Free Amazon Web Services AIF-C01 Practice Exam with Questions & Answers | Set: 6

Amazon Q Developer is a tool designed to assist developers in increasing productivity by generating code snippets, managing reference tracking, and handling open-source license tracking. These features help developers by automating parts of the software development process.

Option A (Correct): "Create software snippets, reference tracking, and open-source license tracking": This is the correct answer because these are key features that help developers streamline and automate tasks, thus improving productivity.

Option B: "Run an application without provisioning or managing servers" is incorrect as it refers to AWS Lambda or AWS Fargate, not Amazon Q Developer.

Option C: "Enable voice commands for coding and providing natural language search" is incorrect because this is not a function of Amazon Q Developer.

Option D: "Convert audio files to text documents by using ML models" is incorrect as this refers to Amazon Transcribe, not Amazon Q Developer.

AWS AI Practitioner References:

Amazon Q Developer Features: AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.

The company is developing ML applications for various use cases, and the task is to select the correct ML paradigm (supervised or unsupervised learning) for each. Supervised learning involves training a model on labeled data to make predictions, while unsupervised learning identifies patterns or structures in unlabeled data. Each use case aligns with one of these paradigms based on its requirements.

Exact Extract from AWS AI Documents:

From the AWS AI Practitioner Learning Path:

"Supervised learning uses labeled data to train models for tasks like classification (e.g., binary or multi-class classification), where the model predicts a category. Unsupervised learning works with unlabeled data for tasks like clustering (e.g., K-means clustering) or dimensionality reduction, identifying patternsor reducing data complexity without predefined labels."

(Source: AWS AI Practitioner Learning Path, Module on Machine Learning Strategies)

Detailed Explanation:

Binary classification: Supervised learningBinary classification involves predicting one of two classes (e.g., yes/no, spam/not spam) using labeled data, making it a supervised learning task. The model learns from examples where the correct class is provided.

Multi-class classification: Supervised learningMulti-class classification extends binary classification to predict one of multiple classes (e.g., categorizing items into several groups). Like binary classification, it requires labeled data, so it falls under supervised learning.

K-means clustering: Unsupervised learningK-means clustering groups data into clusters based on similarity, without requiring labeled data. This is a classic unsupervised learning task, as the algorithm identifies patterns in the data on its own.

Dimensionality reduction: Unsupervised learningDimensionality reduction (e.g., using techniques like PCA) reduces the number of features in a dataset while preserving important information. It does not require labeled data, making it an unsupervised learning task.

Hotspot Selection Analysis:

The hotspot lists four use cases, each with a dropdown containing "Select...," "Supervised learning," and "Unsupervised learning." The correct selections are:

Binary classification: Supervised learning

Multi-class classification: Supervised learning

K-means clustering: Unsupervised learning

Dimensionality reduction: Unsupervised learning

Each paradigm (supervised and unsupervised learning) is used twice, as the question allows for paradigms to be selected one or more times.

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. References: AWS Bedrock Documentation.

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 correct answer is C – A CSV file that consists of measurement data, which is a classic example of structured data. According to AWS documentation, structured data is data that is organized in a predefined schema, typically stored in tabular formats with fixed rows, columns, and data types. CSV files, relational databases, and spreadsheets fall into this category because their structure allows deterministic querying and processing. Measurement data contained in a CSV file is easy to analyze with SQL, Amazon Athena, Amazon Redshift, or SageMaker Data Wrangler. In contrast, options A and D (text comments and transcriptions) are examples of unstructured data, requiring NLP techniques. Option B (video files) represents unstructured multimedia data, requiring computer vision. Structured data is essential for supervised ML models, business analytics, and statistical modeling. AWS emphasizes that structured data provides the simplest path to feature engineering and model training due to its consistent format.

Referenced AWS Documentation:

AWS Data Analytics Whitepaper – Structured vs. Unstructured Data

Amazon SageMaker Data Wrangler Documentation

The correct answer is C – Amazon Kendra, AWS’s enterprise search service designed for organizations with large, diverse document repositories. Kendra uses machine learning and natural language understanding (NLU) to provide semantic search, meaning it retrieves results based on meaning rather than keyword matching. According to AWS documentation, Kendra is ideal for educational, enterprise, and knowledge-management scenarios where users need fast, accurate retrieval across PDFs, HTML, Office documents, FAQs, and multimedia transcripts. Kendra connectors can index content from S3, SharePoint, LMS platforms, and internal databases, making it perfect for large volumes of training materials. Amazon Comprehend (A) is for NLP tasks like entity extraction, not enterprise search. Amazon Textract (B) extracts text from PDFs and scanned materials but does not provide search capabilities. Amazon Personalize (D) is for personalized recommendations, not document retrieval. Kendra is purpose-built for enterprise search and aligns directly with the company's needs.

Referenced AWS Documentation:

Amazon Kendra Developer Guide – Enterprise Search

AWS ML Specialty Guide – Intelligent Search Systems

The correct answer is B because Amazon Bedrock Prompt Management supports tool use via tools configuration, which enables a prompt to define tools that can invoke external APIs or services.

According to the AWS documentation:

"You can use the tools configuration in Amazon Bedrock to specify external APIs that a foundation model can call during inference. This enables the model to interact with external services, such as invoking functions, retrieving real-time data, or executing workflows."

The tools configuration allows a prompt to describe which external functions (APIs) are available, their parameters, and how they should be invoked, similar to OpenAI’s function calling or tool use pattern.

A prompt template defines how the model is structured and guided (system prompts, roles, guardrails).

An attack that reveals or leaks this prompt template is known as a prompt extraction attack.

The other options (persona switching, exploiting friendliness, ignoring prompts) describe adversarial techniques but do not directly expose the internal configured behavior.

???? Reference:

AWS Responsible AI – Prompt Injection & Extraction Attacks

The average response time is the correct metric for measuring the runtime efficiency of operating AI models.

Average Response Time:

Refers to the time taken by the model to generate an output after receiving an input. It is a key metric for evaluating the performance and efficiency of AI models in production.

A lower average response time indicates a more efficient model that can handle queries quickly.

Why Option C is Correct:

Measures Runtime Efficiency: Directly indicates how fast the model processes inputs and delivers outputs, which is critical for real-time applications.

Performance Indicator: Helps identify potential bottlenecks and optimize model performance.

Why Other Options are Incorrect:

A. Customer satisfaction score (CSAT): Measures customer satisfaction, not model runtime efficiency.

B. Training time for each epoch: Measures training efficiency, not runtime efficiency during model operation.

D. Number of training instances: Refers to data used during training, not operational efficiency.

The context window determines how much information can fit into a single prompt when using a large language model (LLM) like those on Amazon Bedrock.

Context Window:

The context window is the maximum amount of text (measured in tokens) that a language model can process in a single pass.

For LLM applications, the size of the context window limits how much input data, such as text for sentiment analysis, can be fed into the model at once.

Why Option B is Correct:

Determines Prompt Size: The context window size directly informs how much information (e.g., words or sentences) can fit in one prompt.

Model Capacity: The larger the context window, the more information the model can consider for generating outputs.

Why Other Options are Incorrect:

A. Temperature: Controls randomness in model outputs but does not affect the prompt size.

C. Batch size: Refers to the number of training samples processed in one iteration, not the amount of information in a prompt.

D. Model size: Refers to the number of parameters in the model, not the input size for a single prompt.