MLS-C01 EXAMCOLLECTION VCE - LATEST STUDY MLS-C01 QUESTIONS

MLS-C01 Examcollection Vce - Latest Study MLS-C01 Questions

MLS-C01 Examcollection Vce - Latest Study MLS-C01 Questions

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The AWS Certified Machine Learning - Specialty Certification Exam covers a range of topics related to machine learning, including data preparation, feature engineering, modeling, tuning, and deployment. It also includes topics such as deep learning, reinforcement learning, and natural language processing. MLS-C01 Exam is designed to test the candidate's ability to apply machine learning concepts to real-world scenarios and assess their proficiency in implementing machine learning solutions on the AWS platform.

Amazon AWS Certified Machine Learning - Specialty certification exam is a professional-level certification that validates a candidate's skills and expertise in designing, implementing, and maintaining machine learning solutions on the AWS platform. AWS Certified Machine Learning - Specialty certification is intended for individuals who already have a solid understanding of machine learning concepts and are looking to deepen their knowledge and skills in AWS-specific machine learning tools and services.

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q126-Q131):

NEW QUESTION # 126
A Machine Learning Specialist is packaging a custom ResNet model into a Docker container so the company can leverage Amazon SageMaker for training The Specialist is using Amazon EC2 P3 instances to train the model and needs to properly configure the Docker container to leverage the NVIDIA GPUs What does the Specialist need to do1?

  • A. Build the Docker container to be NVIDIA-Docker compatible
  • B. Organize the Docker container's file structure to execute on GPU instances.
  • C. Bundle the NVIDIA drivers with the Docker image
  • D. Set the GPU flag in the Amazon SageMaker Create TrainingJob request body

Answer: A

Explanation:
Explanation
To leverage the NVIDIA GPUs on Amazon EC2 P3 instances, the Machine Learning Specialist needs to build the Docker container to be NVIDIA-Docker compatible. NVIDIA-Docker is a tool that enables GPU-accelerated containers to run on Docker. It automatically configures the container to access the NVIDIA drivers and libraries on the host system. The Specialist does not need to bundle the NVIDIA drivers with the Docker image, as they are already installed on the EC2 P3 instances. The Specialist does not need to organize the Docker container's file structure to execute on GPU instances, as this is not relevant for GPU compatibility. The Specialist does not need to set the GPU flag in the Amazon SageMaker Create TrainingJob request body, as this is only required for using Elastic Inference accelerators, not EC2 P3 instances.
References: NVIDIA-Docker, Using GPU-Accelerated Containers, Using Elastic Inference in Amazon SageMaker


NEW QUESTION # 127
A Machine Learning Specialist is working for a credit card processing company and receives an unbalanced dataset containing credit card transactions. It contains 99,000 valid transactions and 1,000 fraudulent transactions The Specialist is asked to score a model that was run against the dataset The Specialist has been advised that identifying valid transactions is equally as important as identifying fraudulent transactions What metric is BEST suited to score the model?

  • A. Recall
  • B. Precision
  • C. Area Under the ROC Curve (AUC)
  • D. Root Mean Square Error (RMSE)

