2026 Amazon AIF-C01: Valid AWS Certified AI Practitioner Exam Tests

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Amazon AIF-C01 Exam Syllabus Topics:

SectionWeightObjectives
AI Application Development30%- Implement AI applications using AWS services
  • 1. Configure model parameters (temperature, top-p, top-k)
  • 2. Build applications using Amazon Bedrock APIs and SDKs
  • 3. Use knowledge bases for context-aware responses
  • 4. Implement prompt templates and chain-of-thought reasoning
- Evaluate and optimize AI applications
  • 1. Apply evaluation frameworks for AI applications
  • 2. Assess application outputs for relevance and accuracy
  • 3. Identify performance bottlenecks and optimization strategies
  • 4. Implement caching and cost optimization techniques
- Understand foundational concepts for building AI applications
  • 1. Describe prompt engineering techniques
  • 2. Explain prompts, tokens, and context windows
  • 3. Explain function calling and tool use in AI applications
  • 4. Identify retrieval-augmented generation (RAG) concepts
Responsible AI20%- Understand governance and compliance requirements
  • 1. Explain data privacy regulations affecting AI
  • 2. Describe model interpretability and explainability
  • 3. Recognize regulatory and ethical considerations
- Explain the principles of responsible AI
  • 1. Describe strategies for bias mitigation
  • 2. Define fairness, transparency, and privacy in AI systems
  • 3. Identify potential biases in AI/ML models
- Implement responsible AI best practices
  • 1. Implement appropriate guardrails for AI applications
  • 2. Apply human oversight in AI decision-making
  • 3. Evaluate AI outputs for quality and safety
AI/ML Fundamentals24%- Understand the AI/ML lifecycle
  • 1. Recognize data collection, preprocessing, and feature engineering stages
  • 2. Identify phases of the ML lifecycle
  • 3. Describe model evaluation and deployment considerations
- Identify foundational terminology and definitions
  • 1. Define models, algorithms, and parameters
  • 2. Describe underfitting and overfitting
  • 3. Define common ML metrics (accuracy, precision, recall, F1 score)
  • 4. Explain training, inference, and fine-tuning
- Explain the fundamental concepts of AI and ML
  • 1. Define generative AI and its key concepts
  • 2. Differentiate between AI, ML, and deep learning
  • 3. Identify common use cases for AI/ML
  • 4. Explain supervised and unsupervised learning
AWS AI Services Overview26%- Describe Amazon Bedrock capabilities
  • 1. Describe model invocation, prompts, and responses
  • 2. Identify Bedrock features (agents, knowledge bases, guardrails)
  • 3. Explain security, privacy, and compliance features
  • 4. Explain foundation models (FMs) available in Bedrock
- Describe Amazon SageMaker capabilities
  • 1. Recognize MLOps capabilities in SageMaker
  • 2. Describe built-in algorithms and SageMaker JumpStart
  • 3. Identify SageMaker features for ML workflows
  • 4. Explain model training and deployment options
- Describe AWS AI services for specific use cases
  • 1. Describe Amazon Polly, Rekognition, Transcribe, and Translate
  • 2. Identify services for NLP, computer vision, and recommendations
  • 3. Explain AWS HealthScribe and other domain-specific services
- Identify appropriate AWS AI services for given scenarios
  • 1. Evaluate use cases for Amazon Bedrock, SageMaker, and other AWS AI offerings
  • 2. Determine when to use pre-trained models vs. custom models
  • 3. Compare AWS AI services by capability and use case

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Amazon AWS Certified AI Practitioner Sample Questions (Q213-Q218):

NEW QUESTION # 213
A company wants to improve multiple ML models.
Select the correct technique from the following list of use cases. Each technique should be selected one time or not at all. (Select THREE.)
Few-shot learning Fine-tuning Retrieval Augmented Generation (RAG) Zero-shot learning

Answer:

Explanation:

Explanation:
AWS References:
Amazon Bedrock - Retrieval Augmented Generation (RAG)
AWS Generative AI Guide - Zero-shot & Few-shot learning


NEW QUESTION # 214
A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?

