Amazon Web Services AIF-C01 Actual Questions

The verified answer is A. Review the training data to check for biases. Include data from all demographics in the training data. The application is being used for loan approval decisions, so fairness is critical. AWS Machine Learning Lens guidance states that teams should analyze whether training data adequately represents the diversity of the user population and check for existing biases in labels or features that could be perpetuated by the model. AWS also recommends evaluating and preparing representative training data, analyzing training data for potential biases, verifying that the data accurately represents the population on which the model will be deployed, and addressing representation gaps.

This directly matches option A. For loan decisions, biased or unrepresentative training data can cause unfair outcomes for demographic groups. Reviewing the training data and including representative data from all demographics helps reduce the risk that the model learns patterns that disadvantage underrepresented groups.

Option B is incorrect because a deep learning model with many hidden layers does not automatically make a system fair. In fact, more complex models can be harder to interpret and may still learn biased patterns from biased data.

Option C is incorrect because secrecy conflicts with responsible AI principles. Financial loan decisions often require transparency, explainability, governance, and auditability. Hiding the decision process does not make outputs fair.

Option D is incorrect because monitoring only a static test dataset is insufficient. A static dataset may not represent changing real-world populations, drift, or emerging bias. AWS guidance recommends tracking fairness metrics over time and detecting emerging bias in deployment.

Therefore, the correct solution is to review and balance representative training data across demographics.

The verified answer is B. Data leakage. The model exposes sensitive company information because that information was inadvertently included in training data. AWS guidance warns that using sensitive data to train models can cause unintended disclosure of data from model output, including data leakage during inference. This is exactly the scenario: sensitive information entered the training process and later appeared in model output after deployment.

AWS security reference guidance for generative AI model customization identifies sensitive information disclosure as a threat where a model trained on datasets containing PII leaks sensitive information during inference. Although the question says company information rather than PII specifically, the same security principle applies: protected data included in training or continued pre-training can be memorized or reproduced by the model later.

Jailbreaking is incorrect because jailbreaking refers to attempts to bypass model safety controls through adversarial prompts. The problem here is not that a user bypassed safety controls; the sensitive information was already embedded through training data.

Contextual grounding is incorrect because grounding is a technique for improving answer accuracy by connecting model responses to trusted context, such as RAG sources. It is not a security risk.

Prompt injection is incorrect because prompt injection involves malicious input that attempts to override or manipulate model behavior at inference time. The question does not describe a malicious prompt. It describes accidental exposure from training data.

Therefore, the correct security risk is data leakage.

The Amazon EC2 Trn series (Trainium) instances are designed for high-performance, cost-effective machine learning training while being energy-efficient. AWS Trainium-powered instances are optimized for deep learning models and have been developed to minimize environmental impact by maximizing energy efficiency.

Option D (Correct): " Amazon EC2 Trn series " : This is the correct answer because the Trn series is purpose-built for training deep learning models with lower energy consumption, which aligns with the company ' s concern about environmental effects.

Option A: " Amazon EC2 C series " is incorrect because it is intended for compute-intensive tasks but not specifically optimized for ML training with environmental considerations.

Option B: " Amazon EC2 G series " (Graphics Processing Unit instances) is optimized for graphics-intensive applications but does not focus on minimizing environmental impact for training.

Option C: " Amazon EC2 P series " is designed for ML training but does not offer the same level of energy efficiency as the Trn series.

AWS AI Practitioner References:

AWS Trainium Overview: AWS promotes Trainium instances as their most energy-efficient and cost-effective solution for ML model training.

The mobile app for users with visual impairment needs to hear user speech and provide voice responses, requiring speech-to-text (speech recognition) and text-to-speech capabilities. Deep learning neural networks are widely used for speech recognition tasks, as they can effectively process and transcribe spoken language. AWS services like Amazon Transcribe, which uses deep learning for speech recognition, can fulfill this requirement by converting user speech to text, and Amazon Polly can generate voice responses.

Exact Extract from AWS AI Documents:

From the AWS Documentation on Amazon Transcribe:

" Amazon Transcribe uses deep learning neural networks to perform automatic speech recognition (ASR), converting spoken language into text with high accuracy. This is ideal for applications requiring voice input, such as accessibility features for visually impaired users. "

(Source: Amazon Transcribe Developer Guide, Introduction to Amazon Transcribe)

Detailed Explanation:

Option A: Use a deep learning neural network to perform speech recognition.This is the correct answer. Deep learning neural networks are the foundation of modern speech recognition systems, as used in AWS services like Amazon Transcribe. They enable the app to hear and transcribe user speech, and a service like Amazon Polly can handle voice responses, meeting the requirements.

Option B: Build ML models to search for patterns in numeric data.This option is irrelevant, as the task involves processing speech (audio data) and generating voice responses, not analyzing numeric data patterns.

Option C: Use generative AI summarization to generate human-like text.Generative AI summarization focuses on summarizing text, not processing speech orgenerating voice responses. This option does not address the core requirement of speech recognition.

Option D: Build custom models for image classification and recognition.Image classification and recognition are unrelated to processing speech or generating voice responses, making this option incorrect for an app focused on audio interaction.