Free Practice Questions for Amazon Web Services MLS-C01 Exam

The algorithms that are best suited to this scenario are latent Dirichlet allocation (LDA) and neural topic modeling (NTM), as they are both unsupervised learning methods that can discover abstract topics from a collection of text documents. LDA and NTM can provide a list of the top words for each topic, as well as the topic distribution for each document, which can help the auditors assess the relevance and priority of the topic12.

The other options are not suitable because:

Option B: A random forest classifier is a supervised learning method that can perform classification or regression tasks by using an ensemble of decision trees. A random forest classifier is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes3.

Option D: A linear support vector machine is a supervised learning method that can perform classification or regression tasks by using a linear function that separates the data into different classes. A linear support vector machine is not suitable for discovering abstract topics from text documents, as it requires labeled data and predefined classes4.

Option E: A linear regression is a supervised learning method that can perform regression tasks by using a linear function that models the relationship between a dependent variable and one or more independent variables. A linear regression is not suitable for discovering abstract topics from text documents, as it requires labeled data and a continuous output variable5.

1: Latent Dirichlet Allocation

2: Neural Topic Modeling

3: Random Forest Classifier

4: Linear Support Vector Machine

5: Linear Regression

The company wants to predict the demand for a new product that will soon be launched, based on the sales history of similar products. This is a time series forecasting problem, which requires a machine learning algorithm that can learn from historical data and generate future predictions.

One of the most suitable solutions for this problem is to use the Amazon SageMaker DeepAR algorithm, which is a supervised learning algorithm for forecasting scalar time series using recurrent neural networks (RNN). DeepAR can handle multiple related time series, such as the sales of different products, and learn a global model that captures the common patterns and trends across the time series. DeepAR can also generate probabilistic forecasts that provide confidence intervals and quantify the uncertainty of the predictions.

DeepAR can outperform traditional forecasting methods, such as ARIMA, especially when the dataset contains hundreds or thousands of related time series. DeepAR can also use the trained model to forecast the demand for new products that are similar to the ones it has been trained on, by using the categorical features that encode the product attributes. For example, the company can use the product type, brand, flavor, size, and price as categorical features to group the products and learn the typical behavior for each group.

Therefore, the Machine Learning Specialist should apply the Amazon SageMaker DeepAR algorithm to forecast the demand for the new product, by using the sales history of the existing products as the training dataset, and the product attributes as the categorical features.

DeepAR Forecasting Algorithm - Amazon SageMaker

Now available in Amazon SageMaker: DeepAR algorithm for more accurate time series forecasting

To create a serverless ingestion and analytics solution for high-velocity, real-time streaming data, the Data Scientist should use the following AWS services:

AWS Glue Data Catalog: This is a managed service that acts as a central metadata repository for data assets across AWS and on-premises data sources. The Data Scientist can use AWS Glue Data Catalog to create a schema of the incoming data format, which defines the structure, format, and data types of the JSON records. The schema can be used by other AWS services to understand and process the data1.

Amazon Kinesis Data Firehose: This is a fully managed service that delivers real-time streaming data to destinations such as Amazon S3, Amazon Redshift, Amazon Elasticsearch Service, and Splunk. The Data Scientist can use Amazon Kinesis Data Firehose to stream the data from the source and transform the data to a query-optimized, columnar format such as Apache Parquet or ORC using the AWS Glue Data Catalog before delivering to Amazon S3. This enables efficient compression, partitioning, and fast analytics on the data2.

Amazon S3: This is an object storage service that offers high durability, availability, and scalability. The Data Scientist can use Amazon S3 as the output datastore for the transformed data, which can be organized into buckets and prefixes according to the desired partitioning scheme. Amazon S3 also integrates with other AWS services such as Amazon Athena, Amazon EMR, and Amazon Redshift Spectrum for analytics3.

Amazon Athena: This is a serverless interactive query service that allows users to analyze data in Amazon S3 using standard SQL. The Data Scientist can use Amazon Athena to run SQL queries against the data in Amazon S3 and connect to existing business intelligence dashboards using the Athena Java Database Connectivity (JDBC) connector. Amazon Athena leverages the AWS Glue Data Catalog to access the schema information and supports formats such as Parquet and ORC for fast and cost-effective queries4.

