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MLS-C01 PDF

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  • Questions: 322 Q&A's With Detailed Explanation
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  • Exam Name: AWS Certified Machine Learning - Specialty
  • Last Update: 19-May-2025
  • Questions and Answers: 322
  • Single Choice: 268 Q&A's
  • Multiple Choice: 54 Q&A's

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Amazon Web Services MLS-C01 Exam Dumps FAQs

The AWS Certified Machine Learning Specialty (MLS-C01) exam validates your ability to design, develop, deploy, and optimize machine learning (ML) solutions using Amazon Web Services (AWS) cloud platform. It assesses your knowledge of:

  • Selecting appropriate ML services for specific business problems.
  • Preparing and managing data for ML workloads on AWS.
  • Training, tuning, and deploying ML models using AWS services like SageMaker.
  • Monitoring and evaluating the performance of ML models in production.
  • Securing and managing ML pipelines within the AWS environment.

AWS recommends at least two years of hands-on experience developing, architecting, and running ML or deep learning workloads on AWS. Understanding of ML fundamentals and experience with popular ML frameworks is also beneficial.

The exam is typically a multiple-choice question format with some questions potentially requiring short answer responses.

The exam duration is typically 180 minutes (3 hours).

AWS doesn't publicly disclose the passing score. It's best to aim for high scores on practice tests to gauge your readiness.

Yes, you can retake the exam after a waiting period (typically 14 days) following an unsuccessful attempt. Retake fees apply.

  • Machine Learning Engineer (AWS focus)
  • Data Scientist (with expertise in AWS ML services)
  • Machine Learning Architect
  • AWS Solutions Architect (with ML specialization)
  • AI/ML Consultant

The MLS-C01 focuses specifically on applying machine learning within the AWS cloud environment. Other AWS certifications, like AWS Certified Solutions Architect - Associate, cover broader cloud architectural principles that might touch on ML but not in the same depth.

  • Follow AWS on social media or subscribe to their certification newsletters for updates.
  • Regularly check the AWS Training and Certification crack4sure for any changes to the exam or its resources.
  • Join online communities or forums dedicated to AWS machine learning.

Several third-party vendors offer practice tests for the MLS-C01 exam. Look for reputable vendors with practice tests aligned with the current exam format.

Salary can vary depending on experience, location, and industry. Salary comparison crack4sures can provide estimates considering these factors. The MLS-C01 certification can potentially contribute to higher earning potential in relevant ML roles.

The preparation time can vary depending on your prior ML and AWS experience. AWS recommends 40-60 hours of study, but it can be more or less depending on your individual pace.

  • Gain hands-on experience with AWS ML services through the AWS free tier or personal projects.
  • Utilize a combination of study resources like AWS documentation, practice tests, and video tutorials.
  • Participate in online communities or forums to discuss

MLS-C01 Questions and Answers

Question # 1

An insurance company is creating an application to automate car insurance claims. A machine learning (ML) specialist used an Amazon SageMaker Object Detection - TensorFlow built-in algorithm to train a model to detect scratches and dents in images of cars. After the model was trained, the ML specialist noticed that the model performed better on the training dataset than on the testing dataset.

Which approach should the ML specialist use to improve the performance of the model on the testing data?

A.

Increase the value of the momentum hyperparameter.

B.

Reduce the value of the dropout_rate hyperparameter.

C.

Reduce the value of the learning_rate hyperparameter.

D.

Increase the value of the L2 hyperparameter.

Question # 2

A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem.

The regular operation label is marked as 0. The abnormal operation label is marked as 1 . Process abnormalities have a significant negative effect on the companys profits. The company must avoid these abnormalities.

Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?

A.

Precision = 0.91 Recall = 0.6

B.

Precision = 0.61 Recall = 0.98

C.

Precision = 0.7 Recall = 0.9

D.

Precision = 0.98 Recall = 0.8

Question # 3

A Machine Learning Specialist is assigned a TensorFlow project using Amazon SageMaker for training, and needs to continue working for an extended period with no Wi-Fi access.

Which approach should the Specialist use to continue working?

A.

Install Python 3 and boto3 on their laptop and continue the code development using that environment.

B.

Download the TensorFlow Docker container used in Amazon SageMaker from GitHub to their local environment, and use the Amazon SageMaker Python SDK to test the code.

C.

Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.

D.

Download the SageMaker notebook to their local environment then install Jupyter Notebooks on their laptop and continue the development in a local notebook.

Question # 4

An automotive company uses computer vision in its autonomous cars. The company trained its object detection models successfully by using transfer learning from a convolutional neural network (CNN). The company trained the models by using PyTorch through the Amazon SageMaker SDK.

The vehicles have limited hardware and compute power. The company wants to optimize the model to reduce memory, battery, and hardware consumption without a significant sacrifice in accuracy.

Which solution will improve the computational efficiency of the models?

A.

Use Amazon CloudWatch metrics to gain visibility into the SageMaker training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set new weights based on the pruned set of filters. Run a new training job with the pruned model.

B.

Use Amazon SageMaker Ground Truth to build and run data labeling workflows. Collect a larger labeled dataset with the labelling workflows. Run a new training job that uses the new labeled data with previous training data.

C.

Use Amazon SageMaker Debugger to gain visibility into the training weights, gradients, biases, and activation outputs. Compute the filter ranks based on the training information. Apply pruning to remove the low-ranking filters. Set the new weights based on the pruned set of filters. Run a new training job with the pruned model.

D.

Use Amazon SageMaker Model Monitor to gain visibility into the ModelLatency metric and OverheadLatency metric of the model after the company deploys the model. Increase the model learning rate. Run a new training job.

Question # 5

A machine learning (ML) specialist wants to create a data preparation job that uses a PySpark script with complex window aggregation operations to create data for training and testing. The ML specialist needs to evaluate the impact of the number of features and the sample count on model performance.

Which approach should the ML specialist use to determine the ideal data transformations for the model?

A.

Add an Amazon SageMaker Debugger hook to the script to capture key metrics. Run the script as an AWS Glue job.

B.

Add an Amazon SageMaker Experiments tracker to the script to capture key metrics. Run the script as an AWS Glue job.

C.

Add an Amazon SageMaker Debugger hook to the script to capture key parameters. Run the script as a SageMaker processing job.

D.

Add an Amazon SageMaker Experiments tracker to the script to capture key parameters. Run the script as a SageMaker processing job.

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