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利用Goldmile-Infobiz提供的資料通過Amazon AWS-Certified-Machine-Learning-Specialty - AWS Certified Machine Learning - Specialty最新試題 認證考試是不成問題的，而且你可以以很高的分數通過考試得到相關認證。 學歷不等於實力，更不等於能力，學歷只是代表你有這個學習經歷而已，而真正的能力是在實踐中鍛煉出來的，與學歷並沒有必然聯繫。不要覺得自己能力不行，更不要懷疑自己，當你選擇了Amazon的新版 AWS-Certified-Machine-Learning-Specialty 題庫考試認證，就要努力通過，如果你擔心考不過，你可以選擇Goldmile-Infobiz Amazon的新版 AWS-Certified-Machine-Learning-Specialty 題庫考試培訓資料，不管你學歷有多高，你能力有多低，你都可以很容易的理解這個培訓資料的內容，並且可以順利的通過考試認證。
而且我們的Goldmile-Infobiz是眾多類似網站中最能給你保障的一個網站，選擇Goldmile-Infobiz就等於選擇了成功。為通過Amazon AWS-Certified-Machine-Learning-Specialty最新試題 認證考試花大量的時間和精力復習相關知識，但是卻是冒險地通過考試。選擇Goldmile-Infobiz的產品卻可以讓你花少量的錢，一次性安全通過考試。
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要想通過Amazon AWS-Certified-Machine-Learning-Specialty最新試題考試認證，選擇相應的培訓工具是非常有必要的，而關於Amazon AWS-Certified-Machine-Learning-Specialty最新試題考試認證的研究材料是很重要的一部分，而我們Goldmile-Infobiz能很有效的提供關於通過Amazon AWS-Certified-Machine-Learning-Specialty最新試題考試認證的資料，Goldmile-Infobiz的IT專家個個都是實力加經驗組成的，他們的研究出來的材料和你真實的考題很接近，幾乎一樣，Goldmile-Infobiz是專門為要參加認證考試的人提供便利的網站，能有效的幫助考生通過考試。
Goldmile-Infobiz的AWS-Certified-Machine-Learning-Specialty最新試題考古題和實際的認證考試一樣，不僅包含了實際考試中的所有問題，而且考古題的軟體版完全類比了真實考試的氛圍。使用了Goldmile-Infobiz的考古題，你在參加考試時完全可以應付自如，輕鬆地獲得高分。
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