Answer: C

Explanation:
Explanation
Area Under the ROC Curve (AUC) is a metric that is best suited to score the model for the given scenario.
AUC is a measure of the performance of a binary classifier, such as a model that predicts whether a credit card transaction is valid or fraudulent. AUC is calculated based on the Receiver Operating Characteristic (ROC) curve, which is a plot that shows the trade-off between the true positive rate (TPR) and the false positive rate (FPR) of the classifier as the decision threshold is varied. The TPR, also known as recall or sensitivity, is the proportion of actual positive cases (fraudulent transactions) that are correctly predicted as positive by the classifier. The FPR, also known as the fall-out, is the proportion of actual negative cases (valid transactions) that are incorrectly predicted as positive by the classifier. The ROC curve illustrates how well the classifier can distinguish between the two classes, regardless of the class distribution or the error costs. A perfect classifier would have a TPR of 1 and an FPR of 0 for all thresholds, resulting in a ROC curve that goes from the bottom left to the top left and then to the top right of the plot. A random classifier would have a TPR and an FPR that are equal for all thresholds, resulting in a ROC curve that goes from the bottom left to the top right of the plot along the diagonal line. AUC is the area under the ROC curve, and it ranges from 0 to 1. A higher AUC indicates a better classifier, as it means that the classifier has a higher TPR and a lower FPR for all thresholds. AUC is a useful metric for imbalanced classification problems, such as the credit card transaction dataset, because it is insensitive to the class imbalance and the error costs. AUC can capture the overall performance of the classifier across all possible scenarios, and it can be used to compare different classifiers based on their ROC curves.
The other options are not as suitable as AUC for the given scenario for the following reasons:
Precision: Precision is the proportion of predicted positive cases (fraudulent transactions) that are actually positive. Precision is a useful metric when the cost of a false positive is high, such as in spam detection or medical diagnosis. However, precision is not a good metric for imbalanced classification problems, because it can be misleadingly high when the positive class is rare. For example, a classifier that predicts all transactions as valid would have a precision of 0, but a very high accuracy of 99%.
Precision is also dependent on the decision threshold and the error costs, which may vary for different scenarios.
Recall: Recall is the same as the TPR, and it is the proportion of actual positive cases (fraudulent transactions) that are correctly predicted as positive by the classifier. Recall is a useful metric when the cost of a false negative is high, such as in fraud detection or cancer diagnosis. However, recall is not a good metric for imbalanced classification problems, because it can be misleadingly low when the positive class is rare. For example, a classifier that predicts all transactions as fraudulent would have a recall of 1, but a very low accuracy of 1%. Recall is also dependent on the decision threshold and the error costs, which may vary for different scenarios.
Root Mean Square Error (RMSE): RMSE is a metric that measures the average difference between the predicted and the actual values. RMSE is a useful metric for regression problems, where the goal is to predict a continuous value, such as the price of a house or the temperature of a city. However, RMSE is not a good metric for classification problems, where the goal is to predict a discrete value, such as the class label of a transaction. RMSE is not meaningful for classification problems, because it does not capture the accuracy or the error costs of the predictions.
References:
ROC Curve and AUC
How and When to Use ROC Curves and Precision-Recall Curves for Classification in Python Precision-Recall Root Mean Squared Error


NEW QUESTION # 128
A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among
200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.
What type of machine learning model should be used?

  • A. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
  • B. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
  • C. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
  • D. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.

Answer: B


NEW QUESTION # 129
An engraving company wants to automate its quality control process for plaques. The company performs the process before mailing each customized plaque to a customer. The company has created an Amazon S3 bucket that contains images of defects that should cause a plaque to be rejected. Low-confidence predictions must be sent to an internal team of reviewers who are using Amazon Augmented Al (Amazon A2I).
Which solution will meet these requirements?

  • A. Use AWS Panorama for automatic processing Use Amazon A2I with Amazon Mechanical Turk for manual review
  • B. Use Amazon Rekognition for automatic processing. Use Amazon A2I with a private workforce option for manual review.
  • C. Use Amazon Transcribe for automatic processing. Use Amazon A2I with a private workforce option for manual review.
  • D. Use Amazon Textract for automatic processing. Use Amazon A2I with Amazon Mechanical Turk for manual review.

Answer: B

Explanation:
Amazon Rekognition is a service that provides computer vision capabilities for image and video analysis, such as object, scene, and activity detection, face and text recognition, and custom label detection. Amazon Rekognition can be used to automate the quality control process for plaques by comparing the images of the plaques with the images of defects in the Amazon S3 bucket and returning a confidence score for each defect. Amazon A2I is a service that enables human review of machine learning predictions, such as low-confidence predictions from Amazon Rekognition. Amazon A2I can be integrated with a private workforce option, which allows the engraving company to use its own internal team of reviewers to manually inspect the plaques that are flagged by Amazon Rekognition. This solution meets the requirements of automating the quality control process, sending low-confidence predictions to an internal team of reviewers, and using Amazon A2I for manual review. References:
1: Amazon Rekognition documentation
2: Amazon A2I documentation
3: Amazon Rekognition Custom Labels documentation
4: Amazon A2I Private Workforce documentation


NEW QUESTION # 130
A large company has developed a BI application that generates reports and dashboards using data collected from various operational metrics. The company wants to provide executives with an enhanced experience so they can use natural language to get data from the reports. The company wants the executives to be able ask questions using written and spoken interfaces.
Which combination of services can be used to build this conversational interface? (Choose three.)

  • A. Amazon Lex
  • B. Amazon Comprehend
  • C. Amazon Polly
  • D. Amazon Connect
  • E. Alexa for Business
  • F. Amazon Transcribe

Answer: B,D,F


NEW QUESTION # 131
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