Practice Free AIF-C01 Exam Online Questions
A financial company wants to build workflows for human review of ML predictions. The company wants to define confidence thresholds for its use case and adjust the threshold over time.
Which AWS service meets these requirements?
- A . Amazon Personalize
- B . Amazon Augmented AI (Amazon A2I)
- C . Amazon Inspector
- D . AWS Audit Manager
B
Explanation:
The correct answer is B because Amazon Augmented AI (Amazon A2I) allows developers to integrate human review workflows into ML systems. It supports defining confidence thresholds, such that only low-confidence predictions are sent to human reviewers.
From AWS documentation:
"Amazon A2I provides built-in human review workflows for ML predictions. You can configure confidence thresholds to determine when human review is triggered, enabling continual adjustment based on accuracy needs."
This supports use cases where business decisions (like financial approvals) require manual oversight for edge cases.
Explanation of other options:
An airline company wants to use a generative AI model to convert a flight booking system from one coding language into another coding language. The company must select a model for this task.
Which criteria should the company use to select the correct generative AI model for this task?
- A . Syntax, semantic understanding, and code optimization capabilities
- B . Code generation speed and error handling capabilities
- C . Ability to generate creative content
- D . Model size and resource requirements
A
Explanation:
The correct answer is A because code translation tasks require a model with deep understanding of syntax, semantic meaning, and the ability to maintain functional equivalence in different programming languages. Some foundation models offered via Amazon Bedrock (like Code Whisperer or Meta’s Code LLMs) are designed with these capabilities in mind.
From AWS documentation:
"Generative AI models used for code generation and translation must understand programming semantics and syntax to generate accurate and secure code. These models are trained to recognize language-specific patterns and preserve logic when converting between languages."
Explanation of other options:
B. Speed and error handling are secondary to correctness and comprehension in code translation tasks.
C. Creative content generation is not relevant for deterministic tasks like source code translation.
D. Model size may affect performance or latency but is not a primary selection criterion for translation accuracy.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Bedrock Model Selection Guide C Code Generation and Translation
AWS Developer Tools C Code Whisperer Capabilities
AWS ML Specialty Study Guide C LLM Use Cases in Software Engineering
Which scenario represents a practical use case for generative AI?
- A . Using an ML model to forecast product demand
- B . Employing a chatbot to provide human-like responses to customer queries in real time
- C . Using an analytics dashboard to track website traffic and user behavior
- D . Implementing a rule-based recommendation engine to suggest products to customers
B
Explanation:
Generative AI is a type of AI that creates new content, such as text, images, or audio, often mimicking human-like outputs. A practical use case for generative AI is employing a chatbot to provide human-like responses to customer queries in real time, as it leverages the ability of large language models (LLMs) to generate natural language responses dynamically.
Exact Extract from AWS AI Documents:
From the AWS Bedrock User Guide:
"Generative AI enables applications like chatbots to produce human-like text responses in real time, enhancing customer support by providing natural and contextually relevant answers to user queries."
(Source: AWS Bedrock User Guide, Introduction to Generative AI)
Detailed
Option A: Using an ML model to forecast product demand Forecasting product demand typically involves predictive analytics using supervised learning (e.g., regression models), not generative AI, which focuses on creating new content.
Option B: Employing a chatbot to provide human-like responses to customer queries in real time This is the correct answer. Generative AI, particularly LLMs, is commonly used to power chatbots that generate human-like responses, making this a practical use case.
Option C: Using an analytics dashboard to track website traffic and user behavior An analytics dashboard involves data visualization and analysis, not generative AI, which is about creating new content.
Option D: Implementing a rule-based recommendation engine to suggest products to customers A rule-based recommendation engine relies on predefined rules, not generative AI. Generative AI could be used for more dynamic recommendations, but this scenario does not describe such a case.
