Amazon MLS-C01 Online Practice Questions and Exam Preparation

Amazon MLS-C01 Online Questions & Answers

• Question 151:

  An online store is predicting future book sales by using a linear regression model that is based on past sales data. The data includes duration, a numerical feature that represents the number of days that a book has been listed in the online store. A data scientist performs an exploratory data analysis and discovers that the relationship between book sales and duration is skewed and non-linear.

  Which data transformation step should the data scientist take to improve the predictions of the model?

  A. One-hot encoding
  B. Cartesian product transformation
  C. Quantile binning
  D. Normalization
  C. Quantile binning

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  Explanation

  https://docs.aws.amazon.com/machine-learning/latest/dg/data-transformations-reference.html

• Question 152:

  A data engineer at a bank is evaluating a new tabular dataset that includes customer data. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.

  Which steps should the data engineer take to address this issue? (Choose two.)

  A. Use a linear-based algorithm to train the model.
  B. Apply principal component analysis (PCA).
  C. Remove a portion of highly correlated features from the dataset.
  D. Apply min-max feature scaling to the dataset.
  E. Apply one-hot encoding category-based variables.
  B. Apply principal component analysis (PCA).
  C. Remove a portion of highly correlated features from the dataset.

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  Explanation

  https://royalsocietypublishing.org/doi/10.1098/rsta.2015.0202 https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html

• Question 153:

  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 the 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?

  A. Implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance
  B. Implement the solution using a low-cost GPU compatible Amazon EC2 instance and use the AWS Instance Scheduler to schedule the task
  C. 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
  D. Implement the solution using Amazon ECS running on Spot Instances and schedule the task using the ECS service scheduler.
  A. Implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance

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  Explanation

  https://aws.amazon.com/blogs/compute/gpu-workloads-on-aws-batch/

• Question 154:

  A machine learning (ML) engineer has created a feature repository in Amazon SageMaker Feature Store for the company. The company has AWS accounts for development, integration, and production. The company hosts a feature store in the development account. The company uses Amazon S3 buckets to store feature values offline. The company wants to share features and to allow the integration account and the production account to reuse the features that are in the feature repository.

  Which combination of steps will meet these requirements? (Select TWO.)

  A. Create an IAM role in the development account that the integration account and production account can assume. Attach IAM policies to the role that allow access to the feature repository and the S3 buckets.
  B. Share the feature repository that is associated the S3 buckets from the development account to the integration account and the production account by using AWS Resource Access Manager (AWS RAM).
  C. Use AWS Security Token Service (AWS STS) from the integration account and the production account to retrieve credentials for the development account.
  D. Set up S3 replication between the development S3 buckets and the integration and production S3 buckets.
  E. Create an AWS PrivateLink endpoint in the development account for SageMaker.
  A. Create an IAM role in the development account that the integration account and production account can assume. Attach IAM policies to the role that allow access to the feature repository and the S3 buckets.
  B. Share the feature repository that is associated the S3 buckets from the development account to the integration account and the production account by using AWS Resource Access Manager (AWS RAM).

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  Explanation

  The combination of steps that will meet the requirements are to create an IAM role in the development account that the integration account and production account can assume, attach IAM policies to the role that allow access to the feature repository and the S3 buckets, and share the feature repository that is associated with the S3 buckets from the development account to the integration account and the production account by using AWS Resource Access Manager (AWS RAM). This approach will enable cross-account access and sharing of the features stored in Amazon SageMaker Feature Store and Amazon S3. Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share curated data used in training and prediction workflows. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. A feature group schema consists of a list of feature definitions, each of which specifies the name, type, and metadata of a feature. Amazon SageMaker Feature Store stores the features in both an online store and an offline store. The online store is a low-latency, high-throughput store that is optimized for real-time inference. The offline store is a historical store that is backed by an Amazon S3 bucket and is optimized for batch processing and model training1. AWS Identity and Access Management (IAM) is a web service that helps you securely control access to AWS resources for your users. You use IAM to control who can use your AWS resources (authentication) and what resources they can use and in what ways (authorization). An IAM role is an IAM identity that you can create in your account that has specific permissions. You can use an IAM role to delegate access to users, applications, or services that don't normally have access to your AWS resources. For example, you can create an IAM role in your development account that allows the integration account and the production account to assume the role and access the resources in the development account. You can attach IAM policies to the role that specify the permissions for the feature repository and the S3 buckets. You can also use IAM conditions to restrict the access based on the source account, IP address, or other factors2. AWS Resource Access Manager (AWS RAM) is a service that enables you to easily and securely share AWS resources with any AWS account or within your AWS Organization. You can share AWS resources that you own with other accounts using resource shares. A resource share is an entity that defines the resources that you want to share, and the principals that you want to share with. For example, you can share the feature repository that is associated with the S3 buckets from the development account to the integration account and the production account by creating a resource share in AWS RAM. You can specify the feature group ARN and the S3 bucket ARN as the resources, and the integration account ID and the production account ID as the principals. You can also use IAM policies to further control the access to the shared resources3. The other options are either incorrect or unnecessary. Using AWS Security Token Service (AWS STS) from the integration account and the production account to retrieve

  credentials for the development account is not required, as the IAM role in the development account can provide temporary security credentials for the cross-account access. Setting up S3 replication between the development S3 buckets and

  the integration and production S3 buckets would introduce redundancy and inconsistency, as the S3 buckets are already shared through AWS RAM. Creating an AWS PrivateLink endpoint in the development account for SageMaker is not

  relevant, as it is used to securely connect to SageMaker services from a VPC, not from another account.

