MLS-C01問題トレーリング、MLS-C01絶対合格 - Amazon MLS-C01基礎問題集 - Royalholidayclubbed

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試験を申し込みたいあなたは、いまどうやって試験に準備すべきなのかで悩んでいますか。そうだったら、下記のものを読んでください。いまMLS-C01問題トレーリング試験に合格するショートカットを教えてあげますから。あなたの希望はRoyalholidayclubbedのAmazonのMLS-C01問題トレーリング試験トレーニング資料にありますから、速く掴みましょう。「私はだめです。ここには、私たちは君の需要に応じます。

AWS Certified Specialty MLS-C01 はやく試してください。

AWS Certified Specialty MLS-C01問題トレーリング - AWS Certified Machine Learning - Specialty 我々の誠意を信じてください。私は教えてあげますよ。RoyalholidayclubbedのMLS-C01 認定内容問題集が一番頼もしい資料です。

自分のIT業界での発展を希望したら、AmazonのMLS-C01問題トレーリング試験に合格する必要があります。AmazonのMLS-C01問題トレーリング試験はいくつ難しくても文句を言わないで、我々Royalholidayclubbedの提供する資料を通して、あなたはAmazonのMLS-C01問題トレーリング試験に合格することができます。AmazonのMLS-C01問題トレーリング試験を準備しているあなたに試験に合格させるために、我々Royalholidayclubbedは模擬試験ソフトを更新し続けています。

Amazon MLS-C01問題トレーリング - 弊社の商品はあなたの圧力を減少できます。

あなたはMLS-C01問題トレーリング試験に不安を持っていますか？MLS-C01問題トレーリング参考資料をご覧下さい。私たちのMLS-C01問題トレーリング参考資料は十年以上にわたり、専門家が何度も練習して、作られました。あなたに高品質で、全面的なMLS-C01問題トレーリング参考資料を提供することは私たちの責任です。私たちより、MLS-C01問題トレーリング試験を知る人はいません。

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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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