LATEST MLS-C01 TEST QUESTIONS | MLS-C01 VALID EXAM CRAM

Latest MLS-C01 Test Questions | MLS-C01 Valid Exam Cram

Latest MLS-C01 Test Questions | MLS-C01 Valid Exam Cram

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Tags: Latest MLS-C01 Test Questions, MLS-C01 Valid Exam Cram, MLS-C01 Exam Certification, New MLS-C01 Dumps Book, Reliable MLS-C01 Test Pattern

The MLS-C01 exam prepare materials of DumpsReview is high quality and high pass rate, it is completed by our experts who have a good understanding of real MLS-C01 exams and have many years of experience writing MLS-C01 study materials. They know very well what candidates really need most when they prepare for the MLS-C01 Exam. They also understand the real MLS-C01 exam situation very well. We will let you know what a real exam is like. You can try the Soft version of our MLS-C01 exam question, which can simulate the real exam.

The Amazon MLS-C01 Exam is intended for those who have a deep understanding of machine learning algorithms and frameworks, as well as experience with AWS services such as Amazon SageMaker, Amazon S3, Amazon EC2, and Amazon EMR. Candidates must also have experience with programming languages such as Python and R, as well as experience with data preprocessing, feature engineering, and model evaluation.

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With the development of computer hi-tech, the computer application is widely used in recent years. The demand of the higher position about computer is increasing. MLS-C01 exam vce files help people who are interested in Amazon company. If you have a useful certification, you will have outstanding advantage over other applicants while interviewing. Our MLS-C01 Exam Vce files help you go through examination and get certifications.

Pattern of the Exam

MLS-C01 test contains questions in the form of multiple choice (with only one correct option) and multiple answer (more than 1 correct response). The candidates will get 180 minutes to finish the exam. Also, they need to pay $300 for registration. They can also choose preferred language from the options such as English, Simplified Chinese, Korean, and Japanese. Finally, one has the opportunity of taking the exam online or in a testing center. Topics covered include ML operations as well as implementation, exploratory data analysis, data engineering, and modeling.

Achieving the Amazon MLS-C01 Certification demonstrates an individual's proficiency in machine learning and their ability to design and implement machine learning solutions using AWS services. It is a valuable certification for professionals looking to advance their careers in the field of machine learning and work with cutting-edge technologies. AWS Certified Machine Learning - Specialty certification validates an individual's skills and knowledge in the field of machine learning and is recognized by employers worldwide.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q250-Q255):

NEW QUESTION # 250
A financial services company is building a robust serverless data lake on Amazon S3. The data lake should be flexible and meet the following requirements:
- Support querying old and new data on Amazon S3 through Amazon Athena
and Amazon Redshift Spectrum.
- Support event-driven ETL pipelines
- Provide a quick and easy way to understand metadata
Which approach meets these requirements?

  • A. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Glue ETL job, and an AWS Glue Data catalog to search and discover metadata.
  • B. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Batch job, and an AWS Glue Data Catalog to search and discover metadata.
  • C. Use an AWS Glue crawler to crawl S3 data, an Amazon CloudWatch alarm to trigger an AWS Glue ETL job, and an external Apache Hive metastore to search and discover metadata.
  • D. Use an AWS Glue crawler to crawl S3 data, an AWS Lambda function to trigger an AWS Batch job, and an external Apache Hive metastore to search and discover metadata.

Answer: A

Explanation:
The AWS Glue Data Catalog is your persistent metadata store. It is a managed service that lets you store, annotate, and share metadata in the AWS Cloud in the same way you would in an Apache Hive metastore.
The Data Catalog is a drop-in replacement for the Apache Hive Metastore
https://docs.aws.amazon.com/zh_tw/glue/latest/dg/components-overview.html


NEW QUESTION # 251
A retail company wants to build a recommendation system for the company's website. The system needs to provide recommendations for existing users and needs to base those recommendations on each user's past browsing history. The system also must filter out any items that the user previously purchased.
Which solution will meet these requirements with the LEAST development effort?

  • A. Train a model by using a user-based collaborative filtering algorithm on Amazon SageMaker. Host the model on a SageMaker real-time endpoint. Configure an Amazon API Gateway API and an AWS Lambda function to handle real-time inference requests that the web application sends. Exclude the items that the user previously purchased from the results before sending the results back to the web application.
  • B. Use an Amazon Personalize PERSONALIZED_RANKING recipe to train a model. Create a real-time filter to exclude items that the user previously purchased. Create and deploy a campaign on Amazon Personalize. Use the GetPersonalizedRanking API operation to get the real-time recommendations.
  • C. Train a neural collaborative filtering model on Amazon SageMaker by using GPU instances. Host the model on a SageMaker real-time endpoint. Configure an Amazon API Gateway API and an AWS Lambda function to handle real-time inference requests that the web application sends. Exclude the items that the user previously purchased from the results before sending the results back to the web application.
  • D. Use an Amazon Personalize USER_ PERSONAL IZATION recipe to train a model Create a real-time filter to exclude items that the user previously purchased. Create and deploy a campaign on Amazon Personalize. Use the GetRecommendations API operation to get the real-time recommendations.

