在IT領域更是這樣。隨著電腦的普及，已經幾乎沒有不會使用電腦的人了。同樣在IT行業工作的你難道沒有感覺到壓力嗎？不管你的學歷有多高都不能代表你的實力。 但是通過Amazon MLS-C01題庫更新資訊 認證考試不是很容易的，需要花很多時間和精力掌握好相關專業知識。Shobhadoshi是一個制訂Amazon MLS-C01題庫更新資訊 認證考試培訓方案的專業IT培訓網站。 機會是留給有準備的人的，希望你不要錯失良機。

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Shobhadoshi始終致力于為客戶提供高品質的學習資料，來提高考生一次性通過Amazon MLS-C01 - AWS Certified Machine Learning - Specialty題庫更新資訊考試的概率，這是考生獲取認證最佳捷徑。 我相信你對我們的產品將會很有信心。如果你選擇使用Shobhadoshi的產品，Shobhadoshi可以幫助你100%通過你的一次參加的Amazon MLS-C01 考試備考經驗 認證考試。

您準備好Amazon MLS-C01題庫更新資訊考試嗎？是否了解最新的認證考試資訊呢？無論是您需要準備什么IT認證考試，Shobhadoshi都能幫助您成功通過首次严格的考试。針對MLS-C01題庫更新資訊認證考試，我們專業的IT講師研究出最適合考試使用的Amazon MLS-C01題庫更新資訊考古題資料，包括當前最新的考題題目。在我們網站，您可以享受100%安全的購物體驗，對于購買MLS-C01題庫更新資訊考古題的客戶，我們還提供一年的免費線上更新服務，一年之內，如果您購買的產品更新了，我們會免費發送你更新版本的MLS-C01題庫更新資訊考古題。

Amazon MLS-C01題庫更新資訊 - ”這是來自安西教練的一句大家都熟知的名言。

只為成功找方法，不為失敗找藉口。想要通過Amazon的MLS-C01題庫更新資訊考試認證其實也沒有那麼難，關鍵在於你用什麼樣的方式方法。選擇Shobhadoshi Amazon的MLS-C01題庫更新資訊考試培訓資料是個不錯選擇，它會幫助我們順利通過考試，這也是通往成功的最佳捷徑，每個人都有可能成功，關鍵在於選擇。

這個考試的認證資格可以證明你擁有很高的技能。但是，和考試的重要性一樣，這個考試也是非常難的。

MLS-C01 PDF DEMO:

QUESTION NO: 1
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: 2
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: 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