AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY CERTIFICATION EXAM INFOR, LATEST AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY BRAINDUMPS

AWS-Certified-Machine-Learning-Specialty Certification Exam Infor, Latest AWS-Certified-Machine-Learning-Specialty Braindumps

AWS-Certified-Machine-Learning-Specialty Certification Exam Infor, Latest AWS-Certified-Machine-Learning-Specialty Braindumps

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For exam applicants VCETorrent offers real Amazon AWS-Certified-Machine-Learning-Specialty exam questions. There are three formats of the AWS Certified Machine Learning - Specialty (AWS-Certified-Machine-Learning-Specialty) practice material. These formats are PDF, desktop practice exam software, and web-based AWS Certified Machine Learning - Specialty (AWS-Certified-Machine-Learning-Specialty) practice exam. With these questions, you can crack the Amazon AWS-Certified-Machine-Learning-Specialty certification exam and save your time and money.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) certification exam is designed to test the knowledge and expertise of professionals in the field of machine learning. AWS-Certified-Machine-Learning-Specialty Exam is intended for individuals who have a solid understanding of machine learning concepts and practices and are looking to validate their skills and knowledge in this area. AWS-Certified-Machine-Learning-Specialty exam is designed to test the candidate's ability to design, implement, and maintain machine learning solutions on the AWS platform.

How to Prepare for the AWS Certified Machine Learning Specialty Exam

Preparation Guide for For AWS Certified Machine Learning Specialty Exam

Introduction

Amazon Web Services is the current market leader in the field of cloud computing. Many organizations are boarding the AWS train for very promising benefits. Profitability, flexibility, ease of use and comprehensive support are the pillars of AWS's popularity. As AWS gained popularity, many companies began looking for AWS certified professionals.

AWS certified machine learning specialty certification is one of many AWS certifications popular today. AWS provides certification to validate an individual's skills and experience in AWS specific tools, resources and technologies. The following discussion will focus on the details of the AWS certified machine learning specialty certification. The purpose of the following discussion is to support the preparation of candidates for the certification exam.

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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. AWS-Certified-Machine-Learning-Specialty 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.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q113-Q118):

NEW QUESTION # 113
A machine learning (ML) engineer has created a feature repository in Amazon SageMaker Feature Store for the company. The company has AWS accounts for development, integration, and production. The company hosts a feature store in the development account. The company uses Amazon S3 buckets to store feature values offline. The company wants to share features and to allow the integration account and the production account to reuse the features that are in the feature repository.
Which combination of steps will meet these requirements? (Select TWO.)

  • A. Create an IAM role in the development account that the integration account and production account can assume. Attach IAM policies to the role that allow access to the feature repository and the S3 buckets.
  • B. Create an AWS PrivateLink endpoint in the development account for SageMaker.
  • C. Use AWS Security Token Service (AWS STS) from the integration account and the production account to retrieve credentials for the development account.
  • D. Share the feature repository that is associated the S3 buckets from the development account to the integration account and the production account by using AWS Resource Access Manager (AWS RAM).
  • E. Set up S3 replication between the development S3 buckets and the integration and production S3 buckets.

Answer: A,D

Explanation:
The combination of steps that will meet the requirements are to create an IAM role in the development account that the integration account and production account can assume, attach IAM policies to the role that allow access to the feature repository and the S3 buckets, and share the feature repository that is associated with the S3 buckets from the development account to the integration account and the production account by using AWS Resource Access Manager (AWS RAM). This approach will enable cross-account access and sharing of the features stored in Amazon SageMaker Feature Store and Amazon S3.
Amazon SageMaker Feature Store is a fully managed, purpose-built repository to store, update, search, and share curated data used in training and prediction workflows. The service provides feature management capabilities such as enabling easy feature reuse, low latency serving, time travel, and ensuring consistency between features used in training and inference workflows. A feature group is a logical grouping of ML features whose organization and structure is defined by a feature group schema. A feature group schema consists of a list of feature definitions, each of which specifies the name, type, and metadata of a feature.
Amazon SageMaker Feature Store stores the features in both an online store and an offline store. The online store is a low-latency, high-throughput store that is optimized for real-time inference. The offline store is a historical store that is backed by an Amazon S3 bucket and is optimized for batch processing and model training1.
AWS Identity and Access Management (IAM) is a web service that helps you securely control access to AWS resources for your users. You use IAM to control who can use your AWS resources (authentication) and what resources they can use and in what ways (authorization). An IAM role is an IAM identity that you can create in your account that has specific permissions. You can use an IAM role to delegate access to users, applications, or services that don't normally have access to your AWS resources. For example, you can create an IAM role in your development account that allows the integration account and the production account to assume the role and access the resources in the development account. You can attach IAM policies to the role that specify the permissions for the feature repository and the S3 buckets. You can also use IAM conditions to restrict the access based on the source account, IP address, or other factors2.
AWS Resource Access Manager (AWS RAM) is a service that enables you to easily and securely share AWS resources with any AWS account or within your AWS Organization. You can share AWS resources that you own with other accounts using resource shares. A resource share is an entity that defines the resources that you want to share, and the principals that you want to share with. For example, you can share the feature repository that is associated with the S3 buckets from the development account to the integration account and the production account by creating a resource share in AWS RAM. You can specify the feature group ARN and the S3 bucket ARN as the resources, and the integration account ID and the production account ID as the principals. You can also use IAM policies to further control the access to the shared resources3.
The other options are either incorrect or unnecessary. Using AWS Security Token Service (AWS STS) from the integration account and the production account to retrieve credentials for the development account is not required, as the IAM role in the development account can provide temporary security credentials for the cross- account access. Setting up S3 replication between the development S3 buckets and the integration and production S3 buckets would introduce redundancy and inconsistency, as the S3 buckets are already shared through AWS RAM. Creating an AWS PrivateLink endpoint in the development account for SageMaker is not relevant, as it is used to securely connect to SageMaker services from a VPC, not from another account.
References:
* 1: Amazon SageMaker Feature Store - Amazon Web Services
* 2: What Is IAM? - AWS Identity and Access Management
* 3: What Is AWS Resource Access Manager? - AWS Resource Access Manager


