MLS-C01 日本語解説集、 Amazon MLS-C01 認定試験 & AWS Certified Machine Learning Specialty - Royalholidayclubbed

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Royalholidayclubbedの専門家チームが君の需要を満たすために自分の経験と知識を利用してAmazonのMLS-C01日本語解説集認定試験対策模擬テスト問題集が研究しました。模擬テスト問題集と真実の試験問題がよく似ています。一目でわかる最新の出題傾向でわかりやすい解説と充実の補充問題があります。もし合格しないと、われは全額で返金いたします。Royalholidayclubbedはずっと君のために最も正確なAmazonのMLS-C01日本語解説集「AWS Certified Machine Learning - Specialty」試験に関する資料を提供して、君が安心に選択することができます。 RoyalholidayclubbedのAmazonのMLS-C01日本語解説集の試験問題は同じシラバスに従って、実際のAmazonのMLS-C01日本語解説集認証試験にも従っています。

AmazonのMLS-C01日本語解説集試験は挑戦がある認定試験です。

RoyalholidayclubbedのAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty日本語解説集試験トレーニング資料は試験問題と解答を含まれて、豊富な経験を持っているIT業種の専門家が長年の研究を通じて作成したものです。それに、我々は一年間の無料更新サービスを提供します。失敗しましたら、当社は全額で返金して、あなたの利益を保障します。

我々の誠意を信じてください。あなたが順調に試験に合格するように。時間とお金の集まりより正しい方法がもっと大切です。

Amazon MLS-C01日本語解説集 - あなたもIT認証資格を取りたいですか。

世の中に去年の自分より今年の自分が優れていないのは立派な恥です。それで、IT人材として毎日自分を充実して、MLS-C01日本語解説集問題集を学ぶ必要があります。弊社のMLS-C01日本語解説集問題集はあなたにこのチャンスを全面的に与えられます。あなたは自分の望ましいAmazon MLS-C01日本語解説集問題集を選らんで、学びから更なる成長を求められます。心はもはや空しくなく、生活を美しくなります。

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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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