Free Practice Questions for Amazon Web Services AIF-C01 Exam

The best solution is Amazon Bedrock Knowledge Bases, which allows for the seamless integration of structured and unstructured internal documents—such as PDFs, CSVs, and extracted image text—into a retrieval-augmented generation (RAG) pipeline. According to AWS documentation, Bedrock Knowledge Bases offer a no-code or low-code setup to link your enterprise data with foundation models for context-aware responses, without needing to fine-tune or retrain models. The system indexes documents in an Amazon S3 bucket, creates embeddings, and stores them in a vector store. At inference time, the model retrieves relevant context and incorporates it into its response. This approach provides dynamic and up-to-date responses while maintaining data privacy, with minimal operational overhead. Unlike fine-tuning or building a model from scratch in SageMaker, which requires considerable compute resources and model management, Bedrock Knowledge Bases are serverless and easy to configure. It is designed exactly for internal knowledge AI assistants.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Developer Guide – Knowledge Bases

AWS Generative AI Best Practices – RAG Patterns for Enterprise Search

Amazon Bedrock Agents provide the capability to automate repetitive tasks and orchestrate complex workflows using generative AI models. This is particularly beneficial for customer support inquiries, where quick and efficient processing is crucial.

Option B (Correct): "Automation of repetitive tasks and orchestration of complex workflows": This is the correct answer because Bedrock Agents can automate common customer service tasks and streamline complex processes, improving response times and efficiency.

Option A: "Generation of custom foundation models (FMs) to predict customer needs" is incorrect as Bedrock agents do not create custom models.

Option C: "Automatically calling multiple foundation models (FMs) and consolidating the results" is incorrect because Bedrock agents focus on task automation rather than combining model outputs.

Option D: "Selecting the foundation model (FM) based on predefined criteria and metrics" is incorrect as Bedrock agents are not designed for selecting models.

AWS AI Practitioner References:

Amazon Bedrock Documentation: AWS explains that Bedrock Agents automate tasks and manage complex workflows, making them ideal for customer support automation.

Unsupervised learning involves training a model on unlabeled data, letting it find patterns or groupings on its own, without explicit outputs provided. Clustering is a primary unsupervised learning technique.

Option A is correct: Grouping customers based on purchase history (without predefined categories) is clustering, a classic unsupervised task.

B and C are supervised learning (classification and regression, respectively).

D is reinforcement learning, not unsupervised learning.

"Unsupervised learning involves training on data without labels and is often used for clustering or dimensionality reduction."

(Reference: AWS Certified AI Practitioner Official Study Guide, AWS ML Concepts)

Amazon SageMaker Serverless Inference is the correct solution for deploying an ML model to production in a way that allows a web application to use the model without the need to manage the underlying infrastructure.

Amazon SageMaker Serverless Inference provides a fully managed environment for deploying machine learning models. It automatically provisions, scales, and manages the infrastructure required to host the model, removing the need for the company to manage servers or other underlying infrastructure.

Why Option A is Correct:

No Infrastructure Management: SageMaker Serverless Inference handles the infrastructure management for deploying and serving ML models. The company can simply provide the model and specify the required compute capacity, and SageMaker will handle the rest.

Cost-Effectiveness: The serverless inference option is ideal for applications with intermittent or unpredictable traffic, as the company only pays for the compute time consumed while handling requests.

Integration with Web Applications: This solution allows the model to be easily accessed by web applications via RESTful APIs, making it an ideal choice for hosting the model and serving predictions.

Why Other Options are Incorrect:

B. Use Amazon CloudFront to deploy the model: CloudFront is a content delivery network (CDN) service for distributing content, not for deploying ML models or serving predictions.

C. Use Amazon API Gateway to host the model and serve predictions: API Gateway is used for creating, deploying, and managing APIs, but it does not provide the infrastructure or the required environment to host and run ML models.

D. Use AWS Batch to host the model and serve predictions: AWS Batch is designed for running batch computing workloads and is not optimized for real-time inference or hosting machine learning models.

Thus, A is the correct answer, as it aligns with the requirement of deploying an ML model without managing any underlying infrastructure.

