試験の目標が変わる限り、あるいは我々の勉強資料が変わる限り、すぐに更新して差し上げます。あなたのニーズをよく知っていていますから、あなたに試験に合格する自信を与えます。NewValidDumpsのAmazonのMLS-C01関連資格知識試験トレーニング資料を手に入れたら、我々は一年間の無料更新サービスを提供します。 ただ短時間の勉強で試験に合格できる最新のMLS-C01関連資格知識問題集が登場しました。この素晴らしい問題集はNewValidDumpsによって提供されます。 NewValidDumpsを選んだら、成功への扉を開きます。

AWS Certified Specialty MLS-C01 自分の幸せは自分で作るものだと思われます。

試用した後、我々のMLS-C01 - AWS Certified Machine Learning - Specialty関連資格知識問題集はあなたを試験に順調に合格させると信じられます。 あなたは弊社の高品質Amazon　MLS-C01 日本語版試験解答試験資料を利用して、一回に試験に合格します。NewValidDumpsのAmazon　MLS-C01 日本語版試験解答問題集は専門家たちが数年間で過去のデータから分析して作成されて、試験にカバーする範囲は広くて、受験生の皆様のお金と時間を節約します。

IT領域により良く発展したいなら、Amazon　MLS-C01関連資格知識のような試験認定資格を取得するのは重要なことです。周知のようにAmazon　MLS-C01関連資格知識のような試験認定資格を手に入れると、会社の規則に沿う奨励があります。それで、速く我々NewValidDumpsのAmazon　MLS-C01関連資格知識試験問題集を入手しましょう。

また、Amazon MLS-C01関連資格知識問題集は的中率が高いです。

多分、MLS-C01関連資格知識テスト質問の数が伝統的な問題の数倍である。Amazon　MLS-C01関連資格知識試験参考書は全ての知識を含めて、全面的です。そして、MLS-C01関連資格知識試験参考書の問題は本当の試験問題とだいたい同じことであるとわかります。MLS-C01関連資格知識試験参考書があれば,ほかの試験参考書を勉強する必要がないです。

簡単で順調にAmazon MLS-C01関連資格知識認定試験を通すのは問題になりますが、NewValidDumpsはこの問題を解決できるよ。弊社の問題集はIT技術者がこつこつ研究して、正確で最新なもので君のMLS-C01関連資格知識認定試験を簡単に通すことにいいトレーニングツールになりますよ。

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