Free Amazon Web Services AIF-C01 Practice Exam with Questions & Answers | Set: 4

The company is training an image classification model for diagnosing insect bites using a diverse dataset that includes photos from different genders, ethnicities, and geographic locations. This approach demonstrates the principle of fairness in responsible AI, as it aims to reduce bias and ensure the model performs equitably across diverse populations.

Exact Extract from AWS AI Documents:

From the AWS AI Practitioner Learning Path:

"Fairness in AI involves ensuring that models do not exhibit bias against certain groups and perform equitably across diverse populations. This can be achieved by training models on diverse datasets that represent various demographics, such as gender, ethnicity, and geographic location."

(Source: AWS AI Practitioner Learning Path, Module on Responsible AI)

Detailed Explanation:

Option A: FairnessThis is the correct answer. By using a diverse dataset, the company ensures the model is less likely to be biased against specific groups, promoting fairness in its predictions and treatments for insect bites.

Option B: ExplainabilityExplainability refers to making the model’s decisions understandable to users, such as byproviding insights into how predictions are made. The scenario focuses on dataset diversity, not explainability.

Option C: GovernanceGovernance involves establishing policies and processes to manage AI systems, such as compliance and oversight. The scenario does not describe governance mechanisms.

Option D: TransparencyTransparency involves disclosing how a model works, its limitations, and its data sources. While transparency is important, the scenario specifically highlights the diversity of the dataset, which aligns more directly with fairness.

AWS Artifact provides compliance reports and certifications (ISO, SOC, GDPR-related documentation) to prove regulatory adherence.

Inference = applying a trained ML model to new, unseen data to make predictions, classifications, or generate outputs.

A is algorithm research, C refers to ensemble learning, D is data collection.

???? Reference:

AWS ML Glossary – Inference

The correct answer is A because Human-in-the-loop (HITL) is a post-processing strategy used to monitor, review, and filter outputs from generative AI models for toxicity, bias, or inappropriate content. It allows human reviewers to approve or reject model responses before they are delivered to end-users, ensuring alignment with ethical guidelines and company policies.

From the AWS documentation:

"Human-in-the-loop (HITL) workflows in generative AI are used to validate and approve outputs of models, especially in applications where content quality, compliance, or harm reduction is critical. HITL is a key step in responsible AI implementations to mitigate hallucinations, bias, and unsafe content."

Explanation of other options:

B. Data augmentation is a pre-processing technique to increase data diversity, not typically used in post-processing stages.

C. Feature engineering is relevant in traditional ML, especially structured data tasks, not typically used in generative AI post-processing.

D. Adversarial training is a model training strategy, not a post-processing mitigation approach.

Referenced AWS AI/ML Documents and Study Guides:

AWS Responsible AI Practices Whitepaper

AWS Generative AI Developer Guide – Human-in-the-loop and Post-processing

Amazon A2I Documentation – Integrating Human Review in ML Workflows

Object detection is the correct strategy for automatically identifying and categorizing animals in photos.

Object Detection:

A computer vision technique that identifies and locates objects within an image and assigns them to predefined categories.

Ideal for tasks such as identifying animals in photos, where the goal is to detect specific objects (animals) and categorize them accordingly.

Why Option A is Correct:

Automatic Identification: Object detection models can automatically identify different types of animals in the images without manual intervention.

Categorization Capability: Assigns labels to detected objects, fulfilling the requirement for categorizing animals.

Why Other Options are Incorrect:

B. Anomaly detection: Identifies outliers or unusual patterns, not specific objects in images.

C. Named entity recognition: Used in NLP to identify entities in text, not for image processing.

D. Inpainting: Used for filling in missing parts of images, not for detecting or categorizing objects.

Amazon S3 is the optimal choice for storing and uploading datasets used for machine learning model validation and training. It offers scalable, durable, and secure storage, making it ideal for holding datasets required by Amazon Bedrock for validation purposes.

Option A (Correct): "Amazon S3": This is the correct answer because Amazon S3 is widely used for storing large datasets that are accessed by machine learning models, including those in Amazon Bedrock.

Option B: "Amazon Elastic Block Store (Amazon EBS)" is incorrect because EBS is a block storage service for use with Amazon EC2, not for directly storing datasets for Amazon Bedrock.

