AIF-C01 Practice Test 27 — 15 Questions

Question 1

A healthcare company is training a model on sensitive patient data using Amazon SageMaker. They need to ensure that individual patient data cannot be reverse-engineered from the model. Which technique should they implement during training?

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

Accepted Answer

Differential privacy. Differential privacy is the technique specifically designed to prevent reverse-engineering of individual records from a trained model by injecting calibrated statistical noise during training. It provides a mathematical guarantee that the presence or absence of any single patient's data has a bounded effect on the model's output, directly addressing the requirement that individual patient data cannot be inferred. This is the only option that operates at the training algorithm level to protect individual records.

Answer

Data encryption at rest

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AWS Identity and Access Management (IAM) policies

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SageMaker Model Monitor

Question 2

A machine learning engineer wants to detect if sensitive data, such as personally identifiable information (PII), exists in a training dataset stored in S3 before training a model. Which AWS service should they use?

Accepted Answer

Amazon Macie. Amazon Macie uses machine learning to automatically discover, classify, and protect sensitive data in S3 buckets.

Answer

Amazon Inspector

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Amazon GuardDuty

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AWS Config

Question 3

A company uses a Bedrock Agent to handle customer support tickets. The agent needs to look up order status from a legacy API that requires authentication. The agent should also escalate to a human if the query is not supported. Which combination of components should the developer configure?

Accepted Answer

Define an action group with a Lambda function that calls the legacy API, and configure the agent instructions to escalate when it cannot answer.. Bedrock Agents use action groups to invoke external APIs; defining an action group with a Lambda function lets the agent call the legacy API with authentication handled inside Lambda. Configuring agent instructions to escalate when the agent cannot answer satisfies the human-handoff requirement. This combination directly addresses both the API integration and the escalation behavior.

Answer

Configure a Guardrail to block unsupported queries and return a static response.

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Use a Bedrock Knowledge Base to store order status information and rely on RAG to answer.

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Fine-tune the model on historical tickets so it can guess order status.

Question 4

A data science team needs to choose a machine learning approach for a project that requires predicting customer churn based on historical data. The team has a labeled dataset with 10,000 records and needs to interpret the model's decisions to provide business insights. Which machine learning technique should the team prioritize?

Accepted Answer

Random forest.. Random forest is the best choice because it handles the classification task of predicting churn (a binary outcome) from a labeled dataset, provides feature importance scores for interpretability, and works well with 10,000 records without overfitting due to its ensemble of decision trees. Its built-in ability to rank input features directly supports the team's need to derive business insights from model decisions.

Answer

K-means clustering.

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Linear regression.

Answer

Deep neural network with multiple hidden layers.

Question 5

A company wants to evaluate the performance of a generative AI model before deployment. Which TWO metrics are most relevant for measuring model quality? (Select two.)

Accepted Answer

BLEU score. BLEU score (A) is correct because it measures the quality of generated text by comparing n-gram overlap between model output and reference text, making it a standard metric for evaluating generative AI models such as translation and summarization systems. Perplexity (C) is correct because it quantifies how well a language model predicts a sample of text, with lower perplexity indicating better model confidence and language modeling quality. Response time (B) is not a quality metric but a latency/performance measure, model size (D) reflects resource footprint rather than output quality, and CPU utilization (E) is an infrastructure efficiency metric unrelated to the model's generative quality.

Answer

Response time

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Model size

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CPU utilization

Question 6

A developer needs to evaluate the quality of a text summarization model by comparing its output to reference summaries. Which automated metric measures the overlap of n‑grams between the generated and reference summaries?

Accepted Answer

ROUGE. ROUGE measures n‑gram overlap and is commonly used for summarization. BLEU is for translation, BERTScore uses embeddings, and METEOR accounts for synonyms — but the question specifically asks about n‑gram overlap.

Answer

BERTScore

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METEOR

Answer

BLEU

Question 7

Refer to the exhibit. An ML team finds that their training data is stored in two subfolders under s3://my-bucket/train/. They need to ensure that the dataset is balanced for training a classification model. What should they do?

Accepted Answer

Count the number of files in each subfolder and resample. The core requirement is to balance the dataset by ensuring an equal number of samples from each subfolder (class). Counting the files in each subfolder and then resampling (e.g., undersampling the majority class or oversampling the minority class) directly addresses class imbalance. This is a standard data preprocessing step before training a classification model, and it does not require any additional AWS services beyond the existing S3 storage.

Answer

Use AWS Glue to create a balanced dataset

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Use Amazon Rekognition custom labels

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Enable versioning on the bucket

Question 8

A healthcare company is building an AI application on AWS that processes patient records. The security team must ensure that data stored in Amazon S3 is encrypted at rest using a key that the company manages and can rotate independently. Which AWS service should they use to meet these requirements?

