Simulations AWS-Certified-Machine-Learning-Specialty Pdf, Updated AWS-Certified-Machine-Learning-Specialty Test Cram

What's more, part of that Actualtests4sure AWS-Certified-Machine-Learning-Specialty dumps now are free: https://drive.google.com/open?id=1QpqsJEuTc_V0vO8rSsMf3PlAJw7F3CyV

If you want to pass your AWS-Certified-Machine-Learning-Specialty exam and get the AWS-Certified-Machine-Learning-Specialty certification which is crucial for you successfully, I highly recommend that you should choose the AWS-Certified-Machine-Learning-Specialty certification preparation materials from our company so that you can get a good understanding of the AWS-Certified-Machine-Learning-Specialty Exam that you are going to prepare for. We believe that if you decide to buy the AWS-Certified-Machine-Learning-Specialty exam materials from our company, you will pass your exam and get the AWS-Certified-Machine-Learning-Specialty certification in a more relaxed way than other people.

The AWS Machine Learning Specialty certification is designed for developers and data scientists who want to enhance their skills in using machine learning via the AWS platform.

AWS MLS-C01 Exam Certification Details:

Recommended Training / Books	Practical Data Science with Amazon SageMaker The Machine Learning Pipeline on AWS Deep Learning on AWS
Passing Score	750 / 1000
Exam Code	MLS-C01
Number of Questions	65
Exam Name	AWS Certified Machine Learning - Specialty (Machine Learning Specialty)
Exam Price	$300 USD

The AWS-Certified-Machine-Learning-Specialty Exam is a valuable certification for individuals who want to advance their careers in the field of machine learning. AWS Certified Machine Learning - Specialty certification demonstrates to employers that the individual has a deep understanding of machine learning concepts and AWS services. Additionally, the certification provides individuals with the knowledge and skills to design and deploy machine learning solutions on the AWS platform, which is a critical skill in today's data-driven business environment.

>> Simulations AWS-Certified-Machine-Learning-Specialty Pdf <<

Top Simulations AWS-Certified-Machine-Learning-Specialty Pdf and First-Grade Updated AWS-Certified-Machine-Learning-Specialty Test Cram & Effective Reliable AWS Certified Machine Learning - Specialty Test Braindumps

AWS-Certified-Machine-Learning-Specialty practice test can be your optimum selection and useful tool to deal with the urgent challenge. With over a decade’s striving, our AWS-Certified-Machine-Learning-Specialty training materials have become the most widely-lauded and much-anticipated products in industry. We will look to build up R&D capacity by modernizing innovation mechanisms and fostering a strong pool of professionals. Therefore, rest assured of full technical support from our professional elites in planning and designing AWS-Certified-Machine-Learning-Specialty Practice Test.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q271-Q276):

NEW QUESTION # 271
A Data Scientist needs to migrate an existing on-premises ETL process to the cloud. The current process runs at regular time intervals and uses PySpark to combine and format multiple large data sources into a single consolidated output for downstream processing.
The Data Scientist has been given the following requirements to the cloud solution:
- Combine multiple data sources.
- Reuse existing PySpark logic.
- Run the solution on the existing schedule.
- Minimize the number of servers that will need to be managed.
Which architecture should the Data Scientist use to build this solution?

A. Write the raw data to Amazon S3. Create an AWS Glue ETL job to perform the ETL processing against the input data. Write the ETL job in PySpark to leverage the existing logic. Create a new AWS Glue trigger to trigger the ETL job based on the existing schedule. Configure the output target of the ETL job to write to a "processed" location in Amazon S3 that is accessible for downstream use.
B. Write the raw data to Amazon S3. Schedule an AWS Lambda function to submit a Spark step to a persistent Amazon EMR cluster based on the existing schedule. Use the existing PySpark logic to run the ETL job on the EMR cluster. Output the results to a "processed" location in Amazon S3 that is accessible for downstream use.
C. Use Amazon Kinesis Data Analytics to stream the input data and perform real-time SQL queries against the stream to carry out the required transformations within the stream. Deliver the output results to a "processed" location in Amazon S3 that is accessible for downstream use.
D. Write the raw data to Amazon S3. Schedule an AWS Lambda function to run on the existing schedule and process the input data from Amazon S3. Write the Lambda logic in Python and implement the existing PySpark logic to perform the ETL process. Have the Lambda function output the results to a "processed" location in Amazon S3 that is accessible for downstream use.

Answer: A

Explanation:
Kinesis Data Analytics can not directly stream the input data.

