MLS-C01 QUESTIONS [2025] - AMAZON MLS-C01 EXAM DUMPS

MLS-C01 Questions [2025] - Amazon MLS-C01 Exam Dumps

MLS-C01 Questions [2025] - Amazon MLS-C01 Exam Dumps

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q137-Q142):

NEW QUESTION # 137
A Data Science team is designing a dataset repository where it will store a large amount of training data commonly used in its machine learning models. As Data Scientists may create an arbitrary number of new datasets every day the solution has to scale automatically and be cost-effective. Also, it must be possible to explore the data using SQL.
Which storage scheme is MOST adapted to this scenario?

  • A. Store datasets as files in an Amazon EBS volume attached to an Amazon EC2 instance.
  • B. Store datasets as global tables in Amazon DynamoDB.
  • C. Store datasets as tables in a multi-node Amazon Redshift cluster.
  • D. Store datasets as files in Amazon S3.

Answer: C


NEW QUESTION # 138
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. A/B testing
  • B. Blue/green deployment
  • C. Shadow deployment
  • D. Canary release

Answer: C

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 # 139
A company that promotes healthy sleep patterns by providing cloud-connected devices currently hosts a sleep tracking application on AWS. The application collects device usage information from device users. The company's Data Science team is building a machine learning model to predict if and when a user will stop utilizing the company's devices. Predictions from this model are used by a downstream application that determines the best approach for contacting users.
The Data Science team is building multiple versions of the machine learning model to evaluate each version against the company's business goals. To measure long-term effectiveness, the team wants to run multiple served by the models.
Which solution satisfies these requirements with MINIMAL effort?

  • A. Build and host multiple models in Amazon SageMaker Neo to take into account different types of medical devices. Programmatically control which model is invoked for inference based on the medical device type.
  • B. Build and host multiple models in Amazon SageMaker. Create multiple Amazon SageMaker endpoints, one for each model. Programmatically control invoking different models for inference at the application layer.
  • C. Build and host multiple models in Amazon SageMaker. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.
  • D. Build and host multiple models in Amazon SageMaker. Create an Amazon SageMaker endpoint configuration with multiple production variants. Programmatically control the portion of the inferences served by the multiple models by updating the endpoint configuration.

Answer: D

Explanation:
A/B testing with Amazon SageMaker is required in the Exam. In A/B testing, you test different variants of your models and compare how each variant performs. Amazon SageMaker enables you to test multiple models or model versions behind the `same endpoint` using `production variants`. Each production variant identifies a machine learning (ML) model and the resources deployed for hosting the model. To test multiple models by `distributing traffic` between them, specify the `percentage of the traffic` that gets routed to each model by specifying the `weight` for each `production variant` in the endpoint configuration.
https://docs.aws.amazon.com/sagemaker/latest/dg/model-ab-testing.html#model-testing-target-variant


NEW QUESTION # 140
A gaming company has launched an online game where people can start playing for free but they need to pay if they choose to use certain features The company needs to build an automated system to predict whether or not a new user will become a paid user within 1 year The company has gathered a labeled dataset from 1 million users The training dataset consists of 1.000 positive samples (from users who ended up paying within 1 year) and
999.000 negative samples (from users who did not use any paid features) Each data sample consists of 200 features including user age, device, location, and play patterns Using this dataset for training, the Data Science team trained a random forest model that converged with over
99% accuracy on the training set However, the prediction results on a test dataset were not satisfactory.
Which of the following approaches should the Data Science team take to mitigate this issue? (Select TWO.)

  • A. Generate more positive samples by duplicating the positive samples and adding a small amount of noise to the duplicated data.
  • B. Add more deep trees to the random forest to enable the model to learn more features.
  • C. Change the cost function so that false negatives have a higher impact on the cost value than false positives
  • D. indicate a copy of the samples in the test database in the training dataset
  • E. Change the cost function so that false positives have a higher impact on the cost value than false negatives

Answer: C,D


NEW QUESTION # 141
A Machine Learning Specialist needs to create a data repository to hold a large amount of time-based training data for a new model. In the source system, new files are added every hour Throughout a single 24-hour period, the volume of hourly updates will change significantly. The Specialist always wants to train on the last 24 hours of the data Which type of data repository is the MOST cost-effective solution?

  • A. An Amazon S3 data lake with hourly object prefixes
  • B. An Amazon RDS database with hourly table partitions
  • C. An Amazon EMR cluster with hourly hive partitions on Amazon EBS volumes
  • D. An Amazon EBS-backed Amazon EC2 instance with hourly directories

Answer: A


NEW QUESTION # 142
......

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