1: What Is the AWS Glue Data Catalog? - AWS Glue

2: What Is Amazon Kinesis Data Firehose? - Amazon Kinesis Data Firehose

3: What Is Amazon S3? - Amazon Simple Storage Service

4: What Is Amazon Athena? - Amazon Athena

 The best feature engineering technique to speed up the model training time without losing a lot of information from the original dataset is to use an autoencoder or principal component analysis (PCA) to replace original features with new features. An autoencoder is a type of neural network that learns a compressed representation of the input data, called the latent space, by minimizing the reconstruction error between the input and the output. PCA is a statistical technique that reduces the dimensionality of the data by finding a set of orthogonal axes, called the principal components, that capture the maximum variance of the data. Both techniques can help reduce the number of features and remove the noise and redundancy in the data, which can improve the model performance and speed up the training process. References:

AWS Machine Learning Specialty Exam Guide

AWS Machine Learning Training - Dimensionality Reduction for Machine Learning

AWS Machine Learning Training - Deep Learning with Amazon SageMaker

The problem of poor performance on the test data is a sign of overfitting, which means the model has learned the training data too well and failed to generalize to new and unseen data. To correct this, the Machine Learning Specialist should consider using methods that reduce the complexity of the model and increase its ability to generalize. Some of these methods are:

Increase regularization: Regularization is a technique that adds a penalty term to the loss function of the model, which reduces the magnitude of the model weights and prevents overfitting. There are different types of regularization, such as L1, L2, and elastic net, that apply different penalties to the weights1.

Increase dropout: Dropout is a technique that randomly drops out some units or connections in the neural network during training, which reduces the co-dependency of the units and prevents overfitting. Dropout can be applied to different layers of the network, and the dropout rate can be tuned to control the amount of dropout2.

Decrease feature combinations: Feature combinations are the interactions between different input features that can be used to create new features for the model. However, too many feature combinations can increase the complexity of the model and cause overfitting. Therefore, the Specialist should decrease the number of feature combinations and select only the most relevant and informative ones for the model3.

1: Regularization for Deep Learning - Amazon SageMaker

2: Dropout - Amazon SageMaker

3: Feature Engineering - Amazon SageMaker

A prediction power score of 1 indicates that the feature perfectly predicts the target variable, which usually suggests target leakage. Target leakage occurs when the feature includes information that would not be available at prediction time, leading to unrealistic performance.

From AWS documentation:

“Prediction power scores close to 1 might suggest target leakage in your dataset, as the feature may contain information about the target that wouldn’t be available at inference.”

— AWS SageMaker Data Wrangler documentation

The solution C will meet the requirements with the least development effort because it uses Amazon Rekognition and AWS CloudTrail, which are fully managed services that can provide the desired functionality. The solution C involves the following steps:

Use Amazon Rekognition to identify celebrities in the pictures. Amazon Rekognition is a service that can analyze images and videos and extract insights such as faces, objects, scenes, emotions, and more. Amazon Rekognition also provides a feature called Celebrity Recognition, which can recognize thousands of celebrities across a number of categories, such as politics, sports, entertainment, and media. Amazon Rekognition can return the name, face, and confidence score of the recognized celebrities, as well as additional information such as URLs and biographies1.

Use AWS CloudTrail to capture IP address and timestamp details. AWS CloudTrail is a service that can record the API calls and events made by or on behalf of AWS accounts. AWS CloudTrail can provide information such as the source IP address, the user identity, the request parameters, and the response elements of the API calls. AWS CloudTrail can also deliver the event records to an Amazon S3 bucket or an Amazon CloudWatch Logs group for further analysis and auditing2.

The other options are not suitable because:

Option A: Using AWS Panorama to identify celebrities in the pictures and using AWS CloudTrail to capture IP address and timestamp details will not meet the requirements effectively. AWS Panorama is a service that can extend computer vision to the edge, where it can run inference on video streams from cameras and other devices. AWS Panorama is not designed for identifying celebrities in pictures, and it may not provide accurate or relevant results. Moreover, AWS Panorama requires the use of an AWS Panorama Appliance or a compatible device, which may incur additional costs and complexity3.

Option B: Using AWS Panorama to identify celebrities in the pictures and making calls to the AWS Panorama Device SDK to capture IP address and timestamp details will not meet the requirements effectively, for the same reasons as option A. Additionally, making calls to the AWS Panorama Device SDK will require more development effort than using AWS CloudTrail, as it will involve writing custom code and handling errors and exceptions4.

Option D: Using Amazon Rekognition to identify celebrities in the pictures and using the text detection feature to capture IP address and timestamp details will not meet the requirements effectively. The text detection feature of Amazon Rekognition is used to detect and recognize text in images and videos, such as street names, captions, product names, and license plates. It is not suitable for capturing IP address and timestamp details, as these are not part of the pictures that users upload. Moreover, the text detection feature may not be accurate or reliable, as it depends on the quality and clarity of the text in the images and videos5.