NEW QUESTION # 272
When submitting Amazon SageMaker training jobs using one of the built-in algorithms, which common parameters MUST be specified? (Choose three.)

A. The validation channel identifying the location of validation data on an Amazon S3 bucket.
B. The output path specifying where on an Amazon S3 bucket the trained model will persist.
C. The Amazon EC2 instance class specifying whether training will be run using CPU or GPU.
D. Hyperparameters in a JSON array as documented for the algorithm used.
E. The IAM role that Amazon SageMaker can assume to perform tasks on behalf of the users.
F. The training channel identifying the location of training data on an Amazon S3 bucket.

Answer: B,C,F

NEW QUESTION # 273
A manufacturing company needs to identify returned smartphones that have been damaged by moisture. The company has an automated process that produces 2.000 diagnostic values for each phone. The database contains more than five million phone evaluations. The evaluation process is consistent, and there are no missing values in the data. A machine learning (ML) specialist has trained an Amazon SageMaker linear learner ML model to classify phones as moisture damaged or not moisture damaged by using all available features. The model's F1 score is 0.6.
What changes in model training would MOST likely improve the model's F1 score? (Select TWO.)

A. Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the scikit- iearn multi-dimensional scaling (MDS) algorithm.
B. Use the SageMaker k-nearest neighbors (k-NN) algorithm. Set a dimension reduction target of less than
1,000 to train the model.
C. Use the SageMaker k-means algorithm with k of less than 1.000 to train the model
D. Continue to use the SageMaker linear learner algorithm. Reduce the number of features with the SageMaker principal component analysis (PCA) algorithm.
E. Continue to use the SageMaker linear learner algorithm. Set the predictor type to regressor.

Answer: B,D

Explanation:
Option A is correct because reducing the number of features with the SageMaker PCA algorithm can help remove noise and redundancy from the data, and improve the model's performance. PCA is a dimensionality reduction technique that transforms the original features into a smaller set of linearly uncorrelated features called principal components. The SageMaker linear learner algorithm supports PCA as a built-in feature transformation option.
Option E is correct because using the SageMaker k-NN algorithm with a dimension reduction target of less than 1,000 can help the model learn from the similarity of the data points, and improve the model's performance. k-NN is a non-parametric algorithm that classifies an input based on the majority vote of its k nearest neighbors in the feature space. The SageMaker k-NN algorithm supports dimension reduction as a built-in feature transformation option.
Option B is incorrect because using the scikit-learn MDS algorithm to reduce the number of features is not a feasible option, as MDS is a computationally expensive technique that does not scale well to large datasets.
MDS is a dimensionality reduction technique that tries to preserve the pairwise distances between the original data points in a lower-dimensional space.
Option C is incorrect because setting the predictor type to regressor would change the model's objective from classification to regression, which is not suitable for the given problem. A regressor model would output a continuous value instead of a binary label for each phone.
Option D is incorrect because using the SageMaker k-means algorithm with k of less than 1,000 would not help the model classify the phones, as k-means is a clustering algorithm that groups the data points into k clusters based on their similarity, without using any labels. A clustering model would not output a binary label for each phone.
Amazon SageMaker Linear Learner Algorithm
Amazon SageMaker K-Nearest Neighbors (k-NN) Algorithm
[Principal Component Analysis - Scikit-learn]
[Multidimensional Scaling - Scikit-learn]

NEW QUESTION # 274
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 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.
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 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.
D. 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.

Answer: A

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 # 275
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 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.
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 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.
D. 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.

Answer: A

Explanation:
https://docs.aws.amazon.com/sagemaker/latest/dg/sms-workforce-private.html

NEW QUESTION # 276
......

Welcome to Actualtests4sure-the online website for providing you with the latest and valid Amazon study material. Here you will find the updated study dumps and training pdf for your AWS-Certified-Machine-Learning-Specialty certification. Our AWS-Certified-Machine-Learning-Specialty practice torrent offers you the realistic and accurate simulations of the real test. The AWS-Certified-Machine-Learning-Specialty Questions & answers are so valid and updated with detail explanations which make you easy to understand and master. The aim of our AWS-Certified-Machine-Learning-Specialty practice torrent is to help you successfully pass.

Updated AWS-Certified-Machine-Learning-Specialty Test Cram: https://www.actualtests4sure.com/AWS-Certified-Machine-Learning-Specialty-test-questions.html

BONUS!!! Download part of Actualtests4sure AWS-Certified-Machine-Learning-Specialty dumps for free: https://drive.google.com/open?id=1QpqsJEuTc_V0vO8rSsMf3PlAJw7F3CyV