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Amazon AIF-C01 Exam Questions
Free Amazon AIF-C01 Exam Actual Questions
Note: Premium Questions for AIF-C01 were last updated On Jul. 14, 2026 (see below)
An education company wants to build a private tutor application. The application will give users the ability to enter text or provide a picture of a question. The application will respond with a written answer and an explanation of the written answer.
Which model type meets these requirements?
Comprehensive and Detailed Explanation From Exact AWS AI documents:
A multimodal large language model (LLM) can:
Accept both text and image inputs
Understand visual and textual context
Generate coherent written explanations
AWS generative AI guidance positions multimodal LLMs as the best choice for applications requiring cross-modal understanding and text generation.
Why the other options are incorrect:
Computer vision (A) does not generate text explanations.
Diffusion models (C) generate images.
Text-to-speech (D) converts text to audio.
AWS AI document references:
Multimodal Foundation Models on AWS
Building AI Tutors with Generative Models
A company wants to use generative AI to increase developer productivity and software development. The company wants to use Amazon Q Developer.
What can Amazon Q Developer do to help the company meet these requirements?
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 Reference:
Amazon Q Developer Features: AWS documentation outlines how Amazon Q Developer supports developers by offering features that reduce manual effort and improve efficiency.
An AI practitioner who has minimal ML knowledge wants to predict employee attrition without writing code. Which Amazon SageMaker feature meets this requirement?
The correct answer is A because Amazon SageMaker Canvas is designed specifically for users with little or no machine learning or programming experience. It provides a visual interface to build ML models by simply uploading data, performing analysis, and generating predictions using a no-code environment.
From the AWS documentation:
'Amazon SageMaker Canvas enables business analysts and other users to generate accurate ML predictions using a visual, point-and-click interface without writing code or having prior ML experience.'
This feature allows the user to:
Import datasets (e.g., HR data)
Automatically explore the data
Select the prediction column (e.g., attrition)
Train the model
Generate and export predictions
Explanation of other options:
B . SageMaker Clarify is used to detect bias and explain ML predictions but not to build models or make predictions without code.
C . SageMaker Model Monitor monitors model quality in production but doesn't build or train models.
D . SageMaker Data Wrangler is used for data preprocessing and transformation but still requires some technical configuration.
Referenced AWS AI/ML Documents and Study Guides:
Amazon SageMaker Canvas Developer Guide
AWS Certified Machine Learning Specialty Study Guide -- AutoML and No-Code Tools Section
AWS Machine Learning Blog: ''Predict Employee Attrition with SageMaker Canvas''
A media streaming platform wants to provide movie recommendations to users based on the users' account history.
Amazon Personalize is a fully managed ML service for personalized recommendations (movies, products, music, etc.) based on user behavior and history.
Polly converts text to lifelike speech.
Comprehend performs NLP tasks like sentiment analysis.
Transcribe is speech-to-text.
Reference:
AWS Documentation -- Amazon Personalize
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention.
The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?
Comprehensive and Detailed Explanation From Exact AWS AI documents:
Prompt engineering is the primary method for controlling tone, style, and behavior of foundation model responses.
AWS generative AI guidance explains that:
Prompts can define tone, voice, and response structure
Iterative refinement ensures consistent outputs
Prompt refinement requires no model retraining
Why the other options are incorrect:
Token limits (A) affect length, not tone.
Batch inferencing (B) affects processing mode, not response style.
Higher temperature (D) increases randomness, reducing consistency.
AWS AI document references:
Prompt Engineering Best Practices
Controlling Model Output Tone
- Select Question Types you want
- Set your Desired Pass Percentage
- Allocate Time (Hours : Minutes)
- Create Multiple Practice tests with Limited Questions
- Customer Support
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