Option C: "Amazon Elastic File System (Amazon EFS)" is incorrect as it is primarily used for file storage with shared access by multiple instances.

Option D: "AWS Snowcone" is incorrect because it is a physical device for offline data transfer, not suitable for directly providing data to Amazon Bedrock.

AWS AI Practitioner References:

Storing and Managing Datasets on AWS for Machine Learning: AWS recommends using S3 for storing and managing datasets required for ML model training and validation.

The financial institution needs an AI solution for loan approval decisions to be explainable for security and audit purposes. Explainability refers to the ability to understand and interpret how a model makes decisions. Model complexity directly impacts explainability: simpler models (e.g., logistic regression) are more interpretable, while complex models (e.g., deep neural networks) are harder to explain, often behaving like "black boxes."

Exact Extract from AWS AI Documents:

From the Amazon SageMaker Developer Guide:

"Model complexity affects the explainability of AI solutions. Simpler models, such as linear regression, are inherently more interpretable, while complex models, such as deep neural networks, may require additional tools like SageMaker Clarify to provide insights into their decision-making processes."

(Source: Amazon SageMaker Developer Guide, Explainability with SageMaker Clarify)

Detailed Explanation:

Option A: Model complexityThis is the correct answer. The complexity of the model directly influences how easily its decisions can be explained, a critical factor for audit and security purposes in loan approvals.

Option B: Training timeTraining time refers to how long it takes to train the model, which does not directly impact the explainability of its decisions.

Option C: Number of hyperparametersWhile hyperparameters affect model performance, they do not directly relate to explainability. A model with many hyperparameters might still be explainable if it is a simple model.

Option D: Deployment timeDeployment time refers to the time taken to deploy the model to production, which is unrelated to the explainability of its decisions.

Amazon CloudWatch is designed for real-time monitoring of applications and infrastructure. It supports metrics and logs for ML model performance and resource utilization. According to the AWS Certified AI Practitioner Study Guide:

“Amazon CloudWatch is a monitoring service that provides data and actionable insights to monitor your ML workloads and applications in real time, ensuring performance and scalability.”

Domain adaptation fine-tuning involves training a foundation model (FM) further using a specific dataset that includes domain-specific terminology and content, such as scientific terms in research papers. This process allows the model to better understand and handle complex terminology, improving its performance on specialized tasks.

Option B (Correct): "Use domain adaptation fine-tuning to adapt the FM to complex scientific terms": This is the correct answer because fine-tuning the model on domain-specific data helps it learn and adapt to the specific language and terms used in the research papers, resulting in better performance.

Option A: "Use few-shot prompting to define how the FM can answer the questions" is incorrect because while few-shot prompting can help in certain scenarios, it is less effective than fine-tuning for handling complex domain-specific terms.

Option C: "Change the FM inference parameters" is incorrect because adjusting inference parameters will not resolve the issue of the model's lack of understanding of complex scientific terminology.

Option D: "Clean the research paper data to remove complex scientific terms" is incorrect because removing the complex terms would result in the loss of important information and context, which is not a viable solution.

AWS AI Practitioner References:

Domain Adaptation in Amazon Bedrock: AWS recommends fine-tuning models with domain-specific data to improve their performance on specialized tasks involving unique terminology.

Increasing the number of epochs during model training allows the model to learn from the data over more iterations, potentially improving its accuracy up to a certain point. This is a common practice when attempting to reach a specific level of accuracy.

Option B (Correct): "Increase the epochs": This is the correct answer because increasing epochs allows the model to learn more from the data, which can lead to higher accuracy.

Option A: "Decrease the batch size" is incorrect as it mainly affects training speed and may lead to overfitting but does not directly relate to achieving a specific accuracy level.

Option C: "Decrease the epochs" is incorrect as it would reduce the training time, possibly preventing the model from reaching the desired accuracy.

Option D: "Increase the temperature parameter" is incorrect because temperature affects the randomness of predictions, not model accuracy.

AWS AI Practitioner References:

Model Training Best Practices on AWS: AWS suggests adjusting training parameters, like the number of epochs, to improve model performance.