1. Which AWS machine learning (ML) application service should be used to train and deploy a natural language processing (NLP) model for sentiment analysis of social media data, given the requirement of minimizing training and deployment costs?

A) Amazon SageMaker Ground Truth
B) Amazon Comprehend
C) Amazon Translate
D) Amazon Rekognition
E) Amazon SageMaker


2. You are training a machine learning model on a large dataset with high-dimensional features. The training process is taking a long time, and you want to speed it up by using multiple GPUs. Which of the following AWS services would you use for distributed model training?

A) Amazon S3
B) Amazon Redshift
C) Amazon SageMaker
D) Amazon Elastic Inference
E) AWS Glue


3. Which of the following techniques is used in natural language processing (NLP) to represent words as dense vectors of real numbers, also known as word embeddings?

A) None of the above
B) Word2Vec
C) Principal Component Analysis (PCA)
D) Singular Value Decomposition (SVD)
E) Bag-of-words model


4. Which of the following machine learning models is supported by Amazon Machine Learning (Amazon ML)?

A) Long Short-Term Memory (LSTM)
B) Recurrent Neural Networks (RNNs)
C) Convolutional Neural Networks (CNNs)
D) Support Vector Machines (SVMs)
E) Random Forest (RF)


5. Which of the following AWS services provides a web-based notebook interface that can be used for data exploration, analysis, and machine learning model building?

A) Amazon EC2
B) AWS Glue
C) Amazon SageMaker Notebooks
D) Amazon EMR