AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY CERTIFIED & EXAM SAMPLE AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY ONLINE

AWS-Certified-Machine-Learning-Specialty Certified & Exam Sample AWS-Certified-Machine-Learning-Specialty Online

AWS-Certified-Machine-Learning-Specialty Certified & Exam Sample AWS-Certified-Machine-Learning-Specialty Online

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Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Certification Exam is designed for individuals who are interested in demonstrating their expertise in the field of machine learning on Amazon Web Services (AWS) platform. AWS Certified Machine Learning - Specialty certification exam is intended for professionals who have a deep understanding of AWS services and a strong background in machine learning. It is one of the most sought-after certifications in the industry and is highly valued by employers.

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This is similar to the AWS-Certified-Machine-Learning-Specialty desktop format but this is browser-based. It requires an active internet connection to run and is compatible with all browsers such as Google Chrome, Mozilla Firefox, Opera, MS Edge, Safari, Internet Explorer, and others. The Amazon AWS-Certified-Machine-Learning-Specialty Mock Exam helps you self-evaluate your Amazon AWS-Certified-Machine-Learning-Specialty exam preparation and mistakes. This way you improve consistently and attempt the AWS-Certified-Machine-Learning-Specialty certification exam in an optimal way for excellent results in the exam.

Amazon MLS-C01 (AWS Certified Machine Learning - Specialty) Certification Exam is designed for individuals who want to validate their expertise in machine learning technologies and AWS services. AWS Certified Machine Learning - Specialty certification is ideal for data scientists, software developers, and IT professionals who want to demonstrate their proficiency in designing, building, and deploying machine learning models on AWS.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q226-Q231):

NEW QUESTION # 226
A telecommunications company is developing a mobile app for its customers. The company is using an Amazon SageMaker hosted endpoint for machine learning model inferences.
Developers want to introduce a new version of the model for a limited number of users who subscribed to a preview feature of the app. After the new version of the model is tested as a preview, developers will evaluate its accuracy. If a new version of the model has better accuracy, developers need to be able to gradually release the new version for all users over a fixed period of time.
How can the company implement the testing model with the LEAST amount of operational overhead?

  • A. Update the DesiredWeightsAndCapacity data type with the new version of the model by using the UpdateEndpointWeightsAndCapacities operation with the DesiredWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. When the new version of the model is ready for release, gradually increase DesiredWeight until all users have the updated version.
  • B. Configure two SageMaker hosted endpoints that serve the different versions of the model. Create an Application Load Balancer (ALB) to route traffic to both endpoints based on the TargetVariant query string parameter. Reconfigure the app to send the TargetVariant query string parameter for users who subscribed to the preview feature. When the new version of the model is ready for release, change the ALB's routing algorithm to weighted until all users have the updated version.
  • C. Configure two SageMaker hosted endpoints that serve the different versions of the model. Create an Amazon Route 53 record that is configured with a simple routing policy and that points to the current version of the model. Configure the mobile app to use the endpoint URL for users who subscribed to the preview feature and to use the Route 53 record for other users. When the new version of the model is ready for release, add a new model version endpoint to Route 53, and switch the policy to weighted until all users have the updated version.
  • D. Update the ProductionVariant data type with the new version of the model by using the CreateEndpointConfig operation with the InitialVariantWeight parameter set to 0. Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. When the new version of the model is ready for release, gradually increase InitialVariantWeight until all users have the updated version.

Answer: A

Explanation:
The best solution for implementing the testing model with the least amount of operational overhead is to use the following steps:
Update the DesiredWeightsAndCapacity data type with the new version of the model by using the UpdateEndpointWeightsAndCapacities operation with the DesiredWeight parameter set to 0. This operation allows the developers to update the variant weights and capacities of an existing SageMaker endpoint without deleting and recreating the endpoint. Setting the DesiredWeight parameter to 0 means that the new version of the model will not receive any traffic initially1 Specify the TargetVariant parameter for InvokeEndpoint calls for users who subscribed to the preview feature. This parameter allows the developers to override the variant weights and direct a request to a specific variant. This way, the developers can test the new version of the model for a limited number of users who opted in for the preview feature2 When the new version of the model is ready for release, gradually increase DesiredWeight until all users have the updated version. This operation allows the developers to perform a gradual rollout of the new version of the model and monitor its performance and accuracy. The developers can adjust the variant weights and capacities as needed until the new version of the model serves all the traffic1 The other options are incorrect because they either require more operational overhead or do not support the desired use cases. For example:
Option A uses the CreateEndpointConfig operation with the InitialVariantWeight parameter set to 0. This operation creates a new endpoint configuration, which requires deleting and recreating the endpoint to apply the changes. This adds extra overhead and downtime for the endpoint. It also does not support the gradual rollout of the new version of the model3 Option B uses two SageMaker hosted endpoints that serve the different versions of the model and an Application Load Balancer (ALB) to route traffic to both endpoints based on the TargetVariant query string parameter. This option requires creating and managing additional resources and services, such as the second endpoint and the ALB. It also requires changing the app code to send the query string parameter for the preview feature4 Option D uses the access key and secret key of the IAM user with appropriate KMS and ECR permissions. This is not a secure way to pass credentials to the Processing job. It also requires the ML specialist to manage the IAM user and the keys.
References:
1: UpdateEndpointWeightsAndCapacities - Amazon SageMaker
2: InvokeEndpoint - Amazon SageMaker
3: CreateEndpointConfig - Amazon SageMaker
4: Application Load Balancer - Elastic Load Balancing


