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MLS-C01 Exam Questions 2024 Updated: Get Ready for Exams, AWS Certified Machine Learning | SPOTO

The 2024 AWS MLS-C01 Exam Prep by SPOTO offers comprehensive practice tests and study materials for the AWS Certified Machine Learning—Specialty (MLS-C01) exam. Designed for professionals in Development or Data Science roles, this certification validates your expertise in building, training, fine-tuning, and deploying machine learning models on the AWS Cloud. By mastering exam questions and answers, you demonstrate your proficiency in leveraging AWS services for ML applications. Utilize practice tests and exam dumps to gauge your readiness and strengthen your knowledge of ML concepts. Access free exam materials and exam simulators to enhance your exam practice and preparation. With SPOTO's resources, including sample questions and mock exams, you can confidently approach the MLS-C01 exam, paving the way for career advancement and recognition in the field of machine learning.
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Question #1
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
View answer
Correct Answer: D
Question #2
A Machine Learning Specialist is training a model to identify the make and model of vehicles in images The Specialist wants to use transfer learning and an existing model trained on images of general objects The Specialist collated a large custom dataset of pictures containing different vehicle makes and models
A. Initialize the model with random weights in all layers including the last fully connected layer
B. Initialize the model with pre-trained weights in all layers and replace the last fully connected layer
C. Initialize the model with random weights in all layers and replace the last fully connected layer
D. Initialize the model with pre-trained weights in all layers including the last fully connected layer
View answer
Correct Answer: B
Question #3
Example Corp has an annual sale event from October to December. The company has sequential sales data from the past 15 years and wants to use Amazon ML to predict the sales for this year's upcoming event. Which method should Example Corp use to split the data into a training dataset and evaluation dataset?
A. Pre-split the data before uploading to Amazon S3
B. Have Amazon ML split the data randomly
C. Have Amazon ML split the data sequentially
D. Perform custom cross-validation on the data
View answer
Correct Answer: C
Question #4
A manufacturing company asks its Machine Learning Specialist to develop a model that classifies defective parts into one of eight defect types. The company has provided roughly 100000 images per defect type for training During the injial training of the image classification model the Specialist notices that the validation accuracy is 80%, while the training accuracy is 90% It is known that human-level performance for this type of image classification is around 90% What should the Specialist consider to fix
A. A longer training time
B. Making the network larger
C. Using a different optimizer
D. Using some form of regularization
View answer
Correct Answer: C
Question #5
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?
A. Perform one-hot encoding on highly correlated features
B. Use matrix multiplication on highly correlated features
C. Create a new feature space using principal component analysis (PCA)
D. Apply the Pearson correlation coefficient
View answer
Correct Answer: BD

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