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| Certification Vendor: | Amazon Web Services (AWS) |
|---|---|
| Exam Name: | AWS Certified AI Practitioner |
| Exam Number: | AIF-C01 |
| Available Languages: | Portuguese (Brazil), Korean, Japanese, Simplified Chinese, English |
| Exam Format: | Multiple response, Multiple choice |
| Exam Price: | USD 100 |
| Real Exam Qty: | 80 |
| Related Certifications: | AWS Certified Cloud Practitioner AWS Certified Data Engineer - Associate AWS Certified Machine Learning Engineer - Associate |
| Certificate Validity Period: | 3 years |
| Passing Score: | 700 / 1000 |
| Exam Duration: | 90 minutes |
| Sample Questions: | Amazon AIF-C01 Sample Questions |
| Exam Way: | Online proctored exam (Pearson VUE) or in-person testing center |
| Pre Condition: | None required. Recommended: General IT cloud knowledge and basic understanding of AI/ML concepts. AWS Cloud Practitioner certification is a recommended prerequisite but not mandatory. |
| Official Syllabus URL: | https://aws.amazon.com/certification/certified-ai-practitioner/ |
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NEW QUESTION # 19
A company has a large amount of unlabeled data. The company wants to group the data based on feature similarities.
Which algorithm will meet this requirement?
Answer: D
Explanation:
Comprehensive and Detailed Explanation (AWS AI documents):
AWS machine learning fundamentals classify K-means as an unsupervised learning algorithm used to group unlabeled data into clusters based on feature similarity and distance metrics. Because the data is unlabeled and the goal is grouping rather than prediction, K-means is the most appropriate choice.
Why the other options are incorrect:
XGBoost is a supervised learning algorithm that requires labeled data.
DeepAR forecasting is designed for time series forecasting.
Linear learner is typically used for supervised regression or classification tasks.
AWS AI Study Guide Reference:
AWS unsupervised learning concepts
AWS clustering algorithms overview
NEW QUESTION # 20
A company wants to use Amazon Q Business for its data. The company needs to ensure the security and privacy of the data. Which combination of steps will meet these requirements? (Select TWO.)
Answer: C,D
Explanation:
The correct answers are A and E because both directly align with AWS best practices for securing generative AI services and data privacy in enterprise applications.
From the AWS Amazon Q Business documentation:
"AWS Key Management Service (KMS) integrates with Amazon Q Business to encrypt sensitive data at rest. You can use customer-managed KMS keys to meet compliance requirements." And:
"You must configure IAM access controls to manage which users and applications can access Amazon Q Business indexes, ensuring that only authorized users can retrieve information." Explanation of other options:
B . Cross-account access is not a common requirement for internal enterprise use of Amazon Q Business unless explicitly sharing data across organizations. It's not a requirement for securing access.
C . Amazon Inspector is a vulnerability management tool for EC2 and containers. It is unrelated to Amazon Q authentication or security.
D . Allowing public access would violate security and privacy principles and directly contradict the stated requirement.
Referenced AWS AI/ML Documents and Study Guides:
Amazon Q Business Developer Guide - Security and Identity Management
AWS KMS Documentation - Integration with Bedrock and Amazon Q
AWS Certified Machine Learning Specialty Guide - Responsible AI and Governance Section
NEW QUESTION # 21
A company wants to extract key insights from large policy documents to increase employee efficiency.
Answer: A
Explanation:
Comprehensive and Detailed
Summarization is a natural language processing (NLP) task that condenses long documents into concise, meaningful summaries while retaining the key information.
Regression predicts numerical values.
Clustering groups similar items.
Classification assigns data into predefined categories.
Reference:
AWS NLP Use Cases - Summarization
NEW QUESTION # 22
A company wants to build an ML application.
Select and order the correct steps from the following list to develop a well-architected ML workload. Each step should be selected one time. (Select and order FOUR.)
* Deploy model
* Develop model
* Monitor model
* Define business goal and frame ML problem
Answer:
Explanation:
Explanation:
Building a well-architected ML workload follows a structured lifecycle as outlined in AWS best practices.
The process begins with defining the business goal and framing the ML problem to ensure the project aligns with organizational objectives. Next, the model is developed, which includes data preparation, training, and evaluation. Once the model is ready, it is deployed tomake predictions in a production environment. Finally, the model is monitored to ensure it performs as expected and to address any issues like drift or degradation over time. This order ensures a systematic approach to ML development.
Exact Extract from AWS AI Documents:
From the AWS AI Practitioner Learning Path:
" The machine learning lifecycle typically follows these stages: 1) Define the business goal and frame the ML problem, 2) Develop the model (including data preparation, training, and evaluation), 3) Deploy the model to production, and 4) Monitor the model for performance and drift to ensure it continues to meet business needs.
"
(Source: AWS AI Practitioner Learning Path, Module on Machine Learning Lifecycle) Detailed Explanation:
Step 1: Define business goal and frame ML problemThis is the first step in any ML project. It involves understanding the business objective (e.g., reducing churn) and framing the ML problem (e.g., classification or regression). Without this step, the project lacks direction. The hotspot lists this option as " Define business goal and frame ML problem, " which matches this stage.
Step 2: Develop modelAfter defining the problem, the next step is to develop the model. This includes collecting and preparing data, selecting an algorithm, training the model, and evaluating its performance. The hotspot lists " Develop model " as an option, aligning with this stage.
Step 3: Deploy modelOnce the model is developed and meets performance requirements, it is deployed to a production environment to make predictions or automate decisions. The hotspot includes " Deploy model " as an option, which fits this stage.
Step 4: Monitor modelAfter deployment, the model must be monitored to ensure it performs well over time, addressing issues like data drift or performance degradation. The hotspot lists " Monitor model " as an option, completing the lifecycle.
Hotspot Selection Analysis:
The hotspot provides four steps, each with the same dropdown options: " Select..., " " Deploy model, " " Develop model, " " Monitor model, " and " Define business goal and frame ML problem. " The correct selections are:
Step 1: Define business goal and frame ML problem
Step 2: Develop model
Step 3: Deploy model
Step 4: Monitor model
Each option is used exactly once, as required, and follows the logical order of the ML lifecycle.
References:
AWS AI Practitioner Learning Path: Module on Machine Learning Lifecycle Amazon SageMaker Developer Guide: Machine Learning Workflow (https://docs.aws.amazon.com/sagemaker
/latest/dg/how-it-works-mlconcepts.html)
AWS Well-Architected Framework: Machine Learning Lens (https://docs.aws.amazon.com/wellarchitected
/latest/machine-learning-lens/)
NEW QUESTION # 23
A company uses a third-party model on Amazon Bediock to analyze confidential documents. The company Is concerned about data privacy.
Which statement describes how Amazon Bedrock protects data privacy?
Answer: B
NEW QUESTION # 24
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