Amazon Web Services Updated MLS-C01 Exam Questions and Answers by alexandru

AWS Glue jobs is the AWS service or feature that will meet the requirements with the least development effort over time. AWS Glue jobs is a fully managed service that enables data engineers to run Apache Spark applications on a serverless Spark environment. AWS Glue jobs can perform batch preprocessing data transformations on large datasets stored in Amazon S3, such as converting data formats, filtering data, joining data, and aggregating data. AWS Glue jobs can also scale the Spark environment automatically based on the data volume and processing needs, without requiring any infrastructure provisioning or management. AWS Glue jobs can reduce the development effort and time by providing a graphical interface to create and monitor Spark applications, as well as a code generation feature that can generate Scala or Python code based on the data sources and targets. AWS Glue jobs can also integrate with other AWS services, such as Amazon Athena, Amazon EMR, and Amazon SageMaker, to enable further data analysis and machine learning tasks1.

The other options are either more complex or less scalable than AWS Glue jobs. Amazon EMR cluster is a managed service that enables data engineers to run Apache Spark applications on a cluster of Amazon EC2 instances. However, Amazon EMR cluster requires more development effort and time than AWS Glue jobs, as it involves setting up, configuring, and managing the cluster, as well as writing and deploying the Spark code. Amazon EMR cluster also does not scale automatically, but requires manual or scheduled resizing of the cluster based on the data volume and processing needs2. Amazon Athena is a serverless interactive query service that enables data engineers to analyze data stored in Amazon S3 using standard SQL. However, Amazon Athena is not suitable for performing complex data transformations, such as joining data from multiple sources, aggregating data, or applying custom logic. Amazon Athena is also not designed for running Spark applications, but only supports SQL queries3. AWS Lambda is a serverless compute service that enables data engineers to run code without provisioning or managing servers. However, AWS Lambda is not optimized for running Spark applications, as it has limitations on the execution time, memory size, and concurrency of the functions. AWS Lambda is also not integrated with Amazon S3, and requires additional steps to read and write data from S3 buckets.

1: AWS Glue - Fully Managed ETL Service - Amazon Web Services

2: Amazon EMR - Amazon Web Services

3: Amazon Athena – Interactive SQL Queries for Data in Amazon S3

[4]: AWS Lambda – Serverless Compute - Amazon Web Services

A convolutional neural network (CNN) is a type of deep learning model that can learn to extract features from images and perform tasks such as classification, segmentation, and detection1. ResNet is a popular CNN architecture that uses residual connections to overcome the problem of vanishing gradients and enable very deep networks2. For the task of predicting product quality based on sensor data, a CNN and ResNet approach can leverage the spatial structure of the data and learn complex patterns that distinguish different quality levels.

Convolutional Neural Networks (CNNs / ConvNets)

PyTorch ResNet: The Basics and a Quick Tutorial

For a model with low accuracy on both training and testing datasets, the following two strategies are effective:

Increase the number of passes and perform hyperparameter tuning: This approach allows the model to better learn from the existing data and improve performance through optimized hyperparameters.

Add domain-specific features and use more complex models: Adding relevant features that capture additional information from domain knowledge and using more complex model architectures can help the model capture patterns better, potentially improving accuracy.

Options B, D, and E would either reduce feature complexity or training data volume, which is less likely to improve performance when accuracy is low on both training and testing sets.

To capture word context and sequential QA information, the embedding vectors need to consider both the order and the meaning of the words in the text.

Option B, Amazon SageMaker BlazingText algorithm in Skip-gram mode, is a valid option because it can learn word embeddings that capture the semantic similarity and syntactic relations between words based on their co-occurrence in a window of words. Skip-gram mode can also handle rare words better than continuous bag-of-words (CBOW) mode1.

Option E, combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN), is another valid option because it can leverage the advantages of Skip-gram mode and also use an RNN to model the sequential nature of the text. An RNN can capture the temporal dependencies and long-term dependencies between words, which are important for QA tasks2.

Option A, Amazon SageMaker seq2seq algorithm, is not a valid option because it is designed for sequence-to-sequence tasks such as machine translation, summarization, or chatbots. It does not produce embedding vectors for text series, but rather generates an output sequence given an input sequence3.

Option C, Amazon SageMaker Object2Vec algorithm, is not a valid option because it is designed for learning embeddings for pairs of objects, such as text-image, text-text, or image-image. It does not produce embedding vectors for text series, but rather learns a similarity function between pairs of objects4.

Option D, Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode, is not a valid option because it does not capture word context as well as Skip-gram mode. CBOW mode predicts a word given its surrounding words, while Skip-gram mode predicts the surrounding words given a word. CBOW mode is faster and more suitable for frequent words, but Skip-gram mode can learn more meaningful embeddings for rare words1.

1: Amazon SageMaker BlazingText

2: Recurrent Neural Networks (RNNs)

3: Amazon SageMaker Seq2Seq

4: Amazon SageMaker Object2Vec