Amazon AIF-C01 AWS Certified AI Practitioner Exam Practice Test

Answer : B

Amazon Bedrock provides large language models (LLMs) that are optimized for natural language understanding and text summarization tasks, making it the best choice for creating concise summaries of user reviews. Time-series forecasting, classification, and image analysis (Rekognition) are not suitable for summarizing textual data. Reference: AWS Bedrock Documentation.

Answer : B

Amazon SageMaker Model Cards provide a standardized way to document important details about an AI model, such as its purpose, performance, intended usage, and known limitations. This enables transparency and compliance while fostering better communication between stakeholders. It does not store models physically or optimize computational requirements. Reference: AWS SageMaker Model Cards Documentation.

Answer : B

Amazon Comprehend is the correct service to analyze customer support interactions and identify frequently asked questions and insights.

Amazon Comprehend:

A natural language processing (NLP) service that uses machine learning to uncover insights and relationships in text.

Capable of extracting key phrases, detecting entities, analyzing sentiment, and identifying topics from text data, making it ideal for analyzing customer support interactions.

Why Option B is Correct:

Text Analysis Capabilities: Can process large volumes of text to identify common topics, phrases, and sentiment, providing valuable insights.

Suitable for Customer Support Analysis: Specifically designed to understand the content and meaning of text, which is key for identifying frequently asked questions.

Why Other Options are Incorrect:

A . Amazon Lex: Used for building conversational interfaces, not for text analysis.

C . Amazon Transcribe: Converts speech to text but does not perform text analysis.

D . Amazon Translate: Used for translating text between languages, not for analyzing content.

Answer : D

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.

Answer : B

Amazon SageMaker JumpStart is the correct service for quickly deploying and consuming a foundation model (FM) within a team's VPC.

Amazon SageMaker JumpStart:

Provides access to a wide range of pre-trained models and solutions that can be easily deployed and consumed within a VPC.

Designed to simplify and accelerate the deployment of machine learning models, including foundation models.

Why Option B is Correct:

Rapid Deployment: JumpStart allows for quick deployment of models with minimal configuration, directly within a secure VPC environment.

Ease of Use: Provides a user-friendly interface to select and deploy models, reducing the time to value.

Why Other Options are Incorrect:

A . Amazon Personalize: Focuses on creating personalized recommendations, not deploying foundation models.

C . PartyRock: Not a recognized AWS service.

D . Amazon SageMaker endpoints: Endpoints are for deploying specific models, not a feature for quickly starting with pre-trained foundation models.

Answer : A

Providing examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified is the correct prompt engineering strategy for using a large language model (LLM) on Amazon Bedrock for sentiment analysis.

Example-Driven Prompts:

This strategy, known as few-shot learning, involves giving the model examples of input-output pairs (e.g., text passages with their sentiment labels) to help it understand the task context.

It allows the model to learn from these examples and apply the learned pattern to classify new text passages correctly.

Why Option A is Correct:

Guides the Model: Providing labeled examples teaches the model how to perform sentiment analysis effectively, increasing accuracy.

Contextual Relevance: Aligns the model's responses to the specific task of classifying sentiment.

Why Other Options are Incorrect:

B . Detailed explanation of sentiment analysis: Unnecessary for the model's operation; it requires examples, not explanations.

C . New text passage without context: Provides no guidance or learning context for the model.

D . Unrelated task examples: Would confuse the model and lead to inaccurate results.

Answer : B

Sampling bias is the correct type of bias affecting the model output when it disproportionately flags people from a specific ethnic group.

Sampling Bias:

Occurs when the training data is not representative of the broader population, leading to skewed model outputs.

In this case, if the model disproportionately flags people from a specific ethnic group, it likely indicates that the training data was not adequately balanced or representative.

Why Option B is Correct:

Reflects Data Imbalance: A biased sample in the training data could result in unfair outcomes, such as disproportionately flagging a particular group.

Common Issue in ML Models: Sampling bias is a known problem that can lead to unfair or inaccurate model predictions.

Why Other Options are Incorrect:

A . Measurement bias: Involves errors in data collection or measurement, not sampling.

C . Observer bias: Refers to bias introduced by researchers or data collectors, not the model's output.

D . Confirmation bias: Involves favoring information that confirms existing beliefs, not relevant to model output bias.