Amazon
MLS-C01 Exam
Questions

AWS Certified Machine Learning - Specialty MLS-C01

Amazon (MLS-C01) Exam Questions And Answers page 19

Which model is suitable for categorizing new products using a labeled dataset with 15 features and 1,200 products?

Single Choice

A retail company intends to use machine learning to categorize new products. A labeled dataset of current products was provided to the Data Science team. The dataset includes 1,200 products. The labeled dataset has 15 features for each product such as title dimensions, weight, and price. Each product is labeled as belonging to one of six categories such as books, games, electronics, and movies.

Which model should be used for categorizing new products using the provided dataset for training?

AnXGBoost model where the objective parameter is set to multi:softmax

A deep convolutional neural network (CNN) with a softmax activation function for the last layer

A regression forest where the number of trees is set equal to the number of product categories

A DeepAR forecasting model based on a recurrent neural network (RNN)

Model Development Machine Learning Implementation and Operations

What Additional Data Sources Can Enhance ML Review Analysis?

Multiple Choice

A retail company is selling products through a global online marketplace. The company wants to use machine learning (ML) to analyze customer feedback and identify specific areas for improvement. A developer has built a tool that collects customer reviews from the online marketplace and stores them in an Amazon S3 bucket. This process yields a dataset of 40 reviews. A data scientist building the ML models must identify additional sources of data to increase the size of the dataset.

Which data sources should the data scientist use to augment the dataset of reviews? (Choose three.)

Emails exchanged by customers and the company s customer service agents

Social media posts containing the name of the company or its products

A publicly available collection of news articles

A publicly available collection of customer reviews

Product sales revenue figures for the company

Instruction manuals for the company s products

Exploratory Data Analysis Model Development

How can a data scientist adjust the Amazon Personalize solution to maintain increased sales during a marketing campaign?

Single Choice

A retail company is using Amazon Personalize to provide personalized product recommendations for its customers during a marketing campaign. The company sees a significant increase in sales of recommended items to existing customers immediately after deploying a new solution version, but these sales decrease a short time after deployment. Only historical data from before the marketing campaign is available for training.

How should a data scientist adjust the solution?

Use the event tracker in Amazon Personalize to include real-time user interactions.

Add user metadata and use the HRNN-Metadata recipe in Amazon Personalize.

Implement a new solution using the built-in factorization machines (FM) algorithm in Amazon SageMaker.

Add event type and event value fields to the interactions dataset in Amazon Personalize.

Exploratory Data Analysis Model Development

How can the ML team visualize the degradation of the sales forecasting model accurately in SageMaker Studio?

Single Choice

A retail company uses a machine learning (ML) model for daily sales forecasting. The company s brand manager reports that the model has provided inaccurate results for the past 3 weeks.

At the end of each day, an AWS Glue job consolidates the input data that is used for the forecasting with the actual daily sales data and the predictions of the model. The AWS Glue job stores the data in Amazon S3. The company s ML team is using an Amazon SageMaker Studio notebook to gain an understanding about the source of the model's inaccuracies.

What should the ML team do on the SageMaker Studio notebook to visualize the model's degradation MOST accurately?

Create a histogram of the daily sales over the last 3 weeks. In addition, create a histogram of the daily sales from before that period.

Create a histogram of the model errors over the last 3 weeks. In addition, create a histogram of the model errors from before that period.

Create a line chart with the weekly mean absolute error (MAE) of the model.

Create a scatter plot of daily sales versus model error for the last 3 weeks. In addition, create a scatter plot of daily sales versus model error from before that period.

Model Development Machine Learning Implementation and Operations

What solution can be used to combine and remove duplicates from two datasets with different structures?

Single Choice

A retail company wants to combine its customer orders with the product description data from its product catalog. The structure and format of the records in each dataset is different. A data analyst tried to use a spreadsheet to combine the datasets, but the effort resulted in duplicate records and records that were not properly combined. The company needs a solution that it can use to combine similar records from the two datasets and remove any duplicates.

