Selected AIF-C01 AWS Certified AI Practitioner Questions Answers

AWS Glue is the correct service for transforming petabytes of unstructured data into a structured format suitable for machine learning tasks.

AWS Glue:

A fully managed extract, transform, and load (ETL) service that makes it easy to prepare and transform unstructured data into a structured format.

Provides a range of tools for cleaning, enriching, and cataloging data, making it ready for data scientists to use in ML models.

Why Option D is Correct:

Data Transformation: AWS Glue can handle large volumes of data and transform unstructured data into structured formats efficiently.

Integrated ML Support: Glue integrates with other AWS services to support ML workflows.

Why Other Options are Incorrect:

A. Amazon Lex: Used for building chatbots, not for data transformation.

B. Amazon Rekognition: Used for image and video analysis, not for data transformation.

C. Amazon Kinesis Data Streams: Handles real-time data streaming, not suitable for batch transformation of large volumes of unstructured data.

The scenario where a student copies content from generative AI to write essays represents the challenge of plagiarism in responsible AI use.

Plagiarism:

Occurs when someone uses content generated by AI (or any source) without proper attribution, claiming it as their own.

This is a key challenge with generative AI models, which can produce human-like text that might be misused for academic or other purposes.

Why Option C is Correct:

Represents Unauthorized Use: Copying content directly from AI without attribution is a clear case of plagiarism.

Ethical Concern: Highlights the ethical considerations around using AI-generated content responsibly.

Why Other Options are Incorrect:

A. Toxicity: Refers to harmful or offensive content generation, not content copying.

B. Hallucinations: When AI generates incorrect or nonsensical information, not plagiarism.

D. Privacy: Involves the misuse or exposure of personal information, not copying content.

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).

Zero-shot learning is when the model is asked to perform a task without being given any examples. The prompt simply describes the task and relies on the model’s pre-trained knowledge.

(Reference: Amazon Bedrock Prompt Engineering Guide)

Few-shot learning provides a few examples (shots) in the prompt to show the model how to solve the task before asking it to complete a similar task.

(Reference: Amazon Bedrock Prompt Engineering Guide)

Chain-of-thought reasoning encourages the model to reason step by step and explain its thinking for more complex or logical tasks.

(Reference: AWS Chain-of-Thought Prompting)