1. A company has a large dataset that is stored in an S3 bucket, and they need to perform some complex data transformations before training a machine learning model. Which AWS service can be used for this purpose?

A) AWS Glue
B) Amazon QuickSight
C) Amazon Kinesis Data Analytics
D) Amazon Athena
E) Amazon SageMaker


2. Which AWS service can be used to automate ETL workflows to transform and enrich data at scale?

A) Amazon S3
B) AWS Data Pipeline
C) AWS Glue
D) AWS AppSync
E) Amazon QuickSight


3. You are building a machine learning model for a customer who wants to predict customer churn based on various demographic and behavioral features. As part of the model evaluation process, you want to generate visualizations to gain insights into the relationship between the features and the target variable. Which of the following AWS services would you use for data analysis and visualization?

A) Amazon S3
B) Amazon DynamoDB
C) Amazon QuickSight
D) Amazon Elastic MapReduce (EMR)
E) Amazon Kinesis Data Analytics


4. You are working on a machine learning project that involves analyzing large volumes of unstructured data such as text, images, and videos. You want to visualize the data to gain insights and evaluate the performance of the machine learning models. Which of the following AWS services would you use for data analysis and visualization?

A) Amazon Comprehend
B) Amazon Redshift
C) Amazon SageMaker
D) Amazon Elasticsearch Service
E) Amazon SQS (Simple Queue Service)


5. You are training a deep learning model on a large dataset with millions of images. You want to speed up the training process by using transfer learning. Which of the following pre-trained models in Amazon SageMaker can be used for transfer learning on images?

A) PCA
B) k-means
C) Linear Learner
D) ResNet-50
E) XGBoost