Which solution will meet these requirements?

Use an AWS Lambda function to process the data. Use two arrays to compare equal strings in the fields from the two datasets and remove any duplicates.

Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Call the AWS Glue SearchTables API operation to perform a fuzzy-matching search on the two datasets, and cleanse the data accordingly.

Create AWS Glue crawlers for reading and populating the AWS Glue Data Catalog. Use the FindMatches transform to cleanse the data.

Create an AWS Lake Formation custom transform. Run a transformation for matching products from the Lake Formation console to cleanse the data automatically.

Data Engineering Exploratory Data Analysis

Which solution for automatic routing of customer claims without requiring ML expertise meets the company's requirements?

Single Choice

A retail company wants to update its customer support system. The company wants to implement automatic routing of customer claims to different queues to prioritize the claims by category.

Currently, an operator manually performs the category assignment and routing. After the operator classifies and routes the claim, the company stores the claim s record in a central database. The claim s record includes the claim s category.

The company has no data science team or experience in the field of machine learning (ML). The company s small development team needs a solution that requires no ML expertise.

Which solution meets these requirements?

Export the database to a .csv file with two columns: claim_label and claim_text. Use the Amazon SageMaker Object2Vec algorithm and the .csv file to train a model. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.

Export the database to a .csv file with one column: claim_text. Use the Amazon SageMaker Latent Dirichlet Allocation (LDA) algorithm and the .csv file to train a model. Use the LDA algorithm to detect labels automatically. Use SageMaker to deploy the model to an inference endpoint. Develop a service in the application to use the inference endpoint to process incoming claims, predict the labels, and route the claims to the appropriate queue.

Use Amazon Textract to process the database and automatically detect two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the extracted information to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.

Export the database to a .csv file with two columns: claim_label and claim_text. Use Amazon Comprehend custom classification and the .csv file to train the custom classifier. Develop a service in the application to use the Amazon Comprehend API to process incoming claims, predict the labels, and route the claims to the appropriate queue.

Data Engineering

What is the recommended architecture for scaling a complex deep neural network solution at the lowest cost?

Single Choice

A technology startup is using complex deep neural networks and GPU compute to recommend the company s products to its existing customers based upon each customer s habits and interactions. The solution currently pulls each dataset from an Amazon S3 bucket before loading the data into a TensorFlow model pulled from the company s Git repository that runs locally. This job then runs for several hours while continually outputting its progress to the same S3 bucket. The job can be paused, restarted, and continued at any time in the event of a failure, and is run from a central queue.

Senior managers are concerned about the complexity of the solution s resource management and the costs involved in repeating the process regularly. They ask for the workload to be automated so it runs once a week, starting Monday and completing by the close of business Friday.

Which architecture should be used to scale the solution at the lowest cost?

Implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance

Implement the solution using a low-cost GPU-compatible Amazon EC2 instance and use the AWS Instance Scheduler to schedule the task

Implement the solution using AWS Deep Learning Containers, run the workload using AWS Fargate running on Spot Instances, and then schedule the task using the built-in task scheduler

Implement the solution using Amazon ECS running on Spot Instances and schedule the task using the ECS service scheduler

Exploratory Data Analysis Machine Learning Implementation and Operations

How can a telecommunications company implement a testing model with minimal operational overhead?

Single Choice

A telecommunications company is developing a mobile app for its customers. The company is using an Amazon SageMaker hosted endpoint for machine learning model inferences.

Developers want to introduce a new version of the model for a limited number of users who subscribed to a preview feature of the app. After the new version of the model is tested as a preview, developers will evaluate its accuracy. If a new version of the model has better accuracy, developers need to be able to gradually release the new version for all users over a fixed period of time.

How can the company implement the testing model with the LEAST amount of operational overhead?

Update the ProductionVariant data type with the new version of the model by using the CreateEndpointConfig operation with the InitialVariantWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. When the new version of the model is ready for release, gradually increase InitialVariantWeight until all users have the updated version.

