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Master MLS-C01 Exams with Exam Questions & Study Materials, AWS Certified Machine Learning | SPOTO

The AWS Certified Machine Learning—Specialty (MLS-C01) exam is designed for professionals in Development or Data Science roles, focusing on machine learning (ML) model building, training, tuning, and deployment on the AWS Cloud. Mastering the MLS-C01 exam requires thorough preparation and access to reliable study materials. SPOTO offers comprehensive exam questions and study materials tailored to help you succeed in the AWS Certified Machine Learning—Specialty exam. Our practice tests cover a wide range of topics and scenarios, allowing you to familiarize yourself with the exam format and types of questions you may encounter. Additionally, our exam dumps provide valuable insights into key concepts and areas of focus for the exam. Accessing our sample questions for free gives you a glimpse into the level of detail and complexity expected in the MLS-C01 exam. Combine these resources with our exam answers and exam practice sessions to enhance your preparation and boost your confidence for exam day. Prepare effectively with SPOTO's exam simulator and online exam questions, ensuring you're well-equipped to tackle the MLS-C01 exam and showcase your machine learning expertise on the AWS Cloud.
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Question #1
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC. Why is the ML Specialist not seeing the instance visible in the VPC?
A. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs
B. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts
C. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts
D. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts
View answer
Correct Answer: D
Question #2
A Machine Learning Specialist has built a model using Amazon SageMaker built-in algorithms and is not getting expected accurate results The Specialist wants to use hyperparameter optimization to increase the model's accuracy Which method is the MOST repeatable and requires the LEAST amount of effort to achieve this?
A. Launch multiple training jobs in parallel with different hyperparameters
B. Create an AWS Step Functions workflow that monitors the accuracy in Amazon CloudWatch Logs and relaunches the training job with a defined list of hyperparameters
C. Create a hyperparameter tuning job and set the accuracy as an objective metric
D. Create a random walk in the parameter space to iterate through a range of values that should be used for each individual hyperparameter
View answer
Correct Answer: AD
Question #3
A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data Which type of data repository is the MOST cost-effective solution?
A. An Amazon EBS-backed Amazon EC2 instance with hourly directories
B. An Amazon RDS database with hourly table partitions
C. An Amazon S3 data lake with hourly object prefixes
D. An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes
View answer
Correct Answer: A
Question #4
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset Which tool should be used to improve the validation accuracy?
A. Amazon Comprehend syntax analysts and entity detection
B. Amazon SageMaker BlazingText allow mode
C. Natural Language Toolkit (NLTK) stemming and stop word removal
D. Scikit-learn term frequency-inverse document frequency (TF-IDF) vectorizers
View answer
Correct Answer: DEF
Question #5
A monitoring service generates 1 TB of scale metrics record data every minute A Research team performs queries on this data using Amazon Athena The queries run slowly due to the large volume of data, and the team requires better performance How should the records be stored in Amazon S3 to improve query performance?
A. CSV files
B. Parquet files
C. Compressed JSON
D. RecordIO
View answer
Correct Answer: B
Question #6
A Machine Learning Specialist is using Apache Spark for pre-processing training data As part of the Spark pipeline, the Specialist wants to use Amazon SageMaker for training a model and hosting it Which of the following would the Specialist do to integrate the Spark application with SageMaker? (Select THREE )
A. Download the AWS SDK for the Spark environment
B. Install the SageMaker Spark library in the Spark environment
C. Use the appropriate estimator from the SageMaker Spark Library to train a model
D. Compress the training data into a ZIP file and upload it to a pre-defined Amazon S3 bucket
E. Use the sageMakerMode
F. transform method to get inferences from the model hosted in SageMaker G
View answer
Correct Answer: ABD
Question #7
A Machine Learning Specialist is configuring automatic model tuning in Amazon SageMaker When using the hyperparameter optimization feature, which of the following guidelines should be followed to improve optimization? Choose the maximum number of hyperparameters supported by
A. Amazon SageMaker to search the largest number of combinations possible
B. Specify a very large hyperparameter range to allow Amazon SageMaker to cover every possible value
C. Use log-scaled hyperparameters to allow the hyperparameter space to be searched as quickly as possible
D. Execute only one hyperparameter tuning job at a time and improve tuning through successive rounds of experiments
View answer
Correct Answer: D

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