Answer: D

Explanation:
Amazon Personalize is a fully managed machine learning service that makes it easy for developers to create personalized user experiences at scale. It uses the same recommender system technology that Amazon uses to create its own personalized recommendations. Amazon Personalize provides several pre-built recipes that can be used to train models for different use cases. The USER_PERSONALIZATION recipe is designed to provide personalized recommendations for existing users based on their past interactions with items. The PERSONALIZED_RANKING recipe is designed to re-rank a list of items for a user based on their preferences. The USER_PERSONALIZATION recipe is more suitable for this use case because it can generate recommendations for each user without requiring a list of candidate items. To filter out the items that the user previously purchased, a real-time filter can be created and applied to the campaign. A real-time filter is a dynamic filter that uses the latest interaction data to exclude items from the recommendations. By using Amazon Personalize, the development effort is minimized because it handles the data processing, model training, and deployment automatically. The web application can use the GetRecommendations API operation to get the real-time recommendations from the campaign. References:
Amazon Personalize
What is Amazon Personalize?
USER_PERSONALIZATION recipe
PERSONALIZED_RANKING recipe
Filtering recommendations
GetRecommendations API operation


NEW QUESTION # 252
A technology startup is using complex deep neural networks and GPU compute to recommend the company's products to its existing customers based upon each customer's habits and interactions. The solution currently pulls each dataset from an Amazon S3 bucket before loading the data into a TensorFlow model pulled from the company's Git repository that runs locally. This job then runs for several hours while continually outputting its progress to the same S3 bucket. The job can be paused, restarted, and continued at any time in the event of a failure, and is run from a central queue.
Senior managers are concerned about the complexity of the solution's resource management and the costs involved in repeating the process regularly. They ask for the workload to be automated so it runs once a week, starting Monday and completing by the close of business Friday.
Which architecture should be used to scale the solution at the lowest cost?

  • A. Implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance
  • B. Implement the solution using a low-cost GPU-compatible Amazon EC2 instance and use the AWS Instance Scheduler to schedule the task
  • C. Implement the solution using Amazon ECS running on Spot Instances and schedule the task using the ECS service scheduler
  • D. Implement the solution using AWS Deep Learning Containers, run the workload using AWS Fargate running on Spot Instances, and then schedule the task using the built-in task scheduler

Answer: A

Explanation:
Explanation
The best architecture to scale the solution at the lowest cost is to implement the solution using AWS Deep Learning Containers and run the container as a job using AWS Batch on a GPU-compatible Spot Instance.
This option has the following advantages:
AWS Deep Learning Containers: These are Docker images that are pre-installed and optimized with popular deep learning frameworks such as TensorFlow, PyTorch, and MXNet. They can be easily deployed on Amazon EC2, Amazon ECS, Amazon EKS, and AWS Fargate. They can also be integrated with AWS Batch to run containerized batch jobs. Using AWS Deep Learning Containers can simplify the setup and configuration of the deep learning environment and reduce the complexity of the resource management.
AWS Batch: This is a fully managed service that enables you to run batch computing workloads on AWS. You can define compute environments, job queues, and job definitions to run your batch jobs.
You can also use AWS Batch to automatically provision compute resources based on the requirements of the batch jobs. You can specify the type and quantity of the compute resources, such as GPU instances, and the maximum price you are willing to pay for them. You can also use AWS Batch to monitor the status and progress of your batch jobs and handle any failures or interruptions.
GPU-compatible Spot Instance: This is an Amazon EC2 instance that uses a spare compute capacity that is available at a lower price than the On-Demand price. You can use Spot Instances to run your deep learning training jobs at a lower cost, as long as you are flexible about when your instances run and how long they run. You can also use Spot Instances with AWS Batch to automatically launch and terminate instances based on the availability and price of the Spot capacity. You can also use Spot Instances with Amazon EBS volumes to store your datasets, checkpoints, and logs, and attach them to your instances when they are launched. This way, you can preserve your data and resume your training even if your instances are interrupted.
References:
AWS Deep Learning Containers
AWS Batch
Amazon EC2 Spot Instances
Using Amazon EBS Volumes with Amazon EC2 Spot Instances


NEW QUESTION # 253
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?

  • A. Reinforcement learning
  • B. Linear regression
  • C. Clustering
  • D. Classification

Answer: D

Explanation:
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) ?answers that need to be predicted ?to train an algorithm.
With classification, businesses can answer the following questions:
Will this customer churn or not?
Will a customer renew their subscription?
Will a user downgrade a pricing plan?
Are there any signs of unusual customer behavior?
https://www.kdnuggets.com/2019/05/churn-prediction-machine-learning.html


NEW QUESTION # 254
A Machine Learning Specialist is required to build a supervised image-recognition model to identify a cat. The ML Specialist performs some tests and records the following results for a neural network-based image classifier:
Total number of images available = 1,000 Test set images = 100 (constant test set) The ML Specialist notices that, in over 75% of the misclassified images, the cats were held upside down by their owners.
Which techniques can be used by the ML Specialist to improve this specific test error?

  • A. Increase the number of layers for the neural network.
  • B. Increase the number of epochs for model training.
  • C. Increase the dropout rate for the second-to-last layer.
  • D. Increase the training data by adding variation in rotation for training images.

Answer: D

Explanation:
Explanation
To improve the test error for the image classifier, the Machine Learning Specialist should use the technique of increasing the training data by adding variation in rotation for training images. This technique is called data augmentation, which is a way of artificially expanding the size and diversity of the training dataset by applying various transformations to the original images, such as rotation, flipping, cropping, scaling, etc. Data augmentation can help the model learn more robust features that are invariant to the orientation, position, and size of the objects in the images. This can improve the generalization ability of the model and reduce the test error, especially for cases where the images are not well-aligned or have different perspectives1.
References:
1: Image Augmentation - Amazon SageMaker


NEW QUESTION # 255
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