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100% Pass Quiz 2025 Amazon MLS-C01: AWS Certified Machine Learning - Specialty–Reliable Exam Dumps.zip

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Amazon MLS-C01 certification exam is designed to test the knowledge and skills of candidates who are interested in becoming an AWS Certified Machine Learning Specialist. MLS-C01 exam is intended to validate the candidate's ability to design, implement, deploy, and maintain machine learning solutions for a variety of business use cases. MLS-C01 Exam is suitable for individuals who have a deep understanding of machine learning algorithms, statistical modeling, data analysis, and data preprocessing techniques.

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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Exam is a certification exam offered by Amazon Web Services (AWS) for individuals who want to prove their expertise in machine learning on the AWS platform. MLS-C01 exam is designed to test the knowledge and skills of candidates in various aspects of machine learning, including data preparation, model training, deployment, and monitoring. MLS-C01 Exam is intended for professionals who have a strong understanding of AWS services and machine learning concepts, and who have experience working with these technologies.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q317-Q322):

NEW QUESTION # 317
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 QuickSight
B. Amazon Athena
C. AWS Glue
D. Amazon Redshift Spectrum

Answer: B

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 # 318
A company has set up and deployed its machine learning (ML) model into production with an endpoint using Amazon SageMaker hosting services. The ML team has configured automatic scaling for its SageMaker instances to support workload changes. During testing, the team notices that additional instances are being launched before the new instances are ready. This behavior needs to change as soon as possible.
How can the ML team solve this issue?

A. Decrease the cooldown period for the scale-in activity. Increase the configured maximum capacity of instances.
B. Replace the current endpoint with a multi-model endpoint using SageMaker.
C. Increase the cooldown period for the scale-out activity.
D. Set up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.

Answer: C

Explanation:
Explanation
The correct solution for changing the scaling behavior of the SageMaker instances is to increase the cooldown period for the scale-out activity. The cooldown period is the amount of time, in seconds, after a scaling activity completes before another scaling activity can start. By increasing the cooldown period for the scale-out activity, the ML team can ensure that the new instances are ready before launching additional instances. This will prevent over-scaling and reduce costs1 The other options are incorrect because they either do not solve the issue or require unnecessary steps. For example:
Option A decreases the cooldown period for the scale-in activity and increases the configured maximum capacity of instances. This option does not address the issue of launching additional instances before the new instances are ready. It may also cause under-scaling and performance degradation.
Option B replaces the current endpoint with a multi-model endpoint using SageMaker. A multi-model endpoint is an endpoint that can host multiple models using a single endpoint. It does not affect the scaling behavior of the SageMaker instances. It also requires creating a new endpoint and updating the application code to use it2 Option C sets up Amazon API Gateway and AWS Lambda to trigger the SageMaker inference endpoint.
Amazon API Gateway is a service that allows users to create, publish, maintain, monitor, and secure APIs. AWS Lambda is a service that lets users run code without provisioning or managing servers.
These services do not affect the scaling behavior of the SageMaker instances. They also require creating and configuring additional resources and services34 References:
1: Automatic Scaling - Amazon SageMaker
2: Create a Multi-Model Endpoint - Amazon SageMaker
3: Amazon API Gateway - Amazon Web Services
4: AWS Lambda - Amazon Web Services

NEW QUESTION # 319
A large consumer goods manufacturer has the following products on sale:
* 34 different toothpaste variants
* 48 different toothbrush variants
* 43 different mouthwash variants
The entire sales history of all these products is available in Amazon S3. Currently, the company is using custom-built autoregressive integrated moving average (ARIMA) models to forecast demand for these products. The company wants to predict the demand for a new product that will soon be launched.
Which solution should a Machine Learning Specialist apply?

A. Train an Amazon SageMaker k-means clustering algorithm to forecast demand for the new product.
B. Train an Amazon SageMaker DeepAR algorithm to forecast demand for the new product.
C. Train a custom XGBoost model to forecast demand for the new product.
D. Train a custom ARIMA model to forecast demand for the new product.

Answer: B

Explanation:
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.
Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html

NEW QUESTION # 320
A retail company stores 100 GB of daily transactional data in Amazon S3 at periodic intervals. The company wants to identify the schema of the transactional dat a. The company also wants to perform transformations on the transactional data that is in Amazon S3.
The company wants to use a machine learning (ML) approach to detect fraud in the transformed data.
Which combination of solutions will meet these requirements with the LEAST operational overhead? {Select THREE.)

A. Use AWS Glue crawlers to scan the data and identify the schema.
B. Use AWS Glue workflows and AWS Glue jobs to perform data transformations.
C. Use Amazon Redshift to store procedures to perform data transformations
D. Use Amazon Fraud Detector to train a model to detect fraud.
E. Use Amazon Athena to scan the data and identify the schema.
F. Use Amazon Redshift ML to train a model to detect fraud.

Answer: A,B,D

Explanation:
To meet the requirements with the least operational overhead, the company should use AWS Glue crawlers, AWS Glue workflows and jobs, and Amazon Fraud Detector. AWS Glue crawlers can scan the data in Amazon S3 and identify the schema, which is then stored in the AWS Glue Data Catalog. AWS Glue workflows and jobs can perform data transformations on the data in Amazon S3 using serverless Spark or Python scripts. Amazon Fraud Detector can train a model to detect fraud using the transformed data and the company's historical fraud labels, and then generate fraud predictions using a simple API call.
Option A is incorrect because Amazon Athena is a serverless query service that can analyze data in Amazon S3 using standard SQL, but it does not perform data transformations or fraud detection.
Option C is incorrect because Amazon Redshift is a cloud data warehouse that can store and query data using SQL, but it requires provisioning and managing clusters, which adds operational overhead. Moreover, Amazon Redshift does not provide a built-in fraud detection capability.
Option E is incorrect because Amazon Redshift ML is a feature that allows users to create, train, and deploy machine learning models using SQL commands in Amazon Redshift. However, using Amazon Redshift ML would require loading the data from Amazon S3 to Amazon Redshift, which adds complexity and cost. Also, Amazon Redshift ML does not support fraud detection as a use case.
References:
AWS Glue Crawlers
AWS Glue Workflows and Jobs
Amazon Fraud Detector

NEW QUESTION # 321
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 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: 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.
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 # 322
......

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