而且，萬一你用了考古題以後，考試還是失敗的話，Shobhadoshi保證全額退款。這樣一來，你還擔心什麼呢？Shobhadoshi對自己的資料有足夠的信心，你也要對Shobhadoshi有足夠的信心。為了你的考試能夠成功，千萬不要錯過Shobhadoshi這個網站。 只有這樣，在考試的時候你才可以輕鬆應對。怎麼樣？Shobhadoshi的資料可以讓你在準備考試時節省很多的時間。 另外，如果你是第一次參加考試，那麼你可以使用軟體版的考古題。

AWS Certified Specialty MLS-C01 你可以免費下載考古題的一部分。

AWS Certified Specialty MLS-C01參考資料 - AWS Certified Machine Learning - Specialty 使用了Shobhadoshi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。 Shobhadoshi可以幫助你實現這一願望。這裏有專業的知識，強大的考古題，優質的服務，可以讓你高速高效的掌握知識技能，在考試中輕鬆過關，讓自己更加接近成功之路。

保證大家通過MLS-C01參考資料認證考試，如果您失敗，可以享受 100%的退款保證。在我們網站，您可以先免費嘗試下載我們的題庫DEMO，體驗我們的Amazon MLS-C01參考資料考古題的品質，相信在您使用之后會很滿意我們的產品。成千上萬的IT考生通過我們的產品成功通過考試，該MLS-C01參考資料考古題的品質已被廣大考生檢驗。

Amazon MLS-C01參考資料 - Shobhadoshi提供的培訓資料將是你的最佳選擇。

為什麼大多數人選擇Shobhadoshi，是因為Shobhadoshi的普及帶來極大的方便和適用。是通過實踐檢驗了的，Shobhadoshi提供 Amazon的MLS-C01參考資料考試認證資料是眾所周知的，許多考生沒有信心贏得 Amazon的MLS-C01參考資料考試認證，擔心考不過，所以你得執行Shobhadoshi Amazon的MLS-C01參考資料的考試培訓資料，有了它，你會信心百倍，真正的作了考試準備。

每個需要通過IT考試認證的考生都知道，這次的認證關係著他們人生的重大轉變，我們Shobhadoshi提供的考試認證培訓資料是用超低的價格和高品質的擬真試題和答案來奉獻給廣大考生，我們的產品還具備成本效益，並提供了一年的免費更新期，我們認證培訓資料都是現成的。我們網站是答案轉儲的領先供應商，我們有你們需要的最新最準確的考試認證培訓資料，也就是答案和考題。

MLS-C01 PDF DEMO:

QUESTION NO: 1
A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?
A. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.
B. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
C. AWS Glue with a custom ETL script to transform the data.
D. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
Answer: B

QUESTION NO: 2
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: 3
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: 4
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: 5
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 Which approach will provide the information required for further analysis?
A. Use Amazon Comprehend with the transcribed files to build the key topics
B. Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics
C. Use Amazon Translate with the transcribed files to train and build a model for the key topics
D. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics
Answer: C

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Updated: May 28, 2022