AIF-C01 Practice Exam 2 — 20 Questions

Question 1

A healthcare company uses Amazon Bedrock to generate patient summaries. They need to ensure no protected health information (PHI) is leaked in the output. Which AWS service can they use to detect and mask PHI in text?

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

Accepted Answer

Amazon Comprehend Medical. Amazon Comprehend Medical is specifically designed to extract and identify protected health information (PHI) from unstructured medical text using natural language processing (NLP). It can detect entities such as patient names, dates, medical conditions, and medications, and provides APIs to mask or redact that PHI before output. This makes it the correct choice for the healthcare company's requirement to prevent PHI leakage in patient summaries generated by Amazon Bedrock.

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Amazon Macie

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AWS Glue

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Amazon Rekognition

Question 2

Refer to the exhibit. A company has the S3 bucket policy shown above. The company uses Amazon Rekognition to analyze images in the 'my-images' bucket. An IAM role used by a Lambda function calls Rekognition. Why might Rekognition be unable to read images from the bucket?

Accepted Answer

The condition 'aws:SourceAccount' does not match the account of the Rekognition service. The bucket policy includes a condition 'aws:SourceAccount' that restricts access to requests originating from a specific AWS account. If the Rekognition service is being called from a different account (e.g., the Lambda function's IAM role is in a different account than the one specified in the condition), Rekognition will be denied read access to the S3 bucket. This condition is often misconfigured, causing cross-account access failures even when the principal is allowed.

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The Lambda function does not have an IAM role that allows Rekognition to call S3

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The bucket policy does not grant Rekognition access to the bucket

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The bucket policy does not include PutObject permission

Question 3

A company wants to automatically discover sensitive data such as credit card numbers in their Amazon S3 training datasets before using them for model training. Which AWS service should they use?

Accepted Answer

Amazon Macie. Amazon Macie uses machine learning and pattern matching to discover and protect sensitive data in S3.

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AWS Config

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Amazon GuardDuty

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AWS Audit Manager

Question 4

A data science team is using Amazon SageMaker Studio. To meet compliance requirements, they need to ensure that all user activity in the environment is logged and that any unauthorized access attempts are detected. Which approach should they take?

Accepted Answer

Enable AWS CloudTrail and Amazon GuardDuty for threat detection.. AWS CloudTrail logs all API activity in SageMaker Studio, including user actions and access attempts, while Amazon GuardDuty provides intelligent threat detection by analyzing CloudTrail logs, VPC flow logs, and DNS logs for unauthorized access patterns. Together, they meet compliance requirements for logging and detecting unauthorized access without additional configuration overhead.

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Enable SageMaker Model Monitor and configure Amazon S3 server access logs.

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Use AWS Config rules to track changes and Amazon Inspector for vulnerability scanning.

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Enable SageMaker Studio with VPC only mode and use AWS CloudTrail.

Question 5

Which TWO of the following are examples of supervised learning tasks that can be performed using Amazon SageMaker built-in algorithms?

Accepted Answer

XGBoost. XGBoost is a supervised learning algorithm that uses gradient-boosted decision trees for regression, classification, and ranking tasks. Amazon SageMaker's built-in XGBoost algorithm is optimized for distributed training and directly supports labeled training data, making it a correct example of a supervised learning task.

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Principal Component Analysis (PCA)

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Latent Dirichlet Allocation (LDA)

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K-Means

Question 6

A data scientist is using Amazon SageMaker to train a large language model from scratch. Which AWS service is most suitable for managing the training infrastructure, including automatic scaling and spot instance recovery?

Accepted Answer

Amazon SageMaker Training job.. Amazon SageMaker Training jobs are the most suitable service for managing training infrastructure because they provide built-in automatic scaling, managed spot instance recovery, and distributed training orchestration. This allows the data scientist to focus on model development rather than provisioning and managing EC2 instances, load balancers, or recovery scripts.

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AWS Lambda function.

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Amazon SageMaker Notebook instance.

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Amazon EC2 with a custom setup.

Question 7

A company wants to use a pre-trained generative AI model to analyze customer feedback. They need to adjust the model for their specific domain without retraining from scratch. Which approach is MOST suitable?

