Practice Test - AWS Certified AI Practitioner AIF-C01

Practice Test - AWS Certified AI Practitioner AIF-C01 free download

Prepare and pass your AIF-C01 exam in the first attempt

The AWS Certified AI Practitioner (AIF-C01) exam has the following scoring and format details:

A. Exam Format:

• Number of questions: 65

• Question types: Multiple choice and multiple response

• Duration: 90 minutes

• Delivery method: Online proctored or in-person at a Pearson VUE testing center

• Languages available: English, Japanese, Korean, Portuguese (Brazil), and Simplified Chinese

B. Scoring Details:

• Passing score: AWS does not publish the exact passing score, but it is typically around 700 out of 1000 for foundational-level exams.

• Scaled scoring: The exam uses a scaled scoring system, meaning your raw score (number of correct answers) is converted to a scale from 100 to 1000.

• Result: Pass or fail, with a breakdown of performance by domain.

C. Cost:

• Exam fee: $100 USD (plus applicable taxes or exchange rates)

D. Knowledge Areas

1. Fundamentals of AI and ML (20%)

• Understand key terms: AI, ML, deep learning, neural networks, NLP, computer vision, etc.

• Differentiate between AI, ML, and deep learning.

• Learn types of inferencing: batch vs. real-time.

• Understand data types: labeled/unlabeled, structured/unstructured, tabular, image, text, etc.

• Explore learning types: supervised, unsupervised, reinforcement learning.

2. Practical Use Cases for AI (30%)

• Identify real-world applications: fraud detection, recommendation systems, forecasting, etc.

• Evaluate when AI/ML is appropriate or not (e.g., cost-benefit analysis).

• Match ML techniques (regression, classification, clustering) to use cases.

3. AWS AI/ML Services (30%)

• Learn about AWS managed services:

  • Amazon SageMaker (model building and deployment)

  • Amazon Comprehend (text analysis)

  • Amazon Lex (chatbots)

  • Amazon Polly (text-to-speech)

  • Amazon Transcribe and Translate

• Understand how these services simplify AI/ML adoption.

4. ML Development Lifecycle (20%)

• Understand the stages of an ML pipeline:

  • Data collection

  • Exploratory data analysis (EDA)

  • Data preprocessing and feature engineering

  • Model training and tuning

  • Evaluation, deployment, and monitoring