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Dumps Amazon AWS-Certified-Machine-Learning-Specialty Download, Regualer AWS-Cer
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If you want to success in your career as a Amazon Certified Professional, you must think outside the box. It would be beneficial if you considered adding AWS Certified Machine Learning - Specialty to your resume. To get this certification, you must pass the AWS-Certified-Machine-Learning-Specialty exam conducted by Amazon. Passing the AWS Certified Machine Learning - Specialty exam will help you advance your career. It is not an easy task to pass the AWS Certified Machine Learning - Specialty certification exam on the first attempt, but now Exam-Killer is here to help. To assist you with remote study, Exam-Killer provides Amazon AWS-Certified-Machine-Learning-Specialty Exam Questions to make your test preparation complete. The Amazon AWS-Certified-Machine-Learning-Specialty exam questions simulate the actual exam pattern, allowing you to pass the AWS Certified Machine Learning - Specialty certification exam the first time.
To prepare for the Amazon MLS-C01 Exam, candidates should have a strong foundation in machine learning concepts and techniques, as well as experience working with AWS services and tools. They should also have experience working on machine learning projects, either in a professional or personal capacity. In addition, candidates should have a good understanding of programming languages such as Python, as well as knowledge of statistics, mathematics, and data analysis.
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Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q240-Q245):NEW QUESTION # 240
A Machine Learning Specialist is working with a large company to leverage machine learning within its products. The company wants to group its customers into categories based on which customers will and will not churn within the next 6 months. The company has labeled the data available to the Specialist.
Which machine learning model type should the Specialist use to accomplish this task?
- A. Clustering
- B. Reinforcement learning
- C. Linear regression
- D. Classification
Answer: D
Explanation:
Explanation
The goal of classification is to determine to which class or category a data point (customer in our case) belongs to. For classification problems, data scientists would use historical data with predefined target variables AKA labels (churner/non-churner) - answers that need to be predicted - to train an algorithm. With classification, businesses can answer the following questions:
* Will this customer churn or not?
* Will a customer renew their subscription?
* Will a user downgrade a pricing plan?
* Are there any signs of unusual customer behavior?
NEW QUESTION # 241
A data scientist at a financial services company used Amazon SageMaker to train and deploy a model that predicts loan defaults. The model analyzes new loan applications and predicts the risk of loan default. To train the model, the data scientist manually extracted loan data from a database. The data scientist performed the model training and deployment steps in a Jupyter notebook that is hosted on SageMaker Studio notebooks. The model's prediction accuracy is decreasing over time. Which combination of slept in the MOST operationally efficient way for the data scientist to maintain the model's accuracy? (Select TWO.)
- A. Configure SageMaker Model Monitor with an accuracy threshold to check for model drift. Initiate an Amazon CloudWatch alarm when the threshold is exceeded. Connect the workflow in SageMaker Pipelines with the CloudWatch alarm to automatically initiate retraining.
- B. Rerun the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model.
- C. Export the training and deployment code from the SageMaker Studio notebooks into a Python script. Package the script into an Amazon Elastic Container Service (Amazon ECS) task that an AWS Lambda function can initiate.
- D. Store the model predictions in Amazon S3 Create a daily SageMaker Processing job that reads the predictions from Amazon S3, checks for changes in model prediction accuracy, and sends an email notification if a significant change is detected.
- E. Use SageMaker Pipelines to create an automated workflow that extracts fresh data, trains the model, and deploys a new version of the model.
Answer: A,E
Explanation:
Option A is correct because SageMaker Pipelines is a service that enables you to create and manage automated workflows for your machine learning projects. You can use SageMaker Pipelines to orchestrate the steps of data extraction, model training, and model deployment in a repeatable and scalable way1.
Option B is correct because SageMaker Model Monitor is a service that monitors the quality of your models in production and alerts you when there are deviations in the model quality. You can use SageMaker Model Monitor to set an accuracy threshold for your model and configure a CloudWatch alarm that triggers when the threshold is exceeded. You can then connect the alarm to the workflow in SageMaker Pipelines to automatically initiate retraining and deployment of a new version of the model2.
Option C is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Creating a daily SageMaker Processing job that reads the predictions from Amazon S3 and checks for changes in model prediction accuracy is a manual and time-consuming process. It also requires you to write custom code to perform the data analysis and send the email notification. Moreover, it does not automatically retrain and deploy the model when the accuracy drops.