NEW QUESTION # 227
A machine learning (ML) specialist needs to extract embedding vectors from a text series. The goal is to provide a ready-to-ingest feature space for a data scientist to develop downstream ML predictive models. The text consists of curated sentences in English. Many sentences use similar words but in different contexts.
There are questions and answers among the sentences, and the embedding space must differentiate between them.
Which options can produce the required embedding vectors that capture word context and sequential QA information? (Choose two.)

  • A. Amazon SageMaker Object2Vec algorithm
  • B. Amazon SageMaker BlazingText algorithm in Skip-gram mode
  • C. Amazon SageMaker seq2seq algorithm
  • D. Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN)
  • E. Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode

Answer: B,D

Explanation:
* To capture word context and sequential QA information, the embedding vectors need to consider both the order and the meaning of the words in the text.
* Option B, Amazon SageMaker BlazingText algorithm in Skip-gram mode, is a valid option because it can learn word embeddings that capture the semantic similarity and syntactic relations between words based on their co-occurrence in a window of words. Skip-gram mode can also handle rare words better than continuous bag-of-words (CBOW) mode1.
* Option E, combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN), is another valid option because it can leverage the advantages of Skip-gram mode and also use an RNN to model the sequential nature of the text. An RNN can capture the temporal dependencies and long-term dependencies between words, which are important for QA tasks2.
* Option A, Amazon SageMaker seq2seq algorithm, is not a valid option because it is designed for sequence-to-sequence tasks such as machine translation, summarization, or chatbots. It does not produce embedding vectors for text series, but rather generates an output sequence given an input sequence3.
* Option C, Amazon SageMaker Object2Vec algorithm, is not a valid option because it is designed for learning embeddings for pairs of objects, such as text-image, text-text, or image-image. It does not produce embedding vectors for text series, but rather learns a similarity function between pairs of objects4.
* Option D, Amazon SageMaker BlazingText algorithm in continuous bag-of-words (CBOW) mode, is not a valid option because it does not capture word context as well as Skip-gram mode. CBOW mode predicts a word given its surrounding words, while Skip-gram mode predicts the surrounding words given a word. CBOW mode is faster and more suitable for frequent words, but Skip-gram mode can learn more meaningful embeddings for rare words1.
References:
* 1: Amazon SageMaker BlazingText
* 2: Recurrent Neural Networks (RNNs)
* 3: Amazon SageMaker Seq2Seq
* 4: Amazon SageMaker Object2Vec


NEW QUESTION # 228
A Machine Learning Specialist is building a prediction model for a large number of features using linear models, such as linear regression and logistic regression During exploratory data analysis the Specialist observes that many features are highly correlated with each other This may make the model unstable What should be done to reduce the impact of having such a large number of features?

  • A. Perform one-hot encoding on highly correlated features
  • B. Create a new feature space using principal component analysis (PCA)
  • C. Apply the Pearson correlation coefficient
  • D. Use matrix multiplication on highly correlated features.

Answer: D


NEW QUESTION # 229
A Machine Learning Specialist is using an Amazon SageMaker notebook instance in a private subnet of a corporate VPC. The ML Specialist has important data stored on the Amazon SageMaker notebook instance's Amazon EBS volume, and needs to take a snapshot of that EBS volume. However the ML Specialist cannot find the Amazon SageMaker notebook instance's EBS volume or Amazon EC2 instance within the VPC.
Why is the ML Specialist not seeing the instance visible in the VPC?

  • A. Amazon SageMaker notebook instances are based on AWS ECS instances running within AWS service accounts.
  • B. Amazon SageMaker notebook instances are based on the EC2 instances within the customer account, but they run outside of VPCs.
  • C. Amazon SageMaker notebook instances are based on EC2 instances running within AWS service accounts.
  • D. Amazon SageMaker notebook instances are based on the Amazon ECS service within customer accounts.

Answer: C


NEW QUESTION # 230
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 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.
  • 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 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.

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 # 231
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