1: Amazon Rekognition Celebrity Recognition

2: AWS CloudTrail Overview

3: AWS Panorama Overview

4: AWS Panorama Device SDK

5: Amazon Rekognition Text Detection

The services that are integrated with Amazon SageMaker to track the information that the Machine Learning Specialist needs are AWS CloudTrail and Amazon CloudWatch. AWS CloudTrail is a service that records the API calls and events for AWS services, including Amazon SageMaker. AWS CloudTrail can track the actions performed by the Data Scientists, such as creating notebooks, training models, and deploying endpoints. AWS CloudTrail can also provide information such as the identity of the user, the time of the action, the parameters used, and the response elements returned. AWS CloudTrail can help the Machine Learning Specialist to monitor the usage and activity of Amazon SageMaker, as well as to audit and troubleshoot any issues1 Amazon CloudWatch is a service that collects and analyzes the metrics and logs for AWS services, including Amazon SageMaker. Amazon CloudWatch can track the performance and utilization of the Amazon SageMaker endpoints, such as the CPU and GPU utilization, the inference latency, the number of invocations, etc. Amazon CloudWatch can also track the errors and alarms that are generated when an endpoint is invoked, such as the model errors, the throttling errors, the HTTP errors, etc. Amazon CloudWatch can help the Machine Learning Specialist to optimize the operational performance and reliability of Amazon SageMaker, as well as to set up notifications and actions based on the metrics and logs

The solution C is the best option to identify and address training issues with the least development effort. The solution C involves the following steps:

Use the SageMaker Debugger vanishing_gradient and LowGPUUtilization built-in rules to detect issues. SageMaker Debugger is a feature of Amazon SageMaker that allows data scientists to monitor, analyze, and debug machine learning models during training. SageMaker Debugger provides a set of built-in rules that can automatically detect common issues and anomalies in model training, such as vanishing or exploding gradients, overfitting, underfitting, low GPU utilization, and more1. The data scientist can use the vanishing_gradient rule to check if the gradients are becoming too small and causing the training to not converge. The data scientist can also use the LowGPUUtilization rule to check if the GPU resources are underutilized and causing the training to be inefficient2.

Launch the StopTrainingJob action if issues are detected. SageMaker Debugger can also take actions based on the status of the rules. One of the actions is StopTrainingJob, which can terminate the training job if a rule is in an error state. This can help the data scientist to save time and money by stopping the training early if issues are detected3.

The other options are not suitable because:

Option A: Using CPU utilization metrics that are captured in Amazon CloudWatch and configuring a CloudWatch alarm to stop the training job early if low CPU utilization occurs will not identify and address training issues effectively. CPU utilization is not a good indicator of model training performance, especially for GPU instances. Moreover, CloudWatch alarms can only trigger actions based on simple thresholds, not complex rules or conditions4.

Option B: Using high-resolution custom metrics that are captured in Amazon CloudWatch and configuring an AWS Lambda function to analyze the metrics and to stop the training job early if issues are detected will incur more development effort than using SageMaker Debugger. The data scientist will have to write the code for capturing, sending, and analyzing the custom metrics, as well as for invoking the Lambda function and stopping the training job. Moreover, this solution may not be able to detect all the issues that SageMaker Debugger can5.

Option D: Using the SageMaker Debugger confusion and feature_importance_overweight built-in rules and launching the StopTrainingJob action if issues are detected will not identify and address training issues effectively. The confusion rule is used to monitor the confusion matrix of a classification model, which is not relevant for a regression model that predicts prices. The feature_importance_overweight rule is used to check if some features have too much weight in the model, which may not be related to the convergence or resource utilization issues2.

1: Amazon SageMaker Debugger

2: Built-in Rules for Amazon SageMaker Debugger

3: Actions for Amazon SageMaker Debugger

4: Amazon CloudWatch Alarms

5: Amazon CloudWatch Custom Metrics

 To use a custom algorithm in Amazon SageMaker, the Docker container image must have an executable file named train that acts as the ENTRYPOINT for the container. This file is responsible for running the training code and communicating with the Amazon SageMaker service. The train file must be in the PATH of the container and have execute permissions. The other options are not valid ways to package the Docker container for Amazon SageMaker. References:

Use Docker containers to build models - Amazon SageMaker

Create a container with your own algorithms and models - Amazon SageMaker