The correct answer is B – Use Amazon Bedrock batch inference, which allows asynchronous generation of large-scale model outputs through APIs without requiring low-latency performance. According to AWS Bedrock documentation, batch inference is ideal for high-volume workloads that can tolerate delay, such as bulk content generation or summarization jobs. Unlike real-time inference, it processes requests in bulk, reducing cost and operational load. AWS handles the queuing, processing, and scaling automatically. Bedrock Agents (option C) are for workflow orchestration, not large-scale generation. AWS Lambda (option D) can automate tasks but is not optimized for high-volume LLM calls. Batch inference provides cost efficiency, scalability, and simplicity for delayed, asynchronous generation needs.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Developer Guide – Batch Inference

AWS ML Specialty Study Guide – Scalable Inference Options

Moderation APIs, such as Amazon Rekognition’s Content Moderation API, can help filter and block inappropriate or unwanted images from being returned by a chatbot. These APIs are specifically designed to detect and manage undesirable content in images.

Option A (Correct): "Implement moderation APIs": This is the correct answer because moderation APIs are designed to identify and filter inappropriate content, ensuring the chatbot does not return unwanted images.

Option B: "Retrain the model with a general public dataset" is incorrect because retraining does not directly prevent inappropriate content from being returned.

Option C: "Perform model validation" is incorrect as it ensures model correctness, not content moderation.

Option D: "Automate user feedback integration" is incorrect because user feedback does not prevent inappropriate images in real-time.

AWS AI Practitioner References:

AWS Content Moderation Services: AWS provides moderation APIs for filtering unwanted content from applications.

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.

To build an AI model responsibly and minimize bias, it is essential to ensure fairness and transparency throughout the model development and deployment process. This involves detecting and mitigating data imbalances and thoroughly evaluating the model's behavior to understand its impact on different groups.

Option A (Correct): "Detect imbalances or disparities in the data": This is correct because identifying and addressing data imbalances or disparities is a critical step in reducing bias. AWS provides tools like Amazon SageMaker Clarify to detect bias during data preprocessing and model training.

Option C (Correct): "Evaluate the model's behavior so that the company can provide transparency to stakeholders": This is correct because evaluating the model's behavior for fairness and accuracy is key to ensuring that stakeholders understand how the model makes decisions. Transparency is a crucial aspect of responsible AI.

Option B: "Ensure that the model runs frequently" is incorrect because the frequency of model runs does not address bias.

Option D: "Use the Recall-Oriented Understudy for Gisting Evaluation (ROUGE) technique to ensure that the model is 100% accurate" is incorrect because ROUGE is a metric for evaluating the quality of text summarization models, not for minimizing bias.

Option E: "Ensure that the model's inference time is within the accepted limits" is incorrect as it relates to performance, not bias reduction.

AWS AI Practitioner References:

Amazon SageMaker Clarify: AWS offers tools such as SageMaker Clarify for detecting bias in datasets and models, and for understanding model behavior to ensure fairness and transparency.

Responsible AI Practices: AWS promotes responsible AI by advocating for fairness, transparency, and inclusivity in model development and deployment.

The correct answer is A, as the most effective way to ensure fairness in AI systems is to audit and diversify training data. AWS Responsible AI documentation stresses that bias originates primarily from unbalanced or incomplete datasets. By including samples from all demographic groups, the model learns patterns that generalize fairly across populations. AWS SageMaker Clarify supports data bias detection and offers fairness metrics like demographic parity and equal opportunity difference. Deeper models (option B) or secrecy (option C) do not mitigate bias and may worsen transparency. Static test datasets (option D) fail to capture evolving data distributions. Therefore, ongoing bias reviews and diverse datasets form the foundation of responsible generative AI for financial use cases.

Referenced AWS AI/ML Documents and Study Guides:

AWS Responsible AI Whitepaper – Fairness and Bias Mitigation

Amazon SageMaker Clarify Documentation – Bias Detection

BLEU (Bilingual Evaluation Understudy) is a metric used to evaluate the accuracy of machine-generated translations by comparing them against reference translations. It is commonly used for translation tasks to measure how close the generated output is to professional human translations.

Option A (Correct): "Bilingual Evaluation Understudy (BLEU)": This is the correct answer because BLEU is specifically designed to evaluate the quality of translations, making it suitable for the company's use case.

Option B: "Root mean squared error (RMSE)" is incorrect because RMSE is used for regression tasks to measure prediction errors, not translation quality.

Option C: "Recall-Oriented Understudy for Gisting Evaluation (ROUGE)" is incorrect as it is used to evaluate text summarization, not translation.

Option D: "F1 score" is incorrect because it is typically used for classification tasks, not for evaluating translation accuracy.

AWS AI Practitioner References:

Model Evaluation Metrics on AWS: AWS supports various metrics like BLEU for specific use cases, such as evaluating machine translation models.