それで、IT人材として毎日自分を充実して、MLS-C01テストサンプル問題問題集を学ぶ必要があります。弊社のMLS-C01テストサンプル問題問題集はあなたにこのチャンスを全面的に与えられます。あなたは自分の望ましいAmazon MLS-C01テストサンプル問題問題集を選らんで、学びから更なる成長を求められます。 試験問題集が更新されると、Omgzlookは直ちにあなたのメールボックスにMLS-C01テストサンプル問題問題集の最新版を送ります。あなたは試験の最新バージョンを提供することを要求することもできます。 また、MLS-C01テストサンプル問題問題集に疑問があると、メールで問い合わせてください。
AWS Certified Specialty MLS-C01 弊社の商品が好きなのは弊社のたのしいです。
AWS Certified Specialty MLS-C01テストサンプル問題 - AWS Certified Machine Learning - Specialty IT業種の人たちは自分のIT夢を持っているのを信じています。 Omgzlook を選択して１００％の合格率を確保することができて、もし試験に失敗したら、Omgzlookが全額で返金いたします。
我々Omgzlookのあなたに開発するAmazonのMLS-C01テストサンプル問題ソフトはあなたの問題を解決することができます。最初の保障はあなたに安心させる高い通過率で、第二の保護手段は、あなたは弊社のソフトを利用してAmazonのMLS-C01テストサンプル問題試験に合格しないなら、我々はあなたのすべての支払を払い戻します。あなたが安心で試験のために準備すればいいです。
Amazon MLS-C01テストサンプル問題 - Omgzlookを選んだら、成功への扉を開きます。
数年以来の整理と分析によって開発されたMLS-C01テストサンプル問題問題集は権威的で全面的です。MLS-C01テストサンプル問題問題集を利用して試験に合格できます。この問題集の合格率は高いので、多くのお客様からMLS-C01テストサンプル問題問題集への好評をもらいました。MLS-C01テストサンプル問題問題集のカーバー率が高いので、勉強した問題は試験に出ることが多いです。だから、弊社の提供するMLS-C01テストサンプル問題問題集を暗記すれば、きっと試験に合格できます。
問題が更新される限り、Omgzlookは直ちに最新版のMLS-C01テストサンプル問題資料を送ってあげます。そうすると、あなたがいつでも最新バージョンの資料を持っていることが保証されます。
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 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: 4
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: 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