PASS GUARANTEED QUIZ 2025 MLS-C01: AUTHORITATIVE AWS CERTIFIED MACHINE LEARNING - SPECIALTY VALID EXAM CAMP PDF

Pass Guaranteed Quiz 2025 MLS-C01: Authoritative AWS Certified Machine Learning - Specialty Valid Exam Camp Pdf

Pass Guaranteed Quiz 2025 MLS-C01: Authoritative AWS Certified Machine Learning - Specialty Valid Exam Camp Pdf

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Understanding functional and technical aspects of AWS Certified Machine Learning - Specialty Modeling

The following will be discussed in AMAZON MLS-C01 exam dumps:

  • Select the appropriate model(s) for a given machine learning problem
  • Frame business problems as machine learning problems
  • Train machine learning models
  • Perform hyperparameter optimization
  • Evaluate machine learning models

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is designed for individuals who want to validate their expertise in machine learning on the Amazon Web Services (AWS) platform. AWS Certified Machine Learning - Specialty certification exam is intended for individuals who have experience in designing, developing, and deploying machine learning models on AWS. By earning this certification, individuals can demonstrate their knowledge and skills in various aspects of machine learning, such as data preparation, feature engineering, model training, and deployment.

The AWS-Certified-Machine-Learning-Specialty Exam is a challenging certification program that requires a comprehensive understanding of machine learning concepts such as data preparation, model training, and model evaluation. MLS-C01 Exam covers a wide range of topics, including machine learning algorithms, AWS services such as Amazon SageMaker, and data analysis techniques. Candidates must also demonstrate their ability to design, deploy, and maintain machine learning solutions using AWS services.

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MLS-C01 Exam Collection Pdf - Latest MLS-C01 Braindumps Files

This offline version of the practice test creates a real AWS Certified Machine Learning - Specialty exam environment. You can practice the Amazon MLS-C01 Questions with the help of desktop practice exam software. The practice exam software is compatible with Windows-based computers only and does not need internet connectivity.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q70-Q75):

NEW QUESTION # 70
A company deployed a machine learning (ML) model on the company website to predict real estate prices.
Several months after deployment, an ML engineer notices that the accuracy of the model has gradually decreased.
The ML engineer needs to improve the accuracy of the model. The engineer also needs to receive notifications for any future performance issues.
Which solution will meet these requirements?

  • A. Use only data from the previous several months to perform incremental training to update the model.Use Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.
  • B. Perform incremental training to update the model. Activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications.
  • C. Use Amazon SageMaker Model Governance. Configure Model Governance to automatically adjust model hyper para meters. Create a performance threshold alarm in Amazon CloudWatch to send notifications.
  • D. Use Amazon SageMaker Debugger with appropriate thresholds. Configure Debugger to send Amazon CloudWatch alarms to alert the team Retrain the model by using only data from the previous several months.

Answer: B

Explanation:
The best solution to improve the accuracy of the model and receive notifications for any future performance issues is to perform incremental training to update the model and activate Amazon SageMaker Model Monitor to detect model performance issues and to send notifications. Incremental training is a technique that allows you to update an existing model with new data without retraining the entire model from scratch. This can save time and resources, and help the model adapt to changing data patterns. Amazon SageMaker Model Monitor is a feature that continuously monitors the quality of machine learning models in production and notifies you when there are deviations in the model quality, such as data drift and anomalies. You can set up alerts that trigger actions, such as sending notifications to Amazon Simple Notification Service (Amazon SNS) topics, when certain conditions are met.
Option B is incorrect because Amazon SageMaker Model Governance is a set of tools that help you implement ML responsibly by simplifying access control and enhancing transparency. It does not provide a mechanism to automatically adjust model hyperparameters or improve model accuracy.
Option C is incorrect because Amazon SageMaker Debugger is a feature that helps you debug and optimize your model training process by capturing relevant data and providing real-time analysis. However, using Debugger alone does not update the model or monitor its performance in production. Also, retraining the model by using only data from the previous several months may not capture the full range of data variability and may introduce bias or overfitting.
Option D is incorrect because using only data from the previous several months to perform incremental training may not be sufficient to improve the model accuracy, as explained above. Moreover, this option does not specify how to activate Amazon SageMaker Model Monitor or configure the alerts and notifications.
References:
* Incremental training
* Amazon SageMaker Model Monitor
* Amazon SageMaker Model Governance
* Amazon SageMaker Debugger


NEW QUESTION # 71
A company uses a long short-term memory (LSTM) model to evaluate the risk factors of a particular energy sector. The model reviews multi-page text documents to analyze each sentence of the text and categorize it as either a potential risk or no risk. The model is not performing well, even though the Data Scientist has experimented with many different network structures and tuned the corresponding hyperparameters.
Which approach will provide the MAXIMUM performance boost?

  • A. Use gated recurrent units (GRUs) instead of LSTM and run the training process until the validation loss stops decreasing.
  • B. Reduce the learning rate and run the training process until the training loss stops decreasing.
  • C. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
  • D. Initialize the words by term frequency-inverse document frequency (TF-IDF) vectors pretrained on a large collection of news articles related to the energy sector.

