NewValidDumpsはあなたと苦楽を共にして、一緒に挑戦に直面します。あなたはIT職員ですか。成功したいのですか。 Amazon MLS-C01試験感想「AWS Certified Machine Learning - Specialty」認証試験に合格することが簡単ではなくて、Amazon MLS-C01試験感想証明書は君にとってはIT業界に入るの一つの手づるになるかもしれません。しかし必ずしも大量の時間とエネルギーで復習しなくて、弊社が丹精にできあがった問題集を使って、試験なんて問題ではありません。 AmazonのMLS-C01試験感想試験に受かるのは実際にそんなに難しいことではないです。

AWS Certified Specialty MLS-C01 では、どうしたらいいでしょうか。

それはコストパフォーマンスが非常に高い資料ですから、もしあなたも私と同じIT夢を持っていたら、NewValidDumpsのAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty試験感想試験トレーニング資料を利用してください。 はAmazonのMLS-C01 復習時間試験に合格するのに、私たちは最も早い時間で合格するのを追求します。

きっと望んでいるでしょう。では、常に自分自身をアップグレードする必要があります。IT業種で仕事しているあなたは、夢を達成するためにどんな方法を利用するつもりですか。

Amazon MLS-C01試験感想 - 弊社の開発したソフトは非常に全面的です。

NewValidDumpsのAmazonのMLS-C01試験感想試験トレーニング資料は豊富な経験を持っているIT専門家が研究したものです。君がAmazonのMLS-C01試験感想問題集を購入したら、私たちは一年間で無料更新サービスを提供することができます。もしAmazonのMLS-C01試験感想問題集は問題があれば、或いは試験に不合格になる場合は、全額返金することを保証いたします。

今日、我々があなたに提供するAmazonのMLS-C01試験感想ソフトは多くの受験生に検査されました。彼らにAmazonのMLS-C01試験感想試験に合格させました。

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