MLS-C01関連復習問題集 & MLS-C01認定資格試験 - Amazon MLS-C01シュミレーション問題集 - Royalholidayclubbed

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もし君はAmazonのMLS-C01関連復習問題集認定試験に合格するのを通じて、競争が激しいIT業種での地位を高めて、IT技能を増強するなら、Royalholidayclubbedの AmazonのMLS-C01関連復習問題集試験トレーニング資料を選んだほうがいいです。長年の努力を通じて、RoyalholidayclubbedのAmazonのMLS-C01関連復習問題集認定試験の合格率が１００パーセントになっていました。Royalholidayclubbedを選ぶのは成功を選ぶのに等しいです。 RoyalholidayclubbedのAmazonのMLS-C01関連復習問題集トレーニング資料即ち問題と解答をダウンロードする限り、気楽に試験に受かることができるようになります。まだ困っていたら、我々の試用版を使ってみてください。常々、時間とお金ばかり効果がないです。

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Amazon MLS-C01関連復習問題集復習教材は有効的な資料です。

我々の承諾だけでなく、お客様に最も全面的で最高のサービスを提供します。AmazonのMLS-C01関連復習問題集の購入の前にあなたの無料の試しから、購入の後での一年間の無料更新まで我々はあなたのAmazonのMLS-C01関連復習問題集試験に一番信頼できるヘルプを提供します。AmazonのMLS-C01関連復習問題集試験に失敗しても、我々はあなたの経済損失を減少するために全額で返金します。

あなたはその他のAmazon MLS-C01関連復習問題集「AWS Certified Machine Learning - Specialty」認証試験に関するツールサイトでも見るかも知れませんが、弊社はIT業界の中で重要な地位があって、Royalholidayclubbedの問題集は君に100%で合格させることと君のキャリアに変らせることだけでなく一年間中で無料でサービスを提供することもできます。

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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