Reference: AWS Bedrock User Guide: Introduction to Generative AI (https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html)
AWS AI Practitioner Learning Path: Module on Generative AI Applications
AWS Documentation: Generative AI Use Cases (https://aws.amazon.com/generative-ai/)
A company has implemented a generative AI solution to create personalized exercise routines for premium subscription users. The company offers free basic subscriptions and paid premium subscriptions.
The company wants to evaluate the AI solution’s return on investment over time.
- A . The average revenue per user (ARPU) over the past month
- B . The number of daily interactions by basic subscription users
- C . The conversion rate and the customer retention rate
- D . The decrease in the number of premium customer queries and issue volume
HOTSPOT
A company has multiple datasets that contain historical data. The company wants to use ML technologies to process each dataset.
Select the correct ML technology from the following list for each dataset. Select each ML technology one time or not at all. (Select THREE.)
Computer vision
Natural language processing (NLP)
Reinforcement learning
Time series forecasting
Explanation:
Dataset 1: A dataset that contains text-based customer reviews → Natural language processing (NLP) NLP is designed for analyzing text (sentiment analysis, text classification, etc.).
Dataset 2: A dataset that contains images of animals labeled with their species names → Computer vision
Computer vision models classify or detect objects in images.
Dataset 3: A dataset that contains daily sales volumes for products → Time series forecasting
Time series forecasting predicts future values based on historical sequential data (like sales, demand, stock prices).
A company has a database of petabytes of unstructured data from internal sources. The company wants to transform this data into a structured format so that its data scientists can perform machine learning (ML) tasks.
Which service will meet these requirements?
- A . Amazon Lex
- B . Amazon Rekognition
- C . Amazon Kinesis Data Streams
- D . AWS Glue
D
Explanation:
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:
Which type of AI model makes numeric predictions?
- A . Diffusion
- B . Regression
- C . Transformer
- D . Multi-modal
B
Explanation:
The regression model is a fundamental type of supervised machine learning algorithm that is specifically designed to make numeric predictions. In regression tasks, the goal is to predict a continuous numerical value based on input features. This contrasts with classification, which predicts discrete labels.
According to AWS documentation:
“Regression models are used for predicting a continuous value. Examples include predicting house prices, stock market prices, or customer credit limits.”
(Reference: AWS Machine Learning Foundations: Regression, AWS AI Practitioner Study Guide) Option A (Diffusion) relates to generative models and is not primarily used for numeric prediction.
Option C (Transformer) is a neural network architecture, often used for sequence modeling tasks (e.g., NLP).
Option D (Multi-modal) describes a model handling multiple data types, not specifically numeric prediction.
Reference: AWS AI/ML Learning Path C Regression Models
AWS Certified AI Practitioner Study Guide (Pearson)
A company wants to use Amazon Bedrock. The company needs to review which security aspects the company is responsible for when using Amazon Bedrock.
- A . Patching and updating the versions of Amazon Bedrock
- B . Protecting the infrastructure that hosts Amazon Bedrock
- C . Securing the company’s data in transit and at rest
- D . Provisioning Amazon Bedrock within the company network
C
Explanation:
With Amazon Bedrock, AWS handles infrastructure security and patching (shared responsibility model).
Customers are responsible for securing their data (encryption, IAM, policies) both in transit and at rest.
Provisioning infrastructure (D) and platform patching (A, B) are AWS responsibilities.
Reference: AWS Shared Responsibility Model
A company wants to keep its foundation model (FM) relevant by using the most recent data. The company wants to implement a model training strategy that includes regular updates to the FM.
Which solution meets these requirements?
- A . Batch learning
- B . Continuous pre-training
- C . Static training
- D . Latent training
What does an F1 score measure in the context of foundation model (FM) performance?
- A . Model precision and recall.
- B . Model speed in generating responses.
- C . Financial cost of operating the model.
- D . Energy efficiency of the model’s computations.
A
Explanation:
The F1 score is the harmonic mean of precision and recall, making it a balanced metric for evaluating model performance when there is an imbalance between false positives and false negatives. Speed, cost, and energy efficiency are unrelated to the F1 score.
Reference: AWS Foundation Models Guide.