The AWS Certified AI Practitioner certification checks your expertise in building, training, and deploying machine learning (ML) models when using AWS services. This guide from usawire.com will provide you with an overview of the exam topics, objectives, sample questions, and interview questions, along with their answers.
Exam Topics
- Machine Learning Fundamentals:
– Models of machine learning: supervised, unsupervised, and reinforcement learning.
– Core machine learning concepts: features, labels, training/testing and validation. - AWS Machine Learning Services:
– Amazon SageMaker
– AWS Deep Learning AMIs
– AWS Rekognition
– AWS Comprehend
– AWS Lex and Polly - Data Engineering:
– Data collection, transformation, and pre-processing
– Data storage options in AWS (e.g., S3, RDS, DynamoDB) - Exploratory Data Analysis:
– Data visualization and analysis techniques
– Identifying data anomalies - Model Training and Evaluation:
– Training models using Amazon SageMaker
– Hyperparameter tuning
– Model evaluation metrics like accuracy, precision and recall - Model Deployment and Monitoring:
– Deploying models with Amazon SageMaker
– Monitoring model performance
– Retraining models
Exam Objectives
- Understanding Machine Learning Concepts:
– Basic principles of machine learning and data sciences.
– Types of machine learning models and their applications. - Implement AI Solutions on AWS:
– Utilize AWS services to build, train, and deploy machine learning models.
– Understand the capabilities and limitations of AWS AI services. - Data Preprocessing and Analysis:
– Perform data preprocessing tasks.
– Conduct exploratory data analysis to understand data distribution and patterns. - Model Evaluation and Deployment:
– Evaluate model performance using appropriate metrics.
– Deploy machine learning models on AWS and monitor their performance.
Sample Questions
Here are few sample questions to help you understand about what can be asked in the AWS Certified AI Practitioner certification exam in the testing center.
- What is the primary purpose of any Amazon Sage Maker?
A. To store large datasets
B. To build, train, and deploy any machine learning modeling
C. To perform data visualization
D. To manage database services
Answer: B
- Which AWS services can be used for image and video analysis in 2024?
A. AWS Lex
B. AWS Rekognition
C. AWS Comprehend
D. AWS Polly
Answer: B
- What is a key advantage of using AWS Deep Learning AMIs?
A. They provide pre-configured environments for deep learning.
B. They are used for data storage.
C. They offer advanced database management.
D. They are used for network security.
Answer: A
- Which metric is most commonly used to evaluate the classification of models?
A. Mean Squared Error
B. Accuracy
C. Root Mean Squared Error
D. Mean Absolute Error
Answer: B
- How can one monitor the performance of a deployed model in an Amazon SageMaker?
A. Using AWS CloudTrail
B. Using Amazon CloudWatch
C. Using AWS IAM
D. Using Amazon RDS
Answer: B
Interview Questions and Answers (AWS Certified AI Practitioner)
Interview questions give you an idea about what can be asked during a job interview after getting AWS Certified AI Practitioner certified.
- Q: What are the types of machine learning, and how do they differ?
A: The three main types of machine learning are supervised, unsupervised and reinforcement learnings. Supervised learning involves training a model on labeled data, unsupervised learning involves finding patterns in data without labels, and reinforcement learning involves training models through trial and error interactions with an environment.
- Q: How does Amazon SageMaker can simplify the machine learning workflow?
A: Amazon SageMaker provides tools for every step of the machine learning process, including data labeling, data preparation, model training, model tuning, model deployment, and monitoring. This integrated environment helps to streamline the workflow and makes it super easier to manage/test machine learning projects in 2024.
- Q: Can you explain the concept of hyperparameter tuning?
A : Hyper-parameter tuning involves optimizing the hyperparameters of a machine learning model in order to improve its performance. This process is critical because the choice of hyperparameters can significantly impact the accuracy and efficiency of the model.
- Q: What is AWS Rekognition used for?
A: AWS Rekognition is an AWS service that provides image and video analysis capabilities. It can be used for tasks such as object detection, facial recognition, and scene detection.
- Q: How do you ensure the security of data used in AWS machine learning projects?
A: Ensuring data security involves using AWS security features such as encryption (both at rest and in transit), IAM roles and policies for access control, VPC for network isolation, and logging and monitoring services like CloudTrail and CloudWatch.
One Stop Solution
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