Accepted Answer

AWS Key Management Service (AWS KMS) customer managed key. AWS KMS customer managed keys allow the company to create, manage, and rotate encryption keys independently. When used with S3 server-side encryption (SSE-KMS), the company retains control over the key lifecycle and access policies, directly meeting the requirement for company-managed encryption keys for patient records.

Answer

AWS KMS AWS managed key

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AWS Secrets Manager

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Amazon S3 default encryption with Amazon S3 managed keys (SSE-S3)

Question 9

A machine learning practitioner is building a binary classifier for a medical diagnosis application. The cost of a false negative (missing a disease) is very high. Which evaluation metric should the team emphasize?

Accepted Answer

Recall. Recall (sensitivity) measures the proportion of actual positives correctly identified, which is critical when the cost of false negatives is high, as in medical diagnosis where missing a disease could have severe consequences. By maximizing recall, the model minimizes false negatives, ensuring that most patients with the disease are detected, even at the expense of more false positives.

Answer

Accuracy

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F1 score

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Precision

Question 10

An organization wants to document key information about their machine learning model, including intended use, performance metrics, training data, and ethical considerations. Which tool or practice should they adopt?

Accepted Answer

Model cards. Model cards are structured documents that describe a model's intended use, performance across relevant subgroups, training data provenance, and ethical considerations such as bias and limitations. They were popularized by Google and are now a standard responsible-AI artifact. This matches the question's requirement to document intended use, metrics, training data, and ethics in one place.

Answer

SageMaker Model Registry

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Data sheets

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AWS CloudTrail logs

Question 11

A research lab is using Amazon SageMaker to fine-tune a large language model (LLM) for scientific text summarization. The training dataset contains 10 million documents, and the lab has a limited budget but needs to minimize training time. They have access to SageMaker Training with managed spot instances, which offer significant cost savings but are interruptible. The team is considering different training strategies to balance cost, time, and model quality. Which strategy should they use?

Accepted Answer

Use SageMaker's distributed training with data parallelism on multiple managed spot instances, and enable checkpointing.. The best strategy. Using SageMaker's distributed training with data parallelism across multiple managed spot instances allows parallel processing of the 10-million-document dataset, significantly reducing training time. Spot instances offer cost savings of up to 90% compared to on-demand, and enabling checkpointing ensures that if any instance is interrupted, training can resume from the last checkpoint without losing progress. Option B is incorrect because fine-tuning only the last few layers on a subset of data compromises model quality and does not leverage the full dataset. Option C is incorrect because a single on-demand instance is expensive and slower than distributed spot instances. Option D is incorrect because training from scratch on a single GPU is prohibitively slow and costly, especially given the budget constraints.

Answer

Fine-tune only the last few layers of the model on a smaller subset of the data.

Answer

Use a single on-demand instance to avoid interruptions and maximize throughput.

Answer

Use a single large GPU instance to train the model from scratch.

Question 12

A company wants to send personalized product recommendations to customers based on their browsing history and previous purchases. Which AWS service is BEST suited for this?

Accepted Answer

Amazon Personalize. Amazon Personalize is a fully managed machine learning service specifically designed to build real-time personalized recommendation systems. It uses the same technology as Amazon.com's recommendation engine, processing user-item interaction data (browsing history, purchases) to generate tailored product suggestions. This makes it the ideal choice for the described use case.

Answer

Amazon SageMaker built-in factorization machines

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Amazon Forecast

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Amazon Rekognition

Question 13

A healthcare organization uses an ML model to predict patient readmission risk. To comply with regulations, they need to explain individual predictions to clinicians. Which explainability technique provides local, model-agnostic explanations that are computationally efficient?

Accepted Answer

LIME (Local Interpretable Model-agnostic Explanations). LIME (Local Interpretable Model-agnostic Explanations) is a model-agnostic method that approximates the model locally to provide explanations for individual predictions. SHAP is also local and model-agnostic but can be computationally intensive.

Answer

Partial dependence plots

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Amazon SageMaker Autopilot

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Global feature importance from a random forest

Question 14

A company wants to use Amazon Bedrock to generate responses grounded in their proprietary knowledge base. They need to minimize hallucinations and ensure responses are based on the provided documents. Which feature should they enable?

Accepted Answer

Grounding check. Grounding check in Bedrock Guardrails verifies that the model's response is supported by the source documents, reducing hallucinations.

Answer

Topic restrictions

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Word filters

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Content filters

Question 15

An organization is required to provide transparency about AI-generated content. Which of the following is the best practice to comply with transparency requirements?

Accepted Answer

Clearly label AI-generated content with a disclosure statement. Clearly labeling AI-generated content with a disclosure statement is the best practice to provide transparency. This directly informs users that the content is AI-generated, fulfilling transparency requirements. It is a straightforward and effective method that does not rely on hidden mechanisms.

Answer

Store metadata but not display it to users

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Use a watermark that is invisible to users

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Only disclose AI generation if the content is inaccurate