Amazon MLS-C01 AWS Certified Machine Learning – Specialty Online Training
Amazon MLS-C01 Online Training
The questions for MLS-C01 were last updated at Apr 21,2024.
- Exam Code: MLS-C01
- Exam Name: AWS Certified Machine Learning - Specialty
- Certification Provider: Amazon
- Latest update: Apr 21,2024
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.
A Machine Learning Specialist has completed a proof of concept for a company using a small data sample and now the Specialist is ready to implement an end-to-end solution in AWS using Amazon SageMaker The historical training data is stored in Amazon RDS
Which approach should the Specialist use for training a model using that data?
- A . Write a direct connection to the SQL database within the notebook and pull data in
- B . Push the data from Microsoft SQL Server to Amazon S3 using an AWS Data Pipeline and provide the S3 location within the notebook.
- C . Move the data to Amazon DynamoDB and set up a connection to DynamoDB within the notebook to pull data in
- D . Move the data to Amazon ElastiCache using AWS DMS and set up a connection within the
notebook to pull data in for fast access.
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?
- A . Recall
- B . Misclassification rate
- C . Mean absolute percentage error (MAPE)
- D . Area Under the ROC Curve (AUC)
A Machine Learning Specialist is using Amazon Sage Maker to host a model for a highly available customer-facing application.
The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production. To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed
What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?
- A . Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by updating the client configuration. Revert traffic to the last version if the model does not perform as expected.
- B . Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.
- C . Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model does not perform as expected.
- D . Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.
A manufacturing company has a large set of labeled historical sales data The manufacturer would like to predict how many units of a particular part should be produced each quarter.
Which machine learning approach should be used to solve this problem?
- A . Logistic regression
- B . Random Cut Forest (RCF)
- C . Principal component analysis (PCA)
- D . Linear regression
A manufacturing company has structured and unstructured data stored in an Amazon S3 bucket A Machine Learning Specialist wants to use SQL to run queries on this data.
Which solution requires the LEAST effort to be able to query this data?
- A . Use AWS Data Pipeline to transform the data and Amazon RDS to run queries.
- B . Use AWS Glue to catalogue the data and Amazon Athena to run queries
- C . Use AWS Batch to run ETL on the data and Amazon Aurora to run the quenes
- D . Use AWS Lambda to transform the data and Amazon Kinesis Data Analytics to run queries
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs
What does the Specialist need to do1?
- A . Bundle the NVIDIA drivers with the Docker image
- B . Build the Docker container to be NVIDIA-Docker compatible
- C . Organize the Docker container’s file structure to execute on GPU instances.
- D . Set the GPU flag in the Amazon SageMaker Create TrainingJob request body
A large JSON dataset for a project has been uploaded to a private Amazon S3 bucket The Machine Learning Specialist wants to securely access and explore the data from an Amazon SageMaker notebook instance A new VPC was created and assigned to the Specialist
How can the privacy and integrity of the data stored in Amazon S3 be maintained while granting access to the Specialist for analysis?
- A . Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled Use an S3 ACL to open read privileges to the everyone group
- B . Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Copy the JSON dataset from Amazon S3 into the ML storage volume on the SageMaker notebook instance and work against the local dataset
- C . Launch the SageMaker notebook instance within the VPC and create an S3 VPC endpoint for the notebook to access the data Define a custom S3 bucket policy to only allow requests from your VPC to access the S3 bucket
- D . Launch the SageMaker notebook instance within the VPC with SageMaker-provided internet access enabled. Generate an S3 pre-signed URL for access to data in the bucket
Given the following confusion matrix for a movie classification model, what is the true class frequency for Romance and the predicted class frequency for Adventure?
- A . The true class frequency for Romance is 77.56% and the predicted class frequency for Adventure is 20 85%
- B . The true class frequency for Romance is 57.92% and the predicted class frequency for Adventure is 1312%
- C . The true class frequency for Romance is 0 78 and the predicted class frequency for Adventure is (0 47 – 0.32).
- D . The true class frequency for Romance is 77.56% * 0.78 and the predicted class frequency for Adventure is 20 85% ‘ 0.32
A Machine Learning Specialist is building a supervised model that will evaluate customers’ satisfaction with their mobile phone service based on recent usage The model’s output should infer whether or not a customer is likely to switch to a competitor in the next 30 days.
Which of the following modeling techniques should the Specialist use1?
- A . Time-series prediction
- B . Anomaly detection
- C . Binary classification
- D . Regression
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