AWS Certified Data Analytics Specialty 2021 - Hands-On! - Amazon SageMaker

Amazon SageMaker is a powerful, fully managed machine learning service on AWS. It offers modules for building, training, and deploying models, supporting advanced algorithms and frameworks like TensorFlow and PyTorch. SageMaker provides high scalability, GPU acceleration, and hyperparameter tuning. It ensures security with encryption and integrates with AWS services like IAM and CloudWatch. While powerful, it requires Python proficiency and is suited for experienced users.

What is a key advantage of using Amazon SageMaker over Amazon's older ML service?

• It offers more flexibility and advanced algorithms.

• It requires no coding knowledge.

• It is easier to use.

• It is cheaper to use.

Which of the following is NOT a feature of the build module in SageMaker?

• Built-in reinforcement learning algorithms

• Automatic data labeling

• CUDA and CU DNN drivers for deep learning

• Preloaded Anaconda packages

What is the purpose of SageMaker's training module?

• To provide a managed environment for hosting models

• To allow for one-click model training and tuning

• To encrypt data at rest

• To integrate with CloudWatch

What does SageMaker Neo enable?

• Training models on local machines

• Running models anywhere in the cloud and at the edge

• Automatic hyperparameter tuning

• Real-time data preprocessing

Which security feature is NOT mentioned for SageMaker?

• SSL secured API and console access

• Integration with IAM roles

• Automatic data backup

• Encryption of data in transit and at rest

How does SageMaker ensure the security of your code?

• By using SSL for all data transfers

• By storing it in EBS volumes controlled by security groups

• By requiring two-factor authentication

• By storing it in encrypted S3 buckets

What is the role of Amazon CloudTrail in SageMaker?

• To monitor real-time metrics

• To manage compute infrastructure

• To record the history of API calls

• To encrypt data at rest