Goldmile-InfobizはAmazonのMLS-C01資格関連題「AWS Certified Machine Learning - Specialty」試験に関する完全な資料を唯一のサービスを提供するサイトでございます。Goldmile-Infobizが提供した問題集を利用してAmazonのMLS-C01資格関連題試験は全然問題にならなくて、高い点数で合格できます。Amazon MLS-C01資格関連題試験の合格のために、Goldmile-Infobizを選択してください。 Goldmile-Infobizが提供するAmazonのMLS-C01資格関連題認証試験問題集が君の試験に合格させます。テストの時に有効なツルが必要でございます。 弊社の商品は試験の範囲を広くカバーすることが他のサイトがなかなか及ばならないです。
AWS Certified Specialty MLS-C01 ショートカットは一つしかないです。
AWS Certified Specialty MLS-C01資格関連題 - AWS Certified Machine Learning - Specialty あなたと同じIT認定試験を受験する周りの人を見てください。 Goldmile-Infobizは現在の実績を持っているのは受験生の皆さんによって実践を通して得られた結果です。真実かつ信頼性の高いものだからこそ、Goldmile-Infobizの試験参考書は長い時間にわたってますます人気があるようになっています。
それはもちろんGoldmile-InfobizのMLS-C01資格関連題問題集ですよ。MLS-C01資格関連題試験の準備をするとき、がむしゃらにITに関連する知識を学ぶのは望ましくない勉強法です。実際は試験に合格するコツがあるのですよ。
Amazon MLS-C01資格関連題 - このサービスは無料なのです。
我々Goldmile-Infobizが数年以来商品の開発をしている目的はIT業界でよく発展したい人にAmazonのMLS-C01資格関連題試験に合格させることです。AmazonのMLS-C01資格関連題試験のための資料がたくさんありますが、Goldmile-Infobizの提供するのは一番信頼できます。我々の提供するソフトを利用する人のほとんどは順調にAmazonのMLS-C01資格関連題試験に合格しました。その中の一部は暇な時間だけでAmazonのMLS-C01資格関連題試験を準備します。
これなので、今から我々社Goldmile-InfobizのMLS-C01資格関連題試験に合格するのに努力していきます。弊社のAmazonのMLS-C01資格関連題真題によって、資格認定証明書を受け取れて、仕事の昇進を実現できます。
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 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