1. A company is building a real-time data streaming application to process a large volume of data from different sources. The application requires a service to ingest and store the data in real-time, as well as analyze and visualize the data for insights. Which AWS service should they use?

A) AWS Glue, because it provides ETL (Extract, Transform, Load) capabilities for data preparation and analysis.
B) Amazon Kinesis Data Firehose, because it is a fully managed service for delivering real-time streaming data to data stores and analytics services.
C) Amazon S3, because it is a cost-effective way to store and retrieve large volumes of data.
D) Amazon Redshift, because it provides fast querying and analysis of large datasets.



2. Which of the following statements is true regarding the use of Notebooks and IDEs in AWS SageMaker?

A) Jupyter Notebooks in SageMaker do not support version control for code and data artifacts.
B) SageMaker Studio supports collaboration between multiple users working on the same Notebook or IDE environment simultaneously.
C) SageMaker Studio provides a built-in profiler that supports profiling of PyTorch and TensorFlow models.
D) SageMaker Notebook instances can only be accessed through the AWS Management Console.



3. You are building a machine learning model to predict the probability of loan default for a financial institution. As part of the model evaluation process, you want to generate visualizations to explore the distribution of loan default probabilities across different loan types and customer segments. Which of the following AWS services would you use for data analysis and visualization?

A) Amazon QuickSight
B) Amazon S3
C) Amazon SageMaker Studio
D) Amazon Redshift
E) Amazon Comprehend


4. A retail company wants to build an ML model that can predict customer churn based on customer behavior and transactional data. Which AWS ML application service would be most appropriate for this use case?

A) Amazon Forecast
B) Amazon Comprehend
C) Amazon Lex
D) Amazon Personalize
E) Amazon SageMaker


5. You are working on a project that involves analyzing large volumes of time-series data such as sensor readings, log files, and stock prices. You want to visualize the data to identify patterns and trends and use machine learning algorithms to make predictions. Which of the following AWS services would you use for data analysis and visualization?

A) Amazon Rekognition
B) Amazon Kinesis Data Firehose
C) Amazon Athena
D) Amazon QuickSight
E) Amazon Pinpoint