MLS-C01日本語版参考資料、MLS-C01的中率 - Amazon MLS-C01受験料過去問 - Royalholidayclubbed

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AWS Certified Specialty MLS-C01 この試験に受かるのは難しいですが、大丈夫です。

AWS Certified Specialty MLS-C01日本語版参考資料 - AWS Certified Machine Learning - Specialty この試験に合格すれば君の専門知識がとても強いを証明し得ます。 Royalholidayclubbedに会ったら、最高のトレーニング資料を見つけました。RoyalholidayclubbedのAmazonのMLS-C01 合格資料試験トレーニング資料を持っていたら、試験に対する充分の準備がありますから、安心に利用したください。

きみはAmazonのMLS-C01日本語版参考資料認定テストに合格するためにたくさんのルートを選択肢があります。Royalholidayclubbedは君のために良い訓練ツールを提供し、君のAmazon認証試に高品質の参考資料を提供しいたします。あなたの全部な需要を満たすためにいつも頑張ります。

Amazon MLS-C01日本語版参考資料 - まだ何を待っているのでしょうか？

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激変なネット情報時代で、質の良いAmazonのMLS-C01日本語版参考資料問題集を見つけるために、あなたは悩むことがありませんか。私たちは君がRoyalholidayclubbedを選ぶことと正確性の高いAmazonのMLS-C01日本語版参考資料問題集を祝っています。

MLS-C01 PDF DEMO:

QUESTION NO: 1
A Marketing Manager at a pet insurance company plans to launch a targeted marketing campaign on social media to acquire new customers Currently, the company has the following data in
Amazon Aurora
* Profiles for all past and existing customers
* Profiles for all past and existing insured pets
* Policy-level information
* Premiums received
* Claims paid
What steps should be taken to implement a machine learning model to identify potential new customers on social media?
A. Use a decision tree classifier engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
B. Use a recommendation engine on customer profile data to understand key characteristics of consumer segments. Find similar profiles on social media
C. Use regression on customer profile data to understand key characteristics of consumer segments
Find similar profiles on social media.
D. Use clustering on customer profile data to understand key characteristics of consumer segments
Find similar profiles on social media.
Answer: B

QUESTION NO: 2
A Machine Learning Specialist is building a logistic regression model that will predict whether or not a person will order a pizza. The Specialist is trying to build the optimal model with an ideal classification threshold.
What model evaluation technique should the Specialist use to understand how different classification thresholds will impact the model's performance?
A. Receiver operating characteristic (ROC) curve
B. Misclassification rate
C. Root Mean Square Error (RM&)
D. L1 norm
Answer: A

QUESTION NO: 3
A Machine Learning Specialist built an image classification deep learning model. However the
Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and
75%r respectively.
How should the Specialist address this issue and what is the reason behind it?
A. The learning rate should be increased because the optimization process was trapped at a local minimum.
B. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
C. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
D. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
Answer: C

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 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

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Updated: May 28, 2022

by RHC.

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