness.

Question 5

A data scientist is preparing to fine-tune an Amazon Titan model for a domain-specific text classification task. Which THREE components are essential for the fine-tuning process on Amazon Bedrock? (Choose THREE.)

Accepted Answer

Hyperparameter configuration (e.g., learning rate, batch size). Hyperparameters such as learning rate, batch size, and number of epochs directly control the optimization process during fine-tuning on Amazon Bedrock. These settings determine how the model updates its weights based on the training data, and they must be configured to achieve convergence without overfitting or underfitting.

Answer

A separate inference endpoint for testing during training

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Model architecture code for the Titan model

Question 6

A data scientist wants to quickly experiment with a pre-trained LLM for text generation without writing any code. Which AWS service is MOST suitable?

Accepted Answer

Amazon SageMaker JumpStart. Amazon SageMaker JumpStart provides a curated set of pre-trained foundation models (including LLMs) that can be deployed with just a few clicks, requiring no code. This makes it the most suitable service for a data scientist who wants to quickly experiment with a pre-trained LLM for text generation without writing any code.

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Amazon Bedrock

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Amazon EC2

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AWS Lambda

Question 7

Refer to the exhibit. A security analyst is reviewing CloudTrail logs and notices a training job creation from an IP address (203.0.113.5) that is not associated with the company's network. What is the most likely cause?

Accepted Answer

The user john.doe is accessing the AWS Management Console from a VPN.. The IP address 203.0.113.5 is a non-routable test IP (RFC 5737) and not associated with the company's network. The most likely cause is that user john.doe is accessing the AWS Management Console through a VPN, which would route traffic through the VPN's public IP rather than the corporate network. This explains why the source IP appears external while the user identity is legitimate.

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The CloudTrail log is being generated by a cross-account role.

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The training job was created using the AWS CLI from an external machine.

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The training job was created by a malicious actor who stole credentials.

Question 8

A data scientist is using Amazon SageMaker to train a model. The training job is taking longer than expected. Which change would most likely reduce training time?

Accepted Answer

Use a larger batch size. Using a larger batch size allows the model to process more training samples per iteration, which reduces the number of weight updates needed per epoch and can improve hardware utilization (e.g., GPU parallelism). This often leads to faster training times, provided the batch size fits within memory constraints and does not degrade model convergence.

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Increase the number of training epochs

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Use a smaller instance type

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Enable spot training

Question 9

A developer wants to test different foundation models quickly without setting up infrastructure. Which AWS service allows interactive prompting and comparison of multiple models?

Accepted Answer

Amazon Bedrock Playground. Amazon Bedrock Playground is a feature within Amazon Bedrock that provides a web-based interface for interactive prompting and side-by-side comparison of multiple foundation models (FMs). It allows developers to test different models quickly without provisioning any infrastructure, making it ideal for rapid experimentation and evaluation.

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Amazon Comprehend

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Amazon Lex

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Amazon SageMaker Studio

Question 10

A financial institution is developing a model to detect fraudulent transactions. They want to ensure the model is robust and does not exhibit bias. Which TWO actions should they take?

Accepted Answer

Use Amazon SageMaker Clarify to compute fairness metrics like demographic parity. Representation bias mitigation ensures diverse data, and using fairness metrics helps detect bias. These two actions directly address robustness and bias.

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Use a single model for all customers without segmenting by region

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Deploy the model with a high confidence threshold to reduce false positives

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Encrypt all transaction data at rest and in transit

Question 11

A company is using Amazon Bedrock to generate images from text prompts. They need to ensure the generated images do not contain offensive content. Which feature should be enabled?

Accepted Answer

Content moderation with AI. Amazon Bedrock's content moderation with AI feature allows you to filter generated images for offensive or inappropriate content by applying predefined or custom moderation policies. This is the correct choice because it directly addresses the requirement to ensure generated images do not contain offensive material, leveraging machine learning models to detect and block such content at inference time.

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VPC endpoints

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AWS WAF

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IAM policies

Question 12

A company is designing a RAG pipeline for a legal document review system. They need to ingest hundreds of documents, create embeddings, and store them for retrieval. Which THREE steps are essential in the ingestion phase of the RAG pipeline?

Accepted Answer

Chunk the documents into smaller pieces. Document chunking, generating embeddings, and storing in a vector store are the core steps of ingestion. Fine-tuning the LLM is not part of RAG ingestion (it's a separate process). Configuring guardrails is a runtime concern, not ingestion.

Answer

Configure Bedrock Guardrails for the application

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Fine-tune the base model on legal documents

Question 13

A company uses Amazon SageMaker to train a model. The training job fails with 'InsufficientInstanceCapacity' error. What is the most likely cause?

Accepted Answer

The requested instance type is not available in the specified region.. The 'InsufficientInstanceCapacity' error in Amazon SageMaker indicates that AWS does not currently have enough available capacity for the requested instance type in the specified region or Availability Zone. This is a common transient error when demand for a particular instance type exceeds supply, and it is not related to request rate, dataset size, or image compatibility.

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The request rate is too high.

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The dataset size exceeds the instance storage limit.

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The training image is not compatible with the instance type.

Question 14

A company is training a deep learning model for image classification. Which THREE practices help reduce overfitting? (Choose three.)

Accepted Answer

L2 regularization. L2 regularization (also known as weight decay) adds a penalty proportional to the square of the weight magnitudes to the loss function. This discourages the model from learning overly complex patterns by forcing weights to stay small, which reduces overfitting by limiting the model's capacity to fit noise in the training data.

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Increasing model depth

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Increasing learning rate

Question 15

A company uses Amazon Rekognition to detect objects in images. The model is producing a high number of false positives for a specific category. Which action should be taken to improve the model's precision for that category?

Accepted Answer

Use Amazon Rekognition Custom Labels to train a custom model with additional labeled images. Amazon Rekognition Custom Labels allows you to train a custom model using your own labeled dataset, which directly addresses the false positives for a specific category by fine-tuning the model on domain-relevant examples. This improves precision because the base Rekognition model is pre-trained on general data and may not capture the nuances of your specific category, whereas a custom model learns from your labeled images to reduce incorrect detections.

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Increase the confidence threshold to 99%

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Submit a request to AWS to retrain the base Rekognition model

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Use Amazon SageMaker to build a new object detection model from scratch