VALID MLS-C01 EXAM QUESTION - MLS-C01 LATEST EXAM FORMAT

Valid MLS-C01 Exam Question - MLS-C01 Latest Exam Format

Valid MLS-C01 Exam Question - MLS-C01 Latest Exam Format

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Tags: Valid MLS-C01 Exam Question, MLS-C01 Latest Exam Format, MLS-C01 Answers Real Questions, MLS-C01 Exam Simulator, Pdf MLS-C01 Format

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The AWS Certified Machine Learning - Specialty exam is a challenging and comprehensive certification program that requires a good amount of preparation and study. It consists of 65 multiple-choice and multiple-response questions, and the duration of the exam is 180 minutes. MLS-C01 Exam Fee is $300, and it is available in multiple languages, including English, Japanese, Korean, and Simplified Chinese.

The AWS Certified Machine Learning - Specialty certification exam covers a wide range of topics, including data engineering, exploratory data analysis, feature engineering, model selection and optimization, and machine learning implementation and operations. Candidates are expected to have a strong understanding of machine learning algorithms and frameworks, as well as experience with AWS services such as Amazon SageMaker, Amazon Comprehend, and Amazon Rekognition. AWS Certified Machine Learning - Specialty certification is ideal for data scientists, machine learning engineers, and developers who want to demonstrate their skills and knowledge in machine learning on the AWS platform.

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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 (Q286-Q291):

NEW QUESTION # 286
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 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.
  • B. 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.
  • 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 workforce with Amazon Cognito. Build a labeling web application with AWS Amplify. Build a labeling workflow backend using AWS Lambda. Write the labeling instructions.

Answer: B

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 # 287
A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only.
What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?

  • A. Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
  • B. Serialize the trained model so the format is compressed for deployment. Tag the Docker image with theregistry hostname and upload it to Amazon S3.
  • C. Serialize the trained model so the format is compressed for deployment. Build the image and upload it toDocker Hub.
  • D. Build the Docker image with the inference code. Tag the Docker image with the registry hostname andupload it to Amazon ECR.

Answer: D

Explanation:
To deploy a model that was trained locally to Amazon SageMaker, the steps are:
* Build the Docker image with the inference code. The inference code should include the model loading, data preprocessing, prediction, and postprocessing logic. The Docker image should also include the dependencies and libraries required by the inference code and the model.
* Tag the Docker image with the registry hostname and upload it to Amazon ECR. Amazon ECR is a fully managed container registry that makes it easy to store, manage, and deploy container images. The registry hostname is the Amazon ECR registry URI for your account and Region. You can use the AWS CLI or the Amazon ECR console to tag and push the Docker image to Amazon ECR.
* Create a SageMaker model entity that points to the Docker image in Amazon ECR and the model artifacts in Amazon S3. The model entity is a logical representation of the model that contains the information needed to deploy the model for inference. The model artifacts are the files generated by the model training process, such as the model parameters and weights. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the model entity.
* Create an endpoint configuration that specifies the instance type and number of instances to use for hosting the model. The endpoint configuration also defines the production variants, which are the different versions of the model that you want to deploy. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the endpoint configuration.
* Create an endpoint that uses the endpoint configuration to deploy the model. The endpoint is a web service that exposes an HTTP API for inference requests. You can use the AWS CLI, the SageMaker Python SDK, or the SageMaker console to create the endpoint.
AWS Machine Learning Specialty Exam Guide
AWS Machine Learning Training - Deploy a Model on Amazon SageMaker
AWS Machine Learning Training - Use Your Own Inference Code with Amazon SageMaker Hosting Services


NEW QUESTION # 288
A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data Which AWS service should the Data Scientist use?

  • A. Amazon Redshift Spectrum
  • B. Amazon QuickSight
  • C. AWS Glue
  • D. Amazon Athena

Answer: D

Explanation:
Explanation
Amazon Athena is a serverless interactive query service that can analyze data in Amazon S3 using standard SQL. Amazon Athena can also query data from other sources, such as MySQL and Amazon Redshift, by using federated queries. Federated queries allow Amazon Athena to run SQL queries across data sources, such as relational and non-relational databases, data warehouses, and data lakes. By using Amazon Athena, the Data Scientist can perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon Redshift, and then calculating the average of a few selected columns from the joined data. Amazon Athena can also integrate with other AWS services, such as AWS Glue and Amazon QuickSight, to provide additional features, such as data cataloging and visualization.
References:
What is Amazon Athena? - Amazon Athena
Federated Query Overview - Amazon Athena
Querying Data from Amazon S3 - Amazon Athena
Querying Data from MySQL - Amazon Athena
[Querying Data from Amazon Redshift - Amazon Athena]


NEW QUESTION # 289
A company is building a predictive maintenance model for its warehouse equipment. The model must predict the probability of failure of all machines in the warehouse. The company has collected 10.000 event samples within 3 months. The event samples include 100 failure cases that are evenly distributed across 50 different machine types.
How should the company prepare the data for the model to improve the model's accuracy?

  • A. Oversample the failure cases by using the Synthetic Minority Oversampling Technique (SMOTE).
  • B. Undersample the non-failure events. Stratify the non-failure events by machine type.
  • C. Adjust the class weight to account for each machine type.
  • D. Undersample the non-failure events by using the Synthetic Minority Oversampling Technique (SMOTE).

Answer: A

Explanation:
In predictive maintenance, when a dataset is imbalanced (with far fewer failure cases than non-failure cases), oversampling the minority class helps the model learn from the minority class effectively. The Synthetic Minority Oversampling Technique (SMOTE) generates synthetic samples for the minority class by creating data points between existing minority class instances. This can enhance the model's ability to recognize failure patterns, particularly in imbalanced datasets.
SMOTE increases the effective presence of failure cases in the dataset, providing a balanced learning environment for the model. This is more effective than undersampling, which would risk losing important non- failure data.


NEW QUESTION # 290
A company is setting up an Amazon SageMaker environment. The corporate data security policy does not allow communication over the internet.
How can the company enable the Amazon SageMaker service without enabling direct internet access to Amazon SageMaker notebook instances?

  • A. Route Amazon SageMaker traffic through an on-premises network.
  • B. Create VPC peering with Amazon VPC hosting Amazon SageMaker.
  • C. Create Amazon SageMaker VPC interface endpoints within the corporate VPC.
  • D. Create a NAT gafjway within the corporate VPC.

Answer: C


NEW QUESTION # 291
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