今の競争の激しいのIT業界の中にAmazon MLS-C01日本語独学書籍認定試験に合格して、自分の社会地位を高めることができます。弊社のIT業で経験豊富な専門家たちが正確で、合理的なAmazon MLS-C01日本語独学書籍「AWS Certified Machine Learning - Specialty」認証問題集を作り上げました。 弊社の勉強の商品を選んで、多くの時間とエネルギーを節約こともできます。 ショートカットは一つしかないです。それはGoldmile-InfobizのAmazonのMLS-C01日本語独学書籍試験トレーニング資料を利用することです。 Goldmile-Infobizはまた一年間に無料なサービスを更新いたします。
AWS Certified Specialty MLS-C01 ふさわしい方式を選ぶのは一番重要なのです。
AWS Certified Specialty MLS-C01日本語独学書籍 - AWS Certified Machine Learning - Specialty もっと長い時間をもらって試験を準備したいのなら、あなたがいつでもサブスクリプションの期間を伸びることができます。 世界は変化している、我々はできるだけそのペースを維持する必要があります。我々Goldmile-InfobizはAmazonのMLS-C01 再テスト試験の変化を注目しています。
速く最新のGoldmile-InfobizのAmazonのMLS-C01日本語独学書籍トレーニング資料を取りに行きましょう。現在、IT業界での激しい競争に直面しているあなたは、無力に感じるでしょう。これは避けられないことですから、あなたがしなければならないことは、自分のキャリアを護衛するのです。
Amazon MLS-C01日本語独学書籍 - ここで成功へのショートカットを教えてあげます。
ローマは一日に建てられませんでした。多くの人にとって、短い時間でMLS-C01日本語独学書籍試験に合格できることは難しいです。しかし、幸いにして、MLS-C01日本語独学書籍の練習問題の専門会社として、弊社の最も正確な質問と回答を含むMLS-C01日本語独学書籍試験の資料は、MLS-C01日本語独学書籍試験対する問題を効果的に解決できます。MLS-C01日本語独学書籍練習問題をちゃんと覚えると、MLS-C01日本語独学書籍に合格できます。あなたはMLS-C01日本語独学書籍練習問題を選ばれば、試験に合格できますよ！
AmazonのMLS-C01日本語独学書籍認定試験と言ったら、人々は迷っています。異なる考えがありますが、要約は試験が大変難しいことです。
MLS-C01 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 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: 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 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
QUESTION NO: 5
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
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Updated: May 28, 2022