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[General] MLS-C01 Valid Test Topics - MLS-C01 Pdf Torrent

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【General】 MLS-C01 Valid Test Topics - MLS-C01 Pdf Torrent

Posted at before yesterday 16:53      View：8 | Replies：1        Print      Only Author   [Copy Link] 1#
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To qualify for the Amazon MLS-C01 Exam, candidates must have at least one year of experience in developing and deploying machine learning models on the AWS platform. They should have a strong understanding of machine learning algorithms, data preparation, and model optimization techniques. Additionally, candidates should be proficient in Python programming language and have experience with AWS services such as Amazon S3, AWS Lambda, and AWS CloudFormation.
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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q190-Q195):NEW QUESTION # 190
A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences, and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?
  • A. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
  • B. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database.
  • C. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database.
  • D. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database.
Answer: B

NEW QUESTION # 191
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 # 192
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 BlazingText algorithm in continuous bag-of-words (CBOW) mode
  • B. Amazon SageMaker seq2seq algorithm
  • C. Amazon SageMaker Object2Vec algorithm
  • D. Amazon SageMaker BlazingText algorithm in Skip-gram mode
  • E. Combination of the Amazon SageMaker BlazingText algorithm in Batch Skip-gram mode with a custom recurrent neural network (RNN)
Answer: D,E
Explanation:
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 # 193
An insurance company developed a new experimental machine learning (ML) model to replace an existing model that is in production. The company must validate the quality of predictions from the new experimental model in a production environment before the company uses the new experimental model to serve general user requests.
Which one model can serve user requests at a time. The company must measure the performance of the new experimental model without affecting the current live traffic Which solution will meet these requirements?
  • A. Shadow deployment
  • B. Blue/green deployment
  • C. Canary release
  • D. A/B testing
Answer: A
Explanation:
Explanation
The best solution for this scenario is to use shadow deployment, which is a technique that allows the company to run the new experimental model in parallel with the existing model, without exposing it to the end users. In shadow deployment, the company can route the same user requests to both models, but only return the responses from the existing model to the users. The responses from the new experimental model are logged and analyzed for quality and performance metrics, such as accuracy, latency, and resource consumption12.
This way, the company can validate the new experimental model in a production environment, without affecting the current live traffic or user experience.
The other solutions are not suitable, because they have the following drawbacks:
A: A/B testing is a technique that involves splitting the user traffic between two or more models, and comparing their outcomes based on predefined metrics. However, this technique exposes the new experimental model to a portion of the end users, which might affect their experience if the model is not reliable or consistent with the existing model3.
B: Canary release is a technique that involves gradually rolling out the new experimental model to a small subset of users, and monitoring its performance and feedback. However, this technique also exposes the new experimental model to some end users, and requires careful selection and segmentation of the user groups4.
D: Blue/green deployment is a technique that involves switching the user traffic from the existing model (blue) to the new experimental model (green) at once, after testing and verifying the new model in a separate environment. However, this technique does not allow the company to validate the new experimental model in a production environment, and might cause service disruption or inconsistency if the new model is not compatible or stable5.
References:
1: Shadow Deployment: A Safe Way to Test in Production | LaunchDarkly Blog
2: Shadow Deployment: A Safe Way to Test in Production | LaunchDarkly Blog
3: A/B Testing for Machine Learning Models | AWS Machine Learning Blog
4: Canary Releases for Machine Learning Models | AWS Machine Learning Blog
5: Blue-Green Deployments for Machine Learning Models | AWS Machine Learning Blog

NEW QUESTION # 194
A Machine Learning Specialist needs to move and transform data in preparation for training Some of the data needs to be processed in near-real time and other data can be moved hourly There are existing Amazon EMR MapReduce jobs to clean and feature engineering to perform on the data Which of the following services can feed data to the MapReduce jobs? (Select TWO )
  • A. AWSDMS
  • B. Amazon Athena
  • C. Amazon ES
  • D. Amazon Kinesis
  • E. AWS Data Pipeline
Answer: B,D

NEW QUESTION # 195
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