Option D is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Rerunning the steps in the Jupyter notebook that is hosted on SageMaker Studio notebooks to retrain the model and redeploy a new version of the model is a manual and error-prone process. It also requires you to monitor the model's performance and initiate the retraining and deployment steps yourself. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.
Option E is incorrect because it is not the most operationally efficient way to maintain the model's accuracy. Exporting the training and deployment code from the SageMaker Studio notebooks into a Python script and packaging the script into an Amazon ECS task that an AWS Lambda function can initiate is a complex and cumbersome process. It also requires you to manage the infrastructure and resources for the Amazon ECS task and the AWS Lambda function. Moreover, it does not leverage the benefits of SageMaker Pipelines and SageMaker Model Monitor to automate and streamline the workflow.
References:
1: SageMaker Pipelines - Amazon SageMaker
2: Monitor data and model quality - Amazon SageMaker
NEW QUESTION # 242
A company wants to classify user behavior as either fraudulent or normal. Based on internal research, a Machine Learning Specialist would like to build a binary classifier based on two features: age of account and transaction month. The class distribution for these features is illustrated in the figure provided.
Based on this information, which model would have the HIGHEST recall with respect to the fraudulent class?
- A. Decision tree
- B. Single Perceptron with sigmoidal activation function
- C. Naive Bayesian classifier
- D. Linear support vector machine (SVM)
Answer: C
NEW QUESTION # 243
A Data Scientist received a set of insurance records, each consisting of a record ID, the final outcome among 200 categories, and the date of the final outcome. Some partial information on claim contents is also provided, but only for a few of the 200 categories. For each outcome category, there are hundreds of records distributed over the past 3 years. The Data Scientist wants to predict how many claims to expect in each category from month to month, a few months in advance.
What type of machine learning model should be used?
- A. Forecasting using claim IDs and timestamps to identify how many claims in each category to expect from month to month.
- B. Classification month-to-month using supervised learning of the 200 categories based on claim contents.
- C. Reinforcement learning using claim IDs and timestamps where the agent will identify how many claims in each category to expect from month to month.
- D. Classification with supervised learning of the categories for which partial information on claim contents is provided, and forecasting using claim IDs and timestamps for all other categories.
Answer: A
Explanation:
Forecasting is a type of machine learning model that predicts future values of a target variable based on historical data and other features. Forecasting is suitable for problems that involve time-series data, such as the number of claims in each category from month to month. Forecasting can handle multiple categories of the target variable, as well as missing or partial information on some features. Therefore, option C is the best choice for the given problem.
Option A is incorrect because classification is a type of machine learning model that assigns a label to an input based on predefined categories. Classification is not suitable for predicting continuous or numerical values, such as the number of claims in each category from month to month. Moreover, classification requires sufficient and complete information on the features that are relevant to the target variable, which is not the case for the given problem. Option B is incorrect because reinforcement learning is a type of machine learning model that learns from its own actions and rewards in an interactive environment. Reinforcement learning is not suitable for problems that involve historical data and do not require an agent to take actions. Option D is incorrect because it combines two different types of machine learning models, which is unnecessary and inefficient. Moreover, classification is not suitable for predicting the number of claims in some categories, as explained in option A.
References:
Forecasting | AWS Solutions for Machine Learning (AI/ML) | AWS Solutions Library Time Series Forecasting Service - Amazon Forecast - Amazon Web Services Amazon Forecast: Guide to Predicting Future Outcomes - Onica Amazon Launches What-If Analyses for Machine Learning Forecasting ...
NEW QUESTION # 244
A Machine Learning Specialist is building a model that will perform time series forecasting using Amazon SageMaker. The Specialist has finished training the model and is now planning to perform load testing on the endpoint so they can configure Auto Scaling for the model variant.
Which approach will allow the Specialist to review the latency, memory utilization, and CPU utilization during the load test?
- A. Generate an Amazon CloudWatch dashboard to create a single view for the latency, memory utilization, and CPU utilization metrics that are outputted by Amazon SageMaker.
- B. Review SageMaker logs that have been written to Amazon S3 by leveraging Amazon Athena and Amazon QuickSight to visualize logs as they are being produced.
- C. Build custom Amazon CloudWatch Logs and then leverage Amazon ES and Kibana to query and visualize the log data as it is generated by Amazon SageMaker.
- D. Send Amazon CloudWatch Logs that were generated by Amazon SageMaker to Amazon ES and use Kibana to query and visualize the log data
Answer: A
Explanation:
https://docs.aws.amazon.com/sage ... ing-cloudwatch.html
NEW QUESTION # 245
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