多くの時間と労力をかかってAmazonのMLS-C01トレーニング資料認定試験に合格するを冒険にすると代わりNewValidDumpsが提供した問題集を利用してわずか一度お金かかって合格するのは価値があるでしょう。今の社会の中で時間がそんなに重要で最も保障できるNewValidDumpsを選ばましょう。 AmazonのMLS-C01トレーニング資料試験に合格することは容易なことではなくて、良い訓練ツールは成功の保証でNewValidDumpsは君の試験の問題を準備してしまいました。君の初めての合格を目標にします。 NewValidDumpsが短期な訓練を提供し、一回に君の試験に合格させることができます。

AWS Certified Specialty MLS-C01 近年、IT領域で競争がますます激しくなります。

AmazonのMLS-C01 - AWS Certified Machine Learning - Specialtyトレーニング資料認定試験に関する研究資料が重要な一部です。 NewValidDumpsのAmazonのMLS-C01 最新問題試験トレーニング資料は豊富な知識と経験を持っているIT専門家に研究された成果で、正確度がとても高いです。NewValidDumpsに会ったら、最高のトレーニング資料を見つけました。

それもほとんどの受験生はNewValidDumpsを選んだ理由です。NewValidDumpsはいつまでも受験生のニーズに注目していて、できるだけ皆様のニーズを満たします。NewValidDumpsのAmazonのMLS-C01トレーニング資料試験トレーニング資料は今までがないIT認証のトレーニング資料ですから、NewValidDumpsを利用したら、あなたのキャリアは順調に進むことができるようになります。

Amazon MLS-C01トレーニング資料 - 進歩を勇敢に追及する人生こそ素晴らしい人生です。

MLS-C01トレーニング資料認定試験に合格することは難しいようですね。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トレーニング資料試験に合格するショートカットを教えてあげますから。あなたを試験に一発合格させる素晴らしいMLS-C01トレーニング資料試験に関連する参考書が登場しますよ。それはNewValidDumpsのMLS-C01トレーニング資料問題集です。気楽に試験に合格したければ、はやく試しに来てください。

NewValidDumpsは試験に失敗すれば全額返金を保証します。このような保証があれば、NewValidDumpsのMLS-C01トレーニング資料問題集を購入しようか購入するまいかと躊躇する必要は全くないです。

MLS-C01 PDF DEMO:

QUESTION NO: 1
A Machine Learning Specialist kicks off a hyperparameter tuning job for a tree-based ensemble model using Amazon SageMaker with Area Under the ROC Curve (AUC) as the objective metric This workflow will eventually be deployed in a pipeline that retrains and tunes hyperparameters each night to model click-through on data that goes stale every 24 hours With the goal of decreasing the amount of time it takes to train these models, and ultimately to decrease costs, the Specialist wants to reconfigure the input hyperparameter range(s) Which visualization will accomplish this?
A. A scatter plot with points colored by target variable that uses (-Distributed Stochastic Neighbor
Embedding (I-SNE) to visualize the large number of input variables in an easier-to-read dimension.
B. A scatter plot showing (he performance of the objective metric over each training iteration
C. A histogram showing whether the most important input feature is Gaussian.
D. A scatter plot showing the correlation between maximum tree depth and the objective metric.
Answer: A

QUESTION NO: 2
A Machine Learning Specialist receives customer data for an online shopping website. The data includes demographics, past visits, and locality information. The Specialist must develop a machine learning approach to identify the customer shopping patterns, preferences and trends to enhance the website for better service and smart recommendations.
Which solution should the Specialist recommend?
A. A neural network with a minimum of three layers and random initial weights to identify patterns in the customer database
B. Random Cut Forest (RCF) over random subsamples to identify patterns in the customer database
C. Latent Dirichlet Allocation (LDA) for the given collection of discrete data to identify patterns in the customer database.
D. Collaborative filtering based on user interactions and correlations to identify patterns in the customer database
Answer: D

QUESTION NO: 3
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

QUESTION NO: 4
A Machine Learning Specialist is using Amazon SageMaker to host a model for a highly available customer-facing application .
The Specialist has trained a new version of the model, validated it with historical data, and now wants to deploy it to production To limit any risk of a negative customer experience, the Specialist wants to be able to monitor the model and roll it back, if needed What is the SIMPLEST approach with the LEAST risk to deploy the model and roll it back, if needed?
A. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by using a load balancer Revert traffic to the last version if the model does not perform as expected.
B. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 5% of the traffic to the new variant. Revert traffic to the last version by resetting the weights if the model does not perform as expected.
C. Update the existing SageMaker endpoint to use a new configuration that is weighted to send 100% of the traffic to the new variant Revert traffic to the last version by resetting the weights if the model does not perform as expected.
D. Create a SageMaker endpoint and configuration for the new model version. Redirect production traffic to the new endpoint by updating the client configuration. Revert traffic to the last version if the model does not perform as expected.
Answer: D

QUESTION NO: 5
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

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Updated: May 28, 2022