AMAZON AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY DUMPS-ENSURE YOUR BRILLIANT SUCCESS IN EXAM

Amazon AWS-Certified-Machine-Learning-Specialty Dumps-Ensure your Brilliant Success In Exam

Amazon AWS-Certified-Machine-Learning-Specialty Dumps-Ensure your Brilliant Success In Exam

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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q115-Q120):

NEW QUESTION # 115
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. Create a single endpoint that accesses multiple models. Use Amazon SageMaker batch transform to control invoking the different models through the single endpoint.
  • B. 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.
  • C. 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.
  • D. 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.

Answer: C

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 # 116
An online reseller has a large, multi-column dataset with one column missing 30% of its data A Machine Learning Specialist believes that certain columns in the dataset could be used to reconstruct the missing data.
Which reconstruction approach should the Specialist use to preserve the integrity of the dataset?

  • A. Listwise deletion
  • B. Last observation carried forward
  • C. Mean substitution
  • D. Multiple imputation

Answer: D

Explanation:
Explanation
Multiple imputation is a technique that uses machine learning to generate multiple plausible values for each missing value in a dataset, based on the observed data and the relationships among the variables. Multiple imputation preserves the integrity of the dataset by accounting for the uncertainty and variability of the missing data, and avoids the bias and loss of information that may result from other methods, such as listwise deletion, last observation carried forward, or mean substitution. Multiple imputation can improve the accuracy and validity of statistical analysis and machine learning models that use the imputed dataset. References:
Managing missing values in your target and related datasets with automated imputation support in Amazon Forecast Imputation by feature importance (IBFI): A methodology to impute missing data in large datasets Multiple Imputation by Chained Equations (MICE) Explained


NEW QUESTION # 117
A Machine Learning team runs its own training algorithm on Amazon SageMaker. The training algorithm requires external assets. The team needs to submit both its own algorithm code and algorithm-specific parameters to Amazon SageMaker.
What combination of services should the team use to build a custom algorithm in Amazon SageMaker?
(Choose two.)

  • A. Amazon S3
  • B. Amazon ECS
  • C. AWS Secrets Manager
  • D. Amazon ECR
  • E. AWS CodeStar

Answer: A,D

Explanation:
The Machine Learning team wants to use its own training algorithm on Amazon SageMaker, and submit both its own algorithm code and algorithm-specific parameters. The best combination of services to build a custom algorithm in Amazon SageMaker are Amazon ECR and Amazon S3.
Amazon ECR is a fully managed container registry service that allows you to store, manage, and deploy Docker container images. You can use Amazon ECR to create a Docker image that contains your training algorithm code and any dependencies or libraries that it requires. You can also use Amazon ECR to push, pull, and manage your Docker images securely and reliably.
Amazon S3 is a durable, scalable, and secure object storage service that can store any amount and type of data. You can use Amazon S3 to store your training data, model artifacts, and algorithm-specific parameters. You can also use Amazon S3 to access your data and parameters from your training algorithm code, and to write your model output to a specified location.
Therefore, the Machine Learning team can use the following steps to build a custom algorithm in Amazon SageMaker:
Write the training algorithm code in Python, using the Amazon SageMaker Python SDK or the Amazon SageMaker Containers library to interact with the Amazon SageMaker service. The code should be able to read the input data and parameters from Amazon S3, and write the model output to Amazon S3.
Create a Dockerfile that defines the base image, the dependencies, the environment variables, and the commands to run the training algorithm code. The Dockerfile should also expose the ports that Amazon SageMaker uses to communicate with the container.
Build the Docker image using the Dockerfile, and tag it with a meaningful name and version.
Push the Docker image to Amazon ECR, and note the registry path of the image.
Upload the training data, model artifacts, and algorithm-specific parameters to Amazon S3, and note the S3 URIs of the objects.
Create an Amazon SageMaker training job, using the Amazon SageMaker Python SDK or the AWS CLI. Specify the registry path of the Docker image, the S3 URIs of the input and output data, the algorithm-specific parameters, and other configuration options, such as the instance type, the number of instances, the IAM role, and the hyperparameters.
Monitor the status and logs of the training job, and retrieve the model output from Amazon S3.
References:
Use Your Own Training Algorithms
Amazon ECR - Amazon Web Services
Amazon S3 - Amazon Web Services


NEW QUESTION # 118
Which of the following metrics should a Machine Learning Specialist generally use to compare/evaluate machine learning classification models against each other?

  • A. Area Under the ROC Curve (AUC)
  • B. Mean absolute percentage error (MAPE)
  • C. Misclassification rate
  • D. Recall

Answer: A

Explanation:
Area Under the ROC Curve (AUC) is a metric that measures the performance of a binary classifier across all possible thresholds. It is also known as the probability that a randomly chosen positive example will be ranked higher than a randomly chosen negative example by the classifier. AUC is a good metric to compare different classification models because it is independent of the class distribution and the decision threshold. It also captures both the sensitivity (true positive rate) and the specificity (true negative rate) of the model.
References:
* AWS Machine Learning Specialty Exam Guide
* AWS Machine Learning Specialty Sample Questions


NEW QUESTION # 119
A Machine Learning Specialist is assigned to a Fraud Detection team and must tune an XGBoost model, which is working appropriately for test data. However, with unknown data, it is not working as expected. The existing parameters are provided as follows.

Which parameter tuning guidelines should the Specialist follow to avoid overfitting?

  • A. Lower the max_depth parameter value.
  • B. Update the objective to binary:logistic.
  • C. Increase the max_depth parameter value.
  • D. Lower the min_child_weight parameter value.

Answer: A

Explanation:
Explanation
Overfitting occurs when a model performs well on the training data but poorly on the test data. This is often because the model has learned the training data too well and is not able to generalize to new data. To avoid overfitting, the Machine Learning Specialist should lower the max_depth parameter value. This will reduce the complexity of the model and make it less likely to overfit. According to the XGBoost documentation1, the max_depth parameter controls the maximum depth of a tree and lower values can help prevent overfitting. The documentation also suggests other ways to control overfitting, such as adding randomness, using regularization, and using early stopping1. References:
XGBoost Parameters


NEW QUESTION # 120
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