AIF-C01 Practice Test 10 — 10 Questions

Question 1

A data scientist needs to grant an IAM user access to a specific Amazon SageMaker notebook instance. The user should only be able to start and stop the notebook instance, but not delete it. Which IAM policy statement should be used?

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

Accepted Answer

{"Effect":"Allow","Action":["sagemaker:StartNotebookInstance","sagemaker:StopNotebookInstance"],"Resource":"arn:aws:sagemaker:us-east-1:123456789012:notebook-instance/MyNotebook"}. It uses the specific actions `sagemaker:StartNotebookInstance` and `sagemaker:StopNotebookInstance` with a resource ARN that targets only the intended notebook instance. This grants the least privilege required to start and stop the instance while explicitly preventing deletion, as no delete action is included. The resource ARN restricts the policy to a single notebook instance, ensuring the user cannot affect other resources.

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{"Effect":"Allow","Action":["sagemaker:Start*","sagemaker:Stop*"],"Resource":"*"}

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{"Effect":"Allow","Action":"sagemaker:*","Resource":"*"}

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{"Effect":"Allow","Action":"sagemaker:*","Resource":"arn:aws:sagemaker:us-east-1:123456789012:notebook-instance/MyNotebook"}

Question 2

A financial services firm must extract text from scanned loan application forms to automate data entry. The forms are in various languages. Which combination of AWS AI services should be used?

Accepted Answer

Amazon Textract and Amazon Translate. Amazon Textract is designed to extract text and data from scanned documents, including forms, using OCR (Optical Character Recognition). Amazon Translate then converts the extracted text from various languages into a target language, enabling automated data entry across multilingual loan applications. Together, they directly address the requirement of extracting text from scanned forms in multiple languages.

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Amazon Transcribe and Amazon Polly

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Amazon Kendra and Amazon Lex

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Amazon Rekognition and Amazon Comprehend

Question 3

A data scientist is using SageMaker to train a model on a dataset with many features. They suspect some features are redundant. Which feature engineering technique would help?

Accepted Answer

Principal Component Analysis (PCA). Principal Component Analysis (PCA) is a dimensionality reduction technique that transforms the original correlated features into a smaller set of uncorrelated principal components, effectively removing redundancy while preserving most of the variance in the data. In SageMaker, PCA can be applied via the built-in PCA algorithm or as a preprocessing step in a scikit-learn container to reduce feature space and eliminate multicollinearity.

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Feature scaling

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One-hot encoding

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Polynomial features

Question 4

A company is deploying a generative AI application that creates marketing copy. They want to ensure the outputs do not include harmful or inappropriate content. Which AWS service can enforce content policies and filter undesirable outputs?

Accepted Answer

Amazon Bedrock Guardrails. Amazon Bedrock Guardrails lets you define content filters, denied topics, word filters, and sensitive information filters that are applied to both inputs and outputs of foundation models. It is purpose-built to enforce responsible AI policies and block harmful or inappropriate content in generative AI applications. This directly addresses the requirement to filter undesirable outputs from a marketing copy generator.

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AWS WAF

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Amazon Comprehend

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AWS Shield

Question 5

A data scientist is training a model using Amazon SageMaker and notices the training loss is decreasing but validation loss starts increasing after a few epochs. Which technique should they apply to address this?

Accepted Answer

Add regularization (e.g., L1 or L2). The scenario describes overfitting, where the model memorizes training data but fails to generalize to validation data. Adding regularization (L1 or L2) penalizes large weights, reducing model complexity and improving generalization. This is a standard technique in SageMaker training jobs, often configured via the `regularizer` hyperparameter in frameworks like TensorFlow or MXNet.

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Increase batch size

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Increase the learning rate

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Add more training data

Question 6

An organization uses Amazon Bedrock to generate content. They have implemented guardrails to block toxic content. However, some users are able to bypass the guardrails by encoding their prompts. What step should be taken to improve security?

Accepted Answer

Enable prompt injection detection in the guardrail configuration.. Amazon Bedrock guardrails include a built-in prompt injection detection capability that can identify and block attempts to bypass content filters through encoded or obfuscated prompts. Enabling this feature specifically addresses the scenario where users encode their inputs to evade toxic content blocking, as it analyzes the decoded intent of the prompt rather than just the surface-level encoding.

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Encode the prompts before sending to the model.

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Use a different foundation model that is less susceptible.

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Restrict access to the model using IAM policies.

Question 7

A machine learning model achieves 99% accuracy on the training set but only 65% on the test set. Which phenomenon is the model experiencing?

Accepted Answer

Overfitting. The model's high accuracy on the training set (99%) but significantly lower accuracy on the test set (65%) indicates that it has memorized the training data, including noise and outliers, rather than learning generalizable patterns. This is the classic symptom of overfitting, where the model performs well on seen data but fails to generalize to unseen data.

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Bias-variance tradeoff

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Data leakage

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Underfitting

Question 8

A company is building a generative AI application using Amazon Bedrock and needs to ensure that the model does not generate outputs containing personally identifiable information (PII). Which TWO actions should the company take? (Choose 2)

Accepted Answer

Implement a custom AWS Lambda function to scan and redact PII from inputs and outputs.. Option A is correct because a custom AWS Lambda function can programmatically scan both the prompt inputs and the model responses for PII patterns (for example, using regex or Amazon Comprehend's PII detection APIs) and redact or block them before they reach the user, giving the company full control over what data enters and leaves the generative AI application. Option D is correct because Amazon Bedrock Guardrails provides a native, purpose-built sensitive information filter that can detect and either block or mask PII entities (such as names, emails, and SSNs) in both user inputs and model outputs, directly satisfying the requirement to prevent PII from appearing in generated content. Option B is not correct because IAM policies only control who can invoke which Bedrock models and actions; they do not inspect or filter the content of prompts and responses for PII. Option C is not correct because CloudWatch Logs only captures and stores model outputs for auditing and monitoring after the fact; it does not prevent PII from being generated or returned. Option E is not correct because placing the Bedrock endpoint in a private VPC only affects network isolation and traffic routing, not the content-level detection or redaction of PII in model outputs.

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

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Enable Amazon CloudWatch Logs to capture and audit model outputs.

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Place the Bedrock model endpoint within a private VPC.

Question 9

A company is developing a speech-to-text application for a diverse user base. To ensure inclusive design, they test the model with different accents and dialects. They find that error rates are higher for certain accents. Which responsible AI principle is most directly violated?

Accepted Answer

Fairness. Fairness in responsible AI refers to avoiding bias and ensuring equitable performance across different demographic groups. Higher error rates for certain accents indicate that the model performs unequally across user groups, which is a direct violation of fairness. This is a classic example of algorithmic bias in speech recognition systems.

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Robustness

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Veracity

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Privacy and security

Question 10

A data scientist is prototyping a text summarization application using Amazon Bedrock. They want to quickly test different prompts and models without writing code. Which AWS service or feature should they use?

Accepted Answer

Bedrock Playground. Bedrock Playground is a no-code interface within Amazon Bedrock that allows users to experiment with different foundation models and prompts interactively. It is specifically designed for rapid prototyping without writing any code, making it the ideal choice for quickly testing text summarization prompts and models.

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AWS Cloud9

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Amazon SageMaker Studio

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AWS Lambda