AIF-C01 Practice Test 9 — 10 Questions

Question 1

A company deploys a large language model to automatically generate product descriptions. They want to ensure customers are aware that the content is AI-generated, as part of transparency requirements. What should they implement?

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

Accepted Answer

Include a disclosure statement such as 'This content was generated by AI' in the output. Transparency requirements often mandate disclosing AI-generated content. The simplest approach is to include a clear disclosure in the output. Bedrock Guardrails can be configured to prepend or append a disclosure message. The other options either hide the AI origin or only apply to specific types of content like images.

Answer

Use Amazon Rekognition to add a visible watermark to images only

Answer

Store metadata in the content database indicating AI generation

Answer

Embed an invisible watermark in the generated text

Question 2

A company is deploying a machine learning model for real-time fraud detection. The model must have latency under 100ms. Which infrastructure choice is most appropriate?

Accepted Answer

Amazon SageMaker real-time endpoints. Amazon SageMaker real-time endpoints are designed for low-latency inference, typically in the tens of milliseconds, making them suitable for real-time fraud detection where latency must be under 100ms. They deploy a model behind a persistent HTTPS endpoint that auto-scales to handle incoming requests with minimal delay.

Answer

Amazon EC2 with Deep Learning AMI

Answer

Amazon SageMaker batch transform

Answer

Amazon SageMaker notebook instance

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.

Answer

Feature scaling

Answer

One-hot encoding

Answer

Polynomial features

Question 4

A healthcare AI system predicts patient diagnoses. The data collection process primarily samples from urban hospitals, leading to underrepresentation of rural populations. Which type of bias is this, and what is the most effective mitigation strategy?

Accepted Answer

Representation bias; collect additional data from rural hospitals. Representation bias occurs when a dataset does not adequately represent the target population. The best mitigation is to collect additional data from rural areas to balance representation. Weighting can help but is less effective than obtaining representative data. Historical bias is about past societal biases, not sampling. Measurement bias is about how features are measured. Regularization does not fix data imbalance.

Answer

Historical bias; reweight urban samples to reduce their influence

Answer

Aggregation bias; use regularization to simplify the model

Answer

Measurement bias; apply data augmentation to rural records

Question 5

According to AWS's responsible AI principles, which principle focuses on the idea that AI systems should produce consistent and reliable results even under unexpected conditions?

Accepted Answer

Robustness. Robustness refers to an AI system's ability to handle unexpected inputs and maintain performance, ensuring reliability.

Answer

Safety

Answer

Veracity

Answer

Fairness

Question 6

A data science team is using SHAP values to explain a complex model. They notice that for a particular prediction, the SHAP value for feature 'age' is +0.3. What does this indicate?

Accepted Answer

The age feature increased the prediction by 0.3 units from the baseline. SHAP values show the contribution of each feature to the prediction, measured in the output space. A positive SHAP value increases the prediction from the baseline.

Answer

The age feature is the most important feature globally

Answer

The age feature has a negative impact on the prediction

Answer

The age feature is causing bias in the model

Question 7

A social media platform uses an AI system to moderate content. They want to ensure that human reviewers can review decisions when the AI is uncertain. Which AWS service can be used to set up a human review workflow for AI predictions?

Accepted Answer

Amazon Augmented AI (A2I). Amazon Augmented AI (A2I) enables easy integration of human review workflows into ML applications, allowing humans to review low-confidence predictions or edge cases.

Answer

Amazon Mechanical Turk

Answer

AWS Lambda

Answer

Amazon SageMaker Ground Truth

Question 8

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 documentation templates that capture relevant model details, promoting transparency and responsible AI practices. They are recommended by responsible AI guidelines.

Answer

SageMaker Model Registry

Answer

Data sheets

Answer

AWS CloudTrail logs

Question 9

A company is developing an AI system that screens job applications. To comply with regulatory requirements, they need to provide explanations for each automated decision. Which explainability technique provides global feature importance across the entire dataset?

Accepted Answer

SHAP (SHapley Additive exPlanations). SHAP provides both local and global feature importance values based on game theory. It can show the overall impact of each feature across all predictions.

Answer

Partial dependence plots

Answer

LIME (Local Interpretable Model-agnostic Explanations)

Answer

Permutation feature importance

Question 10

A bank is deploying a credit scoring model and must comply with regulatory requirements that decisions can be explained to customers. The model is a gradient boosting machine with hundreds of features. Which explainability technique should the team use to provide local explanations for individual loan decisions?

Accepted Answer

SHAP values. SHAP values provide a unified measure of feature importance for individual predictions, satisfying local explainability requirements. LIME is also local but is less stable and not game-theoretic. Global feature importance does not explain individual decisions.

Answer

Global feature importance based on permutation

Answer

Partial dependence plots

Answer

LIME