Configure two SageMaker hosted endpoints that serve the different versions of the model. Create an Application Load Balancer (ALB) to route traffic to both endpoints based on the TargetVariant query string parameter. Reconfigure the app to send the TargetVariant query string parameter for users who subscribed to the preview feature. When the new version of the model is ready for release, change the ALB's routing algorithm to weighted until all users have the updated version.

Update the DesiredWeightsAndCapacity data type with the new version of the model by using the UpdateEndpointWeightsAndCapacities operation with the DesiredWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. When the new version of the model is ready for release, gradually increase DesiredWeight until all users have the updated version.

Configure two SageMaker hosted endpoints that serve the different versions of the model. Create an Amazon Route 53 record that is configured with a simple routing policy and that points to the
current version of the model. Configure the mobile app to use the endpoint URL for users who subscribed to the preview feature and to use the Route 53 record for other users. When the new version of the model is ready for release, add a new model version endpoint to Route 53, and switch the policy to weighted until all users have the updated version.

Machine Learning Implementation and Operations AWS Machine Learning Services

Which storage option allows IAM access control and provides processing flexibility for a trucking company's live image data?

Single Choice

A trucking company is collecting live image data from its fleet of trucks across the globe. The data is growing rapidly and approximately 100 GB of new data is generated every day. The company wants to explore machine learning uses cases while ensuring the data is only accessible to specific IAM users.

Which storage option provides the most processing flexibility and will allow access control with IAM?

Use a database, such as Amazon DynamoDB, to store the images, and set the IAM policies to restrict access to only the desired IAM users.

Use an Amazon S3-backed data lake to store the raw images, and set up the permissions using bucket policies.

Setup up Amazon EMR with Hadoop Distributed File System (HDFS) to store the files, and restrict access to the EMR instances using IAM policies.

Configure Amazon EFS with IAM policies to make the data available to Amazon EC2 instances owned by the IAM users.

Machine Learning Implementation and Operations AWS Machine Learning Services

How can a web-based company improve the accuracy of its deep learning model to maximize conversions?

Single Choice

A web-based company wants to improve its conversion rate on its landing page. Using a large historical dataset of customer visits, the company has repeatedly trained a multi-class deep learning network algorithm on Amazon SageMaker. However, there is an overfitting problem: training data shows 90% accuracy in predictions, while test data shows 70% accuracy only.

The company needs to boost the generalization of its model before deploying it into production to maximize conversions of visits to purchases.

Which action is recommended to provide the HIGHEST accuracy model for the company s test and validation data?

Increase the randomization of training data in the mini-batches used in training

Allocate a higher proportion of the overall data to the training dataset

Apply L1 or L2 regularization and dropouts to the training

Reduce the number of layers and units (or neurons) from the deep learning network

Model Development Machine Learning Implementation and Operations

Previous Questions

Next Questions

Amazon (MLS-C01) Exam Questions And Answers page 19

Which model is suitable for categorizing new products using a labeled dataset with 15 features and 1,200 products?

Comments

What Additional Data Sources Can Enhance ML Review Analysis?

Comments

How can a data scientist adjust the Amazon Personalize solution to maintain increased sales during a marketing campaign?

Comments

How can the ML team visualize the degradation of the sales forecasting model accurately in SageMaker Studio?

Correct Answer:

References:

Comments

What solution can be used to combine and remove duplicates from two datasets with different structures?

Correct Answer:

References:

Comments

Which solution for automatic routing of customer claims without requiring ML expertise meets the company's requirements?

Correct Answer:

References:

Comments

What is the recommended architecture for scaling a complex deep neural network solution at the lowest cost?

Comments

How can a telecommunications company implement a testing model with minimal operational overhead?

Comments

Which storage option allows IAM access control and provides processing flexibility for a trucking company's live image data?

Comments

How can a web-based company improve the accuracy of its deep learning model to maximize conversions?

Comments

{{ exam.shortName }}

{{ exam.longName }}