NEW AWS-CERTIFIED-MACHINE-LEARNING-SPECIALTY TEST BRAINDUMPS | ACCURATE AWS CERTIFIED MACHINE LEARNING - SPECIALTY 100% FREE EXAM VCE

New AWS-Certified-Machine-Learning-Specialty Test Braindumps | Accurate AWS Certified Machine Learning - Specialty 100% Free Exam Vce

New AWS-Certified-Machine-Learning-Specialty Test Braindumps | Accurate AWS Certified Machine Learning - Specialty 100% Free Exam Vce

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The AWS Certified Machine Learning - Specialty exam is designed to test the skills and knowledge of professionals who work with machine learning technologies on the Amazon Web Services (AWS) platform. AWS Certified Machine Learning - Specialty certification is intended for individuals who have experience with data science, machine learning algorithms, and AWS services. AWS-Certified-Machine-Learning-Specialty Exam is designed to test candidates' ability to design, implement, deploy, and maintain machine learning solutions on AWS.

Amazon AWS Certified Machine Learning - Specialty Sample Questions (Q283-Q288):

NEW QUESTION # 283
A Data Scientist is developing a binary classifier to predict whether a patient has a particular disease on a series of test results. The Data Scientist has data on 400 patients randomly selected from the population. The disease is seen in 3% of the population.
Which cross-validation strategy should the Data Scientist adopt?

  • A. A stratified k-fold cross-validation strategy with k=5
  • B. An 80/20 stratified split between training and validation
  • C. A k-fold cross-validation strategy with k=5
  • D. A k-fold cross-validation strategy with k=5 and 3 repeats

Answer: A


NEW QUESTION # 284
A company is running a machine learning prediction service that generates 100 TB of predictions every day A Machine Learning Specialist must generate a visualization of the daily precision-recall curve from the predictions, and forward a read-only version to the Business team.
Which solution requires the LEAST coding effort?

  • A. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Visualize the arrays in Amazon QuickSight, and publish them in a dashboard shared with the Business team
  • B. Generate daily precision-recall data in Amazon QuickSight, and publish the results in a dashboard shared with the Business team
  • C. Generate daily precision-recall data in Amazon ES, and publish the results in a dashboard shared with the Business team.
  • D. Run a daily Amazon EMR workflow to generate precision-recall data, and save the results in Amazon S3 Give the Business team read-only access to S3

Answer: A


NEW QUESTION # 285
A company has raw user and transaction data stored in AmazonS3 a MySQL database, and Amazon RedShift A Data Scientist needs to perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon RedShift, and then calculating the average-of a few selected columns from the joined data Which AWS service should the Data Scientist use?

  • A. Amazon Redshift Spectrum
  • B. AWS Glue
  • C. Amazon QuickSight
  • D. Amazon Athena

Answer: D

Explanation:
Explanation
Amazon Athena is a serverless interactive query service that can analyze data in Amazon S3 using standard SQL. Amazon Athena can also query data from other sources, such as MySQL and Amazon Redshift, by using federated queries. Federated queries allow Amazon Athena to run SQL queries across data sources, such as relational and non-relational databases, data warehouses, and data lakes. By using Amazon Athena, the Data Scientist can perform an analysis by joining the three datasets from Amazon S3, MySQL, and Amazon Redshift, and then calculating the average of a few selected columns from the joined data. Amazon Athena can also integrate with other AWS services, such as AWS Glue and Amazon QuickSight, to provide additional features, such as data cataloging and visualization.
References:
What is Amazon Athena? - Amazon Athena
Federated Query Overview - Amazon Athena
Querying Data from Amazon S3 - Amazon Athena
Querying Data from MySQL - Amazon Athena
[Querying Data from Amazon Redshift - Amazon Athena]


NEW QUESTION # 286
A chemical company has developed several machine learning (ML) solutions to identify chemical process abnormalities. The time series values of independent variables and the labels are available for the past 2 years and are sufficient to accurately model the problem.
The regular operation label is marked as 0. The abnormal operation label is marked as 1 . Process abnormalities have a significant negative effect on the companys profits. The company must avoid these abnormalities.
Which metrics will indicate an ML solution that will provide the GREATEST probability of detecting an abnormality?

  • A. Precision = 0.7
    Recall = 0.9
  • B. Precision = 0.91
    Recall = 0.6
  • C. Precision = 0.61
    Recall = 0.98
  • D. Precision = 0.98
    Recall = 0.8

Answer: C

Explanation:
Explanation
The metrics that will indicate an ML solution that will provide the greatest probability of detecting an abnormality are precision and recall. Precision is the ratio of true positives (TP) to the total number of predicted positives (TP + FP), where FP is false positives. Recall is the ratio of true positives (TP) to the total number of actual positives (TP + FN), where FN is false negatives. A high precision means that the ML solution has a low rate of false alarms, while a high recall means that the ML solution has a high rate of true detections. For the chemical company, the goal is to avoid process abnormalities, which are marked as 1 in the labels. Therefore, the company needs an ML solution that has a high recall for the positive class, meaning that it can detect most of the abnormalities and minimize the false negatives. Among the four options, option B has the highest recall for the positive class, which is 0.98. This means that the ML solution can detect 98% of the abnormalities and miss only 2%. Option B also has a reasonable precision for the positive class, which is 0.61.
This means that the ML solution has a false alarm rate of 39%, which may be acceptable for the company, depending on the cost and benefit analysis. The other options have lower recall for the positive class, which means that they have higher false negative rates, which can be more detrimental for the company than false positive rates.
References:
1: AWS Certified Machine Learning - Specialty Exam Guide
2: AWS Training - Machine Learning on AWS
3: AWS Whitepaper - An Overview of Machine Learning on AWS
4: Precision and recall


NEW QUESTION # 287
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:
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 # 288
......

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