Answer: B


NEW QUESTION # 72
A company is converting a large number of unstructured paper receipts into images. The company wants to create a model based on natural language processing (NLP) to find relevant entities such as date, location, and notes, as well as some custom entities such as receipt numbers.
The company is using optical character recognition (OCR) to extract text for data labeling. However, documents are in different structures and formats, and the company is facing challenges with setting up the manual workflows for each document type. Additionally, the company trained a named entity recognition (NER) model for custom entity detection using a small sample size. This model has a very low confidence score and will require retraining with a large dataset.
Which solution for text extraction and entity detection will require the LEAST amount of effort?

  • A. Extract text from receipt images by using Amazon Textract. Use the Amazon SageMaker BlazingText algorithm to train on the text for entities and custom entities.
  • B. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.
  • C. Extract text from receipt images by using a deep learning OCR model from the AWS Marketplace. Use the NER deep learning model to extract entities.
  • D. Extract text from receipt images by using Amazon Textract. Use Amazon Comprehend for entity detection, and use Amazon Comprehend custom entity recognition for custom entity detection.

Answer: D


NEW QUESTION # 73
A Machine Learning Specialist built an image classification deep learning model. However the Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and 75%r respectively.
How should the Specialist address this issue and what is the reason behind it?

  • A. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
  • B. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
  • C. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
  • D. The learning rate should be increased because the optimization process was trapped at a local minimum.

Answer: B

Explanation:
The best way to address the overfitting problem in image classification is to increase the dropout rate at the flatten layer because the model is not generalized enough. Dropout is a regularization technique that randomly drops out some units from the neural network during training, reducing the co-adaptation of features and preventing overfitting. The flatten layer is the layer that converts the output of the convolutional layers into a one-dimensional vector that can be fed into the dense layers. Increasing the dropout rate at the flatten layer means that more features from the convolutional layers will be ignored, forcing the model to learn more robust and generalizable representations from the remaining features.
The other options are not correct for this scenario because:
* Increasing the learning rate would not help with the overfitting problem, as it would make the optimization process more unstable and prone to overshooting the global minimum. A high learning rate can also cause the model to diverge or oscillate around the optimal solution, resulting in poor performance and accuracy.
* Increasing the dimensionality of the dense layer next to the flatten layer would not help with the overfitting problem, as it would make the model more complex and increase the number of parameters to be learned. A more complex model can fit the training data better, but it can also memorize the noise and irrelevant details in the data, leading to overfitting and poor generalization.
* Increasing the epoch number would not help with the overfitting problem, as it would make the model train longer and more likely to overfit the training data. A high epoch number can cause the model to converge to the global minimum, but it can also cause the model to over-optimize the training data and lose the ability to generalize to new data.
Dropout: A Simple Way to Prevent Neural Networks from Overfitting
How to Reduce Overfitting With Dropout Regularization in Keras
How to Control the Stability of Training Neural Networks With the Learning Rate How to Choose the Number of Hidden Layers and Nodes in a Feedforward Neural Network?
How to decide the optimal number of epochs to train a neural network?


NEW QUESTION # 74
A medical imaging company wants to train a computer vision model to detect areas of concern on patients' CT scans. The company has a large collection of unlabeled CT scans that are linked to each patient and stored in an Amazon S3 bucket. The scans must be accessible to authorized users only. A machine learning engineer needs to build a labeling pipeline.
Which set of steps should the engineer take to build the labeling pipeline with the LEAST effort?

  • A. Create a workforce with Amazon Cognito. Build a labeling web application with AWS Amplify. Build a labeling workflow backend using AWS Lambda. Write the labeling instructions.
  • B. Create a workforce with AWS Identity and Access Management (IAM). Build a labeling tool on Amazon EC2 Queue images for labeling by using Amazon Simple Queue Service (Amazon SQS). Write the labeling instructions.
  • C. Create an Amazon Mechanical Turk workforce and manifest file. Create a labeling job by using the built-in image classification task type in Amazon SageMaker Ground Truth. Write the labeling instructions.
  • D. Create a private workforce and manifest file. Create a labeling job by using the built-in bounding box task type in Amazon SageMaker Ground Truth. Write the labeling instructions.

Answer: D

Explanation:
Explanation
The engineer should create a private workforce and manifest file, and then create a labeling job by using the built-in bounding box task type in Amazon SageMaker Ground Truth. This will allow the engineer to build the labeling pipeline with the least effort.
A private workforce is a group of workers that you manage and who have access to your labeling tasks. You can use a private workforce to label sensitive data that requires confidentiality, such as medical images. You can create a private workforce by using Amazon Cognito and inviting workers by email. You can also use AWS Single Sign-On or your own authentication system to manage your private workforce.
A manifest file is a JSON file that lists the Amazon S3 locations of your input data. You can use a manifest file to specify the data objects that you want to label in your labeling job. You can create a manifest file by using the AWS CLI, the AWS SDK, or the Amazon SageMaker console.
A labeling job is a process that sends your input data to workers for labeling. You can use the Amazon SageMaker console to create a labeling job and choose from several built-in task types, such as image classification, text classification, semantic segmentation, and bounding box. A bounding box task type allows workers to draw boxes around objects in an image and assign labels to them. This is suitable for object detection tasks, such as identifying areas of concern on CT scans.
References:
Create and Manage Workforces - Amazon SageMaker
Use Input and Output Data - Amazon SageMaker
Create a Labeling Job - Amazon SageMaker
Bounding Box Task Type - Amazon SageMaker


NEW QUESTION # 75
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