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Questions and answers from the AWS Certified Machine Learning Specialty include important topics from the AWS Certified Machine Learning Specialty Certification Program and provides easy-to-learn information for easy access.
The AWS Certified Machine Learning – Specialty (MLS-C01) examination is intended for individuals who perform a development or data science role. This exam validates an examinee’s ability to build, train, tune, and deploy machine learning (ML) models using the AWS Cloud.
Candidate must have 1-2 years of hands-on experience developing, architecting, or running ML/deeplearning workloads on the AWS Cloud, along with:
- The ability to follow model-training best practices
- Experience with ML and deep learning frameworks
- The ability to express the intuition behind basic ML algorithms
- The ability to follow deployment and operational best practices
- Experience performing basic hyperparameter optimization
Candidates for the AWS Certified Machine Learning Specialty should have a thorough knowledge and understanding of all the questions and answers of the AWS Certified Machine Learning Specialty in our practice exam and exam dumps.
Amazon AWS Certified Machine Learning – Specialty Sample Questions (Q191-Q196):
NEW QUESTION # 191
An online reseller has a large, multi-column dataset with one column missing 30% of its data. A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?
- A. Multiple imputation
- B. Listwise deletion
- C. Last observation carried forward
- D. Mean substitution
NEW QUESTION # 192
A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.
Which solution requires the LEAST coding effort?
- A. Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team
- B. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3
- C. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team
- D. Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.
NEW QUESTION # 193
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. Download the SageMaker notebook to their local environment then install Jupyter Notebooks on their laptop and continue the development in a local notebook.
- B. Download TensorFlow from tensorflow.org to emulate the TensorFlow kernel in the SageMaker environment.
- C. 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.
- D. Install Python 3 and boto3 on their laptop and continue the code development using that environment.
NEW QUESTION # 194
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.
What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model’s performance?
- A. Misclassification rate
- B. Receiver operating characteristic (ROC) curve
- C. Root Mean Square Error (RM&)
- D. L1 norm
NEW QUESTION # 195
A Data Engineer needs to build a model using a dataset containing customer credit card information.
How can the Data Engineer ensure the data remains encrypted and the credit card information is secure?
- A. Use an 1AM policy to encrypt the data on the Amazon S3 bucket and Amazon Kinesis to automatically discard credit card numbers and insert fake credit card numbers.
- B. Use AWS KMS to encrypt the data on Amazon S3 and Amazon SageMaker, and redact the credit card numbers from the customer data with AWS Glue.
- C. Use a custom encryption algorithm to encrypt the data and store the data on an Amazon SageMaker instance in a VPC. Use the SageMaker DeepAR algorithm to randomize the credit card numbers.
- D. Use an Amazon SageMaker launch configuration to encrypt the data once it is copied to the SageMaker instance in a VPC Use the SageMaker principal component analysis (PCA) algorithm to reduce the length of the credit card numbers.
NEW QUESTION # 196
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