我們Goldmile-Infobiz的IT認證考題擁有多年的培訓經驗，Goldmile-Infobiz Amazon的AWS-Certified-Machine-Learning-Specialty考試大綱考試培訓資料是個值得信賴的產品，我們的IT精英團隊不斷為廣大考生提供最新版的AWS-Certified-Machine-Learning-Specialty考試大綱考試培訓資料，我們的工作人員作出了巨大努力，以確保你們在考試中總是取得好成績，可以肯定的是，Goldmile-Infobiz Amazon的AWS-Certified-Machine-Learning-Specialty考試大綱考試材料是為你提供最實際的IT認證材料。 Amazon的AWS-Certified-Machine-Learning-Specialty考試大綱考試認證肯定會導致你有更好的職業前景，通過Amazon的AWS-Certified-Machine-Learning-Specialty考試大綱考試認證不僅驗證你的技能，也證明你的證書和專業知識，Goldmile-Infobiz Amazon的AWS-Certified-Machine-Learning-Specialty考試大綱考試培訓資料是實踐檢驗的軟體，有了它你會得到的理解理論比以前任何時候都要好，將是和你最配備知識。在你決定購買之前，你可以嘗試一個免費的使用版本，這樣一來你就知道Goldmile-Infobiz Amazon的AWS-Certified-Machine-Learning-Specialty考試大綱考試培訓資料的品質，也是你最佳的選擇。 通過Amazon的AWS-Certified-Machine-Learning-Specialty考試大綱考試認證是從事IT行業的人的夢想，如果你想要變夢想為現實，你只需要選擇專業的培訓，Goldmile-Infobiz就是一個專業的提供IT認證培訓資料的網站之一，選擇Goldmile-Infobiz，它將與你同在，確保你成功，無論追求的是否有所增加，我們Goldmile-Infobiz回讓你的夢想變成現實。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty 这样在考试时你就可以轻松自如地应对了。
AWS Certified Machine Learning AWS-Certified-Machine-Learning-Specialty考試大綱 - AWS Certified Machine Learning - Specialty 它可以讓你得到事半功倍的結果。 或者你也可以選擇為你免費更新考試考古題。有了這樣的保障，實在沒有必要擔心了。
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但是報名參加Amazon AWS-Certified-Machine-Learning-Specialty考試大綱 認證考試是個明智的選擇，因為在如今競爭激烈的IT行業應該要不斷的提升自己。但是您可以選擇很多方式幫你通過考試。
AWS-Certified-Machine-Learning-Specialty PDF DEMO:
QUESTION NO: 1
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s) Which visualization will accomplish this?
A. A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor
Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.
B. A scatter plot showing (he performance of the objective metric over each training iteration
C. A histogram showing whether the most important input feature is Gaussian.
D. A scatter plot showing the correlation between maximum tree depth and the objective metric.
Answer: A
QUESTION NO: 2
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. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database
B. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database
C. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
D. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database
Answer: D
QUESTION NO: 3
A Machine Learning Specialist has created a deep learning neural network model that performs well on the training data but performs poorly on the test data.
Which of the following methods should the Specialist consider using to correct this? (Select THREE.)
A. Decrease dropout.
B. Increase regularization.
C. Increase feature combinations.
D. Decrease feature combinations.
E. Decrease regularization.
F. Increase dropout.
Answer: A,B,C
QUESTION NO: 4
A Machine Learning Specialist is using Amazon SageMaker to host a model for a highly available customer-facing application .
The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?
A. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.
B. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model does not perform as expected.
C. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.
D. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by updating the client configuration. Revert traffic to the last version if the model does not perform as expected.
Answer: D
QUESTION NO: 5
A Machine Learning Specialist working for an online fashion company wants to build a data ingestion solution for the company's Amazon S3-based data lake.
The Specialist wants to create a set of ingestion mechanisms that will enable future capabilities comprised of:
* Real-time analytics
* Interactive analytics of historical data
* Clickstream analytics
* Product recommendations
Which services should the Specialist use?
A. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data
Analytics for historical data insights; Amazon DynamoDB streams for clickstream analytics; AWS Glue to generate personalized product recommendations
B. AWS Glue as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for historical data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
C. AWS Glue as the data dialog; Amazon Kinesis Data Streams and Amazon Kinesis Data Analytics for real-time data insights; Amazon Kinesis Data Firehose for delivery to Amazon ES for clickstream analytics; Amazon EMR to generate personalized product recommendations
D. Amazon Athena as the data catalog; Amazon Kinesis Data Streams and Amazon Kinesis Data
Analytics for near-realtime data insights; Amazon Kinesis Data Firehose for clickstream analytics; AWS
Glue to generate personalized product recommendations
Answer: C
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Updated: May 28, 2022