Accepted Answer

Fine-tuning the model on domain-specific data. Fine-tuning is the most suitable approach because it takes a pre-trained generative AI model and updates its weights using a smaller, domain-specific dataset (e.g., customer feedback transcripts). This allows the model to adapt to the company's specific terminology, sentiment patterns, and context without the massive computational cost and data requirements of training from scratch. It preserves the general language understanding from pre-training while specializing the model for the target domain.

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Reinforcement Learning from Human Feedback (RLHF)

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Training a new model from scratch on the domain data

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Using prompt engineering to provide context

Question 8

A company wants to extract text and data from scanned PDF invoices for automated processing. Which AWS service is MOST appropriate for this task?

Accepted Answer

Amazon Textract. Amazon Textract is specifically designed to extract text, handwriting, and data from scanned documents like PDF invoices. It uses machine learning to recognize and extract key-value pairs, tables, and form data, making it the most appropriate service for automated invoice processing.

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Amazon Rekognition

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Amazon Transcribe

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Amazon Comprehend

Question 9

A company is building a chatbot using Amazon Bedrock to answer customer questions about their product catalog. The chatbot should only use information from the company's internal knowledge base and should not generate answers based on the model's pre-training data. Which feature should be enabled?

Accepted Answer

Configure a knowledge base with Retrieval Augmented Generation (RAG). Configuring a knowledge base with Retrieval Augmented Generation (RAG) allows the chatbot to retrieve relevant documents from the company's internal knowledge base and use them as context for generating answers. This ensures the model's responses are grounded solely in the provided data, preventing reliance on its pre-training knowledge.

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Use prompt engineering to instruct the model to only use the knowledge base

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Enable model invocation logging to review responses

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Fine-tune the model on the product catalog data

Question 10

An e-commerce company is using a foundation model to generate product descriptions. They want to reduce costs by caching frequently requested descriptions. Which AWS service should they use to implement a cache?

Accepted Answer

Amazon ElastiCache. Amazon ElastiCache is the correct choice because it provides an in-memory caching layer (using Redis or Memcached) that can store frequently requested product descriptions, reducing the need to invoke the foundation model repeatedly. This directly lowers inference costs and latency by serving cached responses instead of generating new ones each time.

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Amazon CloudFront

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Amazon DynamoDB

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Amazon S3

Question 11

A company wants to deploy a real-time inference endpoint for a custom model on SageMaker. The model has high latency (100ms) and they need to handle variable traffic with spikes. Which deployment strategy is most cost-effective?

Accepted Answer

Deploy on a single SageMaker endpoint with automatic scaling. A single SageMaker endpoint with automatic scaling allows the endpoint to dynamically adjust the number of instances based on traffic patterns, handling variable traffic and spikes cost-effectively. For a model with 100ms latency, automatic scaling can add instances during spikes and remove them during low traffic, ensuring you only pay for the compute resources you use while maintaining low inference latency.

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Deploy on a SageMaker multi-model endpoint

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Use batch transform

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Use a single large instance type

Question 12

A healthcare startup is using Amazon Bedrock to generate clinical notes. They must prevent the model from outputting any personally identifiable information (PII) such as patient names. What is the most effective approach?

Accepted Answer

Configure a guardrail in Amazon Bedrock to deny PII topics. Amazon Bedrock Guardrails provide a native, policy-based mechanism to deny the generation of PII topics at inference time, without requiring model retraining or external filtering. This approach directly intercepts and blocks prohibited content before it is returned, offering the most reliable and maintainable solution for compliance with healthcare privacy regulations like HIPAA.

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Fine-tune the model on de-identified data only

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Use a prompt engineering technique to instruct the model to avoid PII

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Post-process the output with a regex filter

Question 13

An AI system is used to detect fraudulent transactions. The system has a high false positive rate for a certain demographic group. To ensure fairness and reduce false positives, which mitigation strategy should be considered?

Accepted Answer

Re-weight the training data to give more importance to samples from the affected group. Re-weighting training samples from the affected group can help the model learn to reduce false positives for that group, addressing the bias.

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Increase the model complexity

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Reduce the decision threshold globally

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Remove the demographic attribute from the model

Question 14

A security engineer creates the above IAM policy to allow a user to invoke an Amazon Bedrock model. However, invocation fails. What is the issue?