NEW QUESTION # 114
A data scientist is using an Amazon SageMaker notebook instance and needs to securely access data stored in a specific Amazon S3 bucket.
How should the data scientist accomplish this?

  • A. Add an S3 bucket policy allowing GetObject, PutObject, and ListBucket permissions to the Amazon SageMaker notebook ARN as principal.
  • B. Encrypt the objects in the S3 bucket with a custom AWS Key Management Service (AWS KMS) key that only the notebook owner has access to.
  • C. Attach the policy to the IAM role associated with the notebook that allows GetObject, PutObject, and ListBucket operations to the specific S3 bucket.
  • D. Use a script in a lifecycle configuration to configure the AWS CLI on the instance with an access key ID and secret.

Answer: C


NEW QUESTION # 115
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
* Real-time analytics
* Interactive analytics of historical data
* Clickstream analytics
* Product recommendations
Which services should the Specialist use?

  • A. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
  • B. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations
  • C. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
  • D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS Glue to generate personalized product recommendations

Answer: A


NEW QUESTION # 116
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. 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.
  • C. Initialize the words by word2vec embeddings pretrained on a large collection of news articles related to the energy sector.
  • D. Reduce the learning rate and run the training process until the training loss stops decreasing.

Answer: D


NEW QUESTION # 117
An ecommerce company has used Amazon SageMaker to deploy a factorization machines (FM) model to suggest products for customers. The company's data science team has developed two new models by using the TensorFlow and PyTorch deep learning frameworks. The company needs to use A/B testing to evaluate the new models against the deployed model.
...required A/B testing setup is as follows:
* Send 70% of traffic to the FM model, 15% of traffic to the TensorFlow model, and 15% of traffic to the Py Torch model.
* For customers who are from Europe, send all traffic to the TensorFlow model
..sh architecture can the company use to implement the required A/B testing setup?

  • A. Create two production variants for the TensorFlow and PyTorch models. Specify the weight for each production variant in the SageMaker endpoint configuration. Update the existing SageMaker endpoint with the new configuration. To send traffic to the TensorFlow model for customers who are from Europe, set the TargetVariant header in the request to point to the variant name of the TensorFlow model.
  • B. Create two new SageMaker endpoints for the TensorFlow and PyTorch models in addition to the existing SageMaker endpoint. Create a Network Load Balancer. Create a target group for each endpoint.
    Configure listener rules and add weight to the target groups. To send traffic to the TensorFlow model for customers who are from Europe, create an additional listener rule to forward traffic to the TensorFlow target group.
  • C. Create two new SageMaker endpoints for the TensorFlow and PyTorch models in addition to the existing SageMaker endpoint. Create an Application Load Balancer Create a target group for each endpoint. Configure listener rules and add weight to the target groups. To send traffic to the TensorFlow model for customers who are from Europe, create an additional listener rule to forward traffic to the TensorFlow target group.
  • D. Create two production variants for the TensorFlow and PyTorch models. Create an auto scaling policy and configure the desired A/B weights to direct traffic to each production variant Update the existing SageMaker endpoint with the auto scaling policy. To send traffic to the TensorFlow model for customers who are from Europe, set the TargetVariant header in the request to point to the variant name of the TensorFlow model.

Answer: A

Explanation:
Explanation
The correct answer is D because it allows the company to use the existing SageMaker endpoint and leverage the built-in functionality of production variants for A/B testing. Production variants can be used to test ML models that have been trained using different training datasets, algorithms, and ML frameworks; test how they perform on different instance types; or a combination of all of the above1. By specifying the weight for each production variant in the endpoint configuration, the company can control how much traffic to send to each variant. By setting the TargetVariant header in the request, the company can invoke a specific variant directly for each request2. This enables the company to implement the required A/B testing setup without creating additional endpoints or load balancers.
References:
1: Production variants - Amazon SageMaker
2: A/B Testing ML models in production using Amazon SageMaker | AWS Machine Learning Blog


NEW QUESTION # 118
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