こうして、弊社の商品はどのくらいあなたの力になるのはよく分かっています。Goldmile-InfobizはAmazon MLS-C01模擬問題集認証試験を助けって通じての最良の選択で、１００％のAmazon MLS-C01模擬問題集認証試験合格率のはGoldmile-Infobiz最高の保証でございます。君が選んだのはGoldmile-Infobiz、成功を選択したのに等しいです。 Goldmile-Infobizは認定で優秀なIT資料のウエブサイトで、ここでAmazon MLS-C01模擬問題集認定試験の先輩の経験と暦年の試験の材料を見つけることができるとともに部分の最新の試験の題目と詳しい回答を無料にダウンロードこともできますよ。弊社のIT技術専門家たち は質が高い問題集と答えを提供し、お客様が合格できるように努めています。 Goldmile-Infobizは頼りが強い上にサービスもよくて、もし試験に失敗したら全額で返金いたしてまた一年の無料なアップデートいたします。
AWS Certified Specialty MLS-C01 逆境は人をテストすることができます。
AWS Certified Specialty MLS-C01模擬問題集 - AWS Certified Machine Learning - Specialty これは一般的に認められている最高級の認証で、あなたのキャリアにヘルプを与えられます。 自分のレベルを高めたいですか。では、仕事に役に立つスキルをもっと身に付けましょう。
IT職員としてのあなたは切迫感を感じましたか。Goldmile-Infobizを選んだら、成功への扉を開きます。頑張ってください。
Amazon MLS-C01模擬問題集 - いかがですか。
ここで私は明確にしたいのはGoldmile-InfobizのMLS-C01模擬問題集問題集の核心価値です。Goldmile-Infobizの問題集は100％の合格率を持っています。Goldmile-InfobizのMLS-C01模擬問題集問題集は多くのIT専門家の数年の経験の結晶で、高い価値を持っています。そのMLS-C01模擬問題集参考資料はIT認定試験の準備に使用することができるだけでなく、自分のスキルを向上させるためのツールとして使えることもできます。そのほか、もし試験に関連する知識をより多く知りたいなら、それもあなたの望みを満たすことができます。
それに、より効率的に試験の準備をするために、Goldmile-InfobizのMLS-C01模擬問題集試験問題集を選択したほうがいいです。それはあなたが試験に合格する最善の方法です。
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 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 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
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Updated: May 28, 2022