Accepted Answer

The ARN should use "foundation-model" instead of "model".. The IAM policy's resource ARN incorrectly uses 'model' in the path, but Amazon Bedrock requires 'foundation-model' to reference foundation models. The correct ARN format for invoking a Bedrock foundation model is 'arn:aws:bedrock:region::foundation-model/model-id'. Using 'model' instead of 'foundation-model' causes the policy to not match any valid Bedrock resource, resulting in an invocation failure.

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The action should be "bedrock:InvokeModelWithResponseStream".

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The resource ARN is missing the account ID.

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The statement is missing a condition for the model ID.

Question 15

A company wants to personalize product recommendations for its e-commerce website. The recommendation engine should adapt to each user's browsing and purchase history in real time. Which AWS service is MOST suitable?

Accepted Answer

Amazon Personalize. Amazon Personalize is the correct choice because it is a fully managed ML service specifically designed to build real-time personalized recommendation systems. It uses the same technology as Amazon's own recommendation engine, processing user-item interaction data (browsing and purchase history) to generate tailored product suggestions with sub-second latency via a real-time inference endpoint.

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Amazon Rekognition

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Amazon Comprehend

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Amazon Forecast

Question 16

Which THREE statements about Amazon SageMaker Ground Truth are correct? (Choose three.)

Accepted Answer

It provides built-in workflows for image classification and object detection.. Amazon SageMaker Ground Truth provides built-in workflows for common tasks like image classification and object detection, which simplifies the setup of labeling jobs. These pre-built templates handle the UI and data formatting, allowing users to focus on the labeling instructions rather than building the labeling interface from scratch.

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It can only be used for text data.

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It can only use a public workforce from Amazon Mechanical Turk.

Question 17

Which of the following is a benefit of using cross-validation during model training?

Accepted Answer

It provides a more reliable estimate of model performance on unseen data. Cross-validation partitions the dataset into multiple folds, training on some and validating on others iteratively. This process yields a more reliable estimate of model performance on unseen data by reducing the variance associated with a single train-test split and ensuring the model is evaluated across different subsets of the data.

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It reduces training time by using all data for training simultaneously

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It eliminates the need for feature engineering

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It guarantees that the model will achieve high accuracy on the test set

Question 18

A developer is using the Amazon Bedrock InvokeModel API with a model that has a context window of 8,000 tokens. The developer sends a prompt that is 7,500 tokens long and expects a response of about 1,000 tokens. The API call fails with an error indicating the input exceeds the model's context window. Why did this happen?

Accepted Answer

The context window includes both input and output tokens, so the total of 7,500 + 1,000 = 8,500 exceeds the 8,000 limit. The context window of a foundation model includes both the input prompt and the generated output tokens. The developer's prompt of 7,500 tokens plus the expected 1,000-token response totals 8,500 tokens, which exceeds the model's 8,000-token limit. The InvokeModel API enforces this combined limit, so even though the prompt alone is under the limit, the total token count causes the error.

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The API automatically reserves 2,000 tokens for output, reducing available input capacity

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The model requires a minimum of 512 tokens for internal processing

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The model's context window counts only input tokens; output tokens are separate

Question 19

An insurance company is using a machine learning model to approve claims. They want to ensure that the model's approval rate is similar across different demographic groups. Which fairness metric would directly measure whether the proportion of positive outcomes is equal across groups?

Accepted Answer

Demographic parity. Demographic parity, also known as statistical parity, requires that the probability of a positive outcome is the same for all groups. Equalized odds focuses on equal true positive and false positive rates, disparate impact measures the ratio of positive outcomes, but demographic parity directly compares proportions.

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Disparate impact ratio

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Equalized odds

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SHAP values

Question 20

A developer is using Amazon Bedrock to generate text summaries. The output sometimes includes irrelevant information. What is the most effective prompt engineering technique to improve relevance?

Accepted Answer

Use few-shot examples with summaries. Few-shot examples provide the model with explicit patterns of desired output, directly guiding it to produce summaries that match the format and content of the examples. This technique is the most effective for improving relevance because it gives the model concrete reference points, reducing the likelihood of including irrelevant information.

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Add a negative prompt specifying what to avoid

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Increase max tokens

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Decrease temperature