Printable PDFAmazon MLS-C01 Exam Questions & Answers
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Vendor: Amazon
Exam Code: MLS-C01
Exam Name: AWS Certified Machine Learning - Specialty (MLS-C01)
Certification Provider: Amazon
Total Questions: 396 Q&A ( View Details)
Updated on: May 23, 2026
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MLS-C01 Online Practice Questions and Answers
A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?
A. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
B. AWS Glue with a custom ETL script to transform the data.
C. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
D. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.
A Machine Learning Specialist observes several performance problems with the training portion of a machine learning solution on Amazon SageMaker The solution uses a large training dataset 2 TB in size and is using the SageMaker k-means algorithm The observed issues include the unacceptable length of time it takes before the training job launches and poor I/O throughput while training the model.
What should the Specialist do to address the performance issues with the current solution?
A. Use the SageMaker batch transform feature
B. Compress the training data into Apache Parquet format.
C. Ensure that the input mode for the training job is set to Pipe.
D. Copy the training dataset to an Amazon EFS volume mounted on the SageMaker instance.
A machine learning (ML) specialist is developing a deep learning sentiment analysis model that is based on data from movie reviews. After the ML specialist trains the model and reviews the model results on the validation set, the ML specialist discovers that the model is overfitting.
Which solutions will MOST improve the model generalization and reduce overfitting? (Choose three.)
A. Shuffle the dataset with a different seed.
B. Decrease the learning rate.
C. Increase the number of layers in the network.
D. Add L1 regularization and L2 regularization.
E. Add dropout.
F. Decrease the number of layers in the network.
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