AIF-C01 Exam Dumps - AWS Certified AI Practitioner Exam

The problem involves a simple probability calculation that can be handled efficiently by straightforward mathematical rules and computations. Using machine learning techniques would introduce unnecessary complexity and operational overhead.

Option C (Correct): "Use code that will calculate probability by using simple rules and computations": This is the correct answer because it directly solves the problem with minimal overhead, using basic probability rules.

Option A: "Use supervised learning to create a regression model" is incorrect as it overcomplicates the solution for a simple probability problem.

Option B: "Use reinforcement learning to train a model" is incorrect because reinforcement learning is not needed for a simple probability calculation.

Option D: "Use unsupervised learning to create a model" is incorrect as unsupervised learning is not applicable to this task.

AWS AI Practitioner References:

Choosing the Right Solution for AI Tasks: AWS recommends using the simplest and most efficient approach to solve a given problem, avoiding unnecessary machine learning techniques for straightforward tasks.

Amazon Transcribe is the AWS service that converts speech to text, enabling the generation of subtitles (closed captions) for audio and video content automatically.

C is correct:

“Amazon Transcribe is an automatic speech recognition (ASR) service that makes it easy for developers to add speech-to-text capability to applications.”

This feature supports creating subtitles and transcripts for media files.

(Reference: Amazon Transcribe Overview, AWS AI Practitioner Official Study Guide)

A (Comprehend) is for NLP/text analytics.

B (Polly) is text-to-speech.

D (Translate) translates text, but does not create subtitles from audio/video.

Amazon SageMaker endpoints provide fully managed, serverless model deployment for real-time and batch predictions, allowing companies to deploy ML models without handling any servers or infrastructure management.

D is correct: SageMaker endpoints let you deploy, scale, and monitor ML models with no infrastructure overhead.

A and B require infrastructure management.

C (CloudFront/S3) is not for model deployment, but for static content delivery.

“Amazon SageMaker endpoints allow you to deploy machine learning models for inference without the need to manage underlying infrastructure.”

(Reference: AWS SageMaker Model Deployment, AWS Certified AI Practitioner Study Guide)

Comprehensive and Detailed Explanation From Exact AWS AI documents:

Inference parameters control output behavior:

Higher temperature increases creativity and randomness

Shorter response length limits output size

AWS prompt and inference guidance recommends increasing temperature for creative tasks and reducing response length for concise outputs.

Why the other options are incorrect:

A reduces creativity.

B produces long outputs.

D produces deterministic and lengthy responses.

AWS AI document references:

Inference Parameters for Foundation Models

Controlling Creativity and Length in Text Generation

Prompt Engineering Best Practices

Comprehensive and Detailed Explanation (AWS AI documents):

AWS machine learning fundamentals emphasize the bias–variance tradeoff as a core concept in building models that generalize well to unseen data. A model that is accurate on unseen (test or production) data must balance these two properties effectively.

Low bias means the model can capture the underlying patterns in the data and is not overly simplistic.

Low variance means the model’s predictions are stable and not overly sensitive to fluctuations or noise in the training dataset.

A model with low bias and low variance is best positioned to generalize well to new, unseen fraud data, which is critical in fraud detection systems where patterns evolve and false positives or false negatives can be costly.

Why the other options are incorrect:

High bias leads to underfitting and poor learning of fraud patterns.

High variance leads to overfitting, where the model performs well on training data but poorly on unseen data.

AWS AI Study Guide References:

AWS Machine Learning concepts: bias, variance, and generalization

AWS best practices for model training and evaluation in Amazon SageMaker

Retraining or pre-training is costly and unnecessary for just using company policies.

Fine-tuning adapts models but is inefficient for frequently changing company documents.

Retrieval-Augmented Generation (RAG) is the best approach — it retrieves relevant policy documents from a knowledge base and feeds them into the model context in real time, ensuring accurate and up-to-date responses.

📖 Reference:

AWS Documentation – RAG with Amazon Bedrock

The ecommerce company is deploying a chatbot that needs safeguards to filter harmful content from input prompts and responses. Amazon Bedrock Guardrails provide mechanisms to ensure responsible AI usage by filtering harmful content, such as hate speech, violence, or misinformation, making it the appropriate feature for this requirement.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"Amazon Bedrock Guardrails enable developers to implement safeguards for generative AI applications, such as chatbots, by filtering harmful content in input prompts and model responses. Guardrails include content filters, word filters, and denied topics to ensure safe and responsible outputs."

(Source: AWS Bedrock User Guide, Guardrails for Responsible AI)

Detailed Explanation:

Option A: Amazon Bedrock GuardrailsThis is the correct answer. Amazon Bedrock Guardrails are specifically designed to filter harmful content from chatbot inputs and responses, ensuring safe interactions for users.

Option B: Amazon Bedrock AgentsAmazon Bedrock Agents are used to automate tasks and integrate with external tools, not to filter harmful content. This option does not meet the requirement.

Option C: Amazon Bedrock inference APIsAmazon Bedrock inference APIs allow users to invoke foundation models for generating responses, but they do not provide built-in content filtering mechanisms.

Option D: Amazon Bedrock custom modelsCustom models on Amazon Bedrock allow users to fine-tune models, but they do not inherently include safeguards for filtering harmful content unless explicitly implemented.

Precision is the metric that measures the proportion of true positives (actual frauds) among all flagged positives (flagged frauds). High precision ensures that most of the flagged cases are truly fraudulent, minimizing the number of false positives employees must review.

C is correct:

"Precision is the ratio of true positives to all predicted positives, and it answers: ‘Of all the cases flagged as fraud, how many were actually fraud?’ High precision means fewer non-fraudulent cases are sent for manual review."

(Reference: AWS ML Concepts – Precision and Recall, AWS Certified AI Practitioner Study Guide)

A (Recall) measures how many actual frauds are caught, but does not minimize false positives.

B (Accuracy) can be misleading in imbalanced datasets (like fraud detection).