  References:

  1: Amazon SageMaker Feature Store ?Amazon Web Services

  2: What Is IAM? - AWS Identity and Access Management

  3: What Is AWS Resource Access Manager? - AWS Resource Access Manager

• Question 155:

  A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test data. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.

  

  Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

  A. Increase the max_depth parameter value.
  B. Lower the max_depth parameter value.
  C. Update the objective to binary:logistic.
  D. Lower the min_child_weight parameter value.
  B. Lower the max_depth parameter value.

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  Explanation

• Question 156:

  A company is setting up a mechanism for data scientists and engineers from different departments to access an Amazon SageMaker Studio domain. Each department has a unique SageMaker Studio domain.

  The company wants to build a central proxy application that data scientists and engineers can log in to by using their corporate credentials. The proxy application will authenticate users by using the company's existing Identity provider (IdP).

  The application will then route users to the appropriate SageMaker Studio domain.

  The company plans to maintain a table in Amazon DynamoDB that contains SageMaker domains for each department.

  How should the company meet these requirements?

  A. Use the SageMaker CreatePresignedDomainUrl API to generate a presigned URL for each domain according to the DynamoDB table. Pass the presigned URL to the proxy application.
  B. Use the SageMaker CreateHumanTaskUi API to generate a UI URL. Pass the URL to the proxy application.
  C. Use the Amazon SageMaker ListHumanTaskUis API to list all UI URLs. Pass the appropriate URL to the DynamoDB table so that the proxy application can use the URL.
  D. Use the SageMaker CreatePresignedNotebooklnstanceUrl API to generate a presigned URL. Pass the presigned URL to the proxy application.
  A. Use the SageMaker CreatePresignedDomainUrl API to generate a presigned URL for each domain according to the DynamoDB table. Pass the presigned URL to the proxy application.

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  Explanation

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  Domain specific presigned URL's can be generated through dynamodb to route users to correct domain

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  central proxy app can authenticate existing users with company IDP -> retrieve presigned url for users dept from dynamodb -> redirect user to sagemake domain without needing direct auth on AWS

• Question 157:

  A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.

  Which machine learning model type should the Specialist use to accomplish this task?

  A. Linear regression
  B. Classification
  C. Clustering
  D. Reinforcement learning
  B. Classification

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  Explanation

  The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/nonchurner) ?answers that need to be predicted ?to train an algorithm. With classification, businesses can answer the following questions:

  1.Will this customer churn or not?

  2.Will a customer renew their subscription?

  3.Will a user downgrade a pricing plan?

  4.Are there any signs of unusual customer behavior?

  https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html

• Question 158:

  A company needs to quickly make sense of a large amount of data and gain insight from it. The data is in different formats, the schemas change frequently, and new data sources are added regularly. The company wants to use AWS services to explore multiple data sources, suggest schemas, and enrich and transform the data. The solution should require the least possible coding effort for the data flows and the least possible infrastructure management.

  Which combination of AWS services will meet these requirements?

  A. 1. Amazon EMR for data discovery, enrichment, and transformation 2.Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL 3.Amazon QuickSight for reporting and getting insights
  B. 1. Amazon Kinesis Data Analytics for data ingestion 2.Amazon EMR for data discovery, enrichment, and transformation 3.Amazon Redshift for querying and analyzing the results in Amazon S3
  C. 1. AWS Glue for data discovery, enrichment, and transformation 2.Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL 3.Amazon QuickSight for reporting and getting insights
  D. 1. AWS Data Pipeline for data transfer 2.AWS Step Functions for orchestrating AWS Lambda jobs for data discovery, enrichment, and transformation 3.Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL 4.Amazon QuickSight for reporting and getting insights
  C. 1. AWS Glue for data discovery, enrichment, and transformation 2.Amazon Athena for querying and analyzing the results in Amazon S3 using standard SQL 3.Amazon QuickSight for reporting and getting insights

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  Explanation

• Question 159:

  A Machine Learning Specialist is working with multiple data sources containing billions of records that need to be joined. What feature engineering and model development approach should the Specialist take with a dataset this large?

  A. Use an Amazon SageMaker notebook for both feature engineering and model development
  B. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development
  C. Use Amazon EMR for feature engineering and Amazon SageMaker SDK for model development
  D. Use Amazon ML for both feature engineering and model development.
  B. Use an Amazon SageMaker notebook for feature engineering and Amazon ML for model development

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  Explanation

• Question 160:

  A company has hired a data scientist to create a loan risk model. The dataset contains loan amounts and variables such as loan type, region, and other demographic variables. The data scientist wants to use Amazon SageMaker to test bias regarding the loan amount distribution with respect to some of these categorical variables.

  Which pretraining bias metrics should the data scientist use to check the bias distribution? (Choose three.)

  A. Class imbalance
  B. Conditional demographic disparity
  C. Difference in proportions of labels
  D. Jensen-Shannon divergence
  E. Kullback-Leibler divergence
  F. Total variation distance
  D. Jensen-Shannon divergence
  E. Kullback-Leibler divergence
  F. Total variation distance

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  Explanation

  https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-measure-data-bias.html