Answer: A

Explanation:
Amazon QuickSight is a fully managed business intelligence (BI) service that allows users to create and publish interactive dashboards that include visualizations like graphs, charts, and tables. "Amazon Q" is the natural language query feature within Amazon QuickSight. It enables users to ask questions about their data in natural language and receive visual responses such as graphs.
* Option C (Correct): "Amazon Q in Amazon QuickSight": This is the correct answer because Amazon QuickSight Q is specifically designed to allow users to explore their data through natural language queries, and it can automatically generate graphs to display sales data and other metrics. This makes it an ideal choice for the company to automate the generation of graphs showing total sales for its top-selling products across various retail locations.
* Option A, B, and D: These options are incorrect:
* A. Amazon Q in Amazon EC2: Amazon EC2 is a compute service that provides virtual servers, but it is not directly related to generating graphs or providing natural language querying features.
* B. Amazon Q Developer: This is not an existing AWS service or feature.
* D. Amazon Q in AWS Chatbot: AWS Chatbot is a service that integrates with Amazon Chime and Slack for monitoring and managing AWS resources, but it is not used for generating graphs based on sales data.
AWS AI Practitioner References:
* Amazon QuickSight Q is designed to provide insights from data by using natural language queries, making it a powerful tool for generating automated graphs and visualizations directly from queried data.
* Business Intelligence (BI) on AWS: AWS services such as Amazon QuickSight provide business intelligence capabilities, including automated reporting and visualization features, which are ideal for companies seeking to visualize data like sales trends over time.


NEW QUESTION # 215
Which feature of Amazon OpenSearch Service gives companies the ability to build vector database applications?

Answer: D

Explanation:
Amazon OpenSearch Service (formerly Amazon Elasticsearch Service) has introduced capabilities to support vector search, which allows companies to build vector database applications. This is particularly useful in machine learning, where vector representations (embeddings) of data are often used to capture semantic meaning.
Scalable index management and nearest neighbor search capability are the core features enabling vector database functionalities in OpenSearch. The service allows users to index high-dimensional vectors and perform efficient nearest neighbor searches, which are crucial for tasks such as recommendation systems, anomaly detection, and semantic search.
Here is why option C is the correct answer:
* Scalable Index Management: OpenSearch Service supports scalable indexing of vector data. This means you can index a large volume of high-dimensional vectors and manage these indexes in a cost- effective and performance-optimized way. The service leverages underlying AWS infrastructure to ensure that indexing scales seamlessly with data size.
* Nearest Neighbor Search Capability: OpenSearch Service's nearest neighbor search capability allows for fast and efficient searches over vector data. This is essential for applications like product recommendation engines, where the system needs to quickly find the most similar items based on a user's query or behavior.
* AWS AI Practitioner References:
* According to AWS documentation, OpenSearch Service's support for nearest neighbor search using vector embeddings is a key feature for companies building machine learning applications that require similarity search.
* The service uses Approximate Nearest Neighbors (ANN) algorithms to speed up searches over large datasets, ensuring high performance even with large-scale vector data.
The other options do not directly relate to building vector database applications:
* A. Integration with Amazon S3 for object storage is about storing data objects, not vector-based searching or indexing.
* B. Support for geospatial indexing and queries is related to location-based data, not vectors used in machine learning.
* D. Ability to perform real-time analysis on streaming data relates to analyzing incoming data streams, which is different from the vector search capabilities.


NEW QUESTION # 216
An online learning company with large volumes of education materials wants to use enterprise search.

Answer: C


NEW QUESTION # 217
A company is building an ML model. The company collected new data and analyzed the data by creating a correlation matrix, calculating statistics, and visualizing the data.
Which stage of the ML pipeline is the company currently in?

Answer: C


NEW QUESTION # 218
......

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