受験生は問題を選べ、テストの時間もコントロールできます。Goldmile-Infobizというサイトで、あなたはストレスと不安なく試験の準備をすることができますから、一般的な間違いを避けられます。そうしたら、あなたは自信を得ることができて、実際の試験で経験を活かして気楽に合格します。 世界は変化している、我々はできるだけそのペースを維持する必要があります。我々Goldmile-InfobizはAmazonのMLS-C01認定資格試験問題集試験の変化を注目しています。 短い時間に最も小さな努力で一番効果的にAmazonのMLS-C01認定資格試験問題集試験の準備をしたいのなら、Goldmile-InfobizのAmazonのMLS-C01認定資格試験問題集試験トレーニング資料を利用することができます。
多くの人はAmazonのMLS-C01認定資格試験問題集試験への準備に悩んでいます。
Goldmile-Infobiz のAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty認定資格試験問題集試験資料はあなたに時間を節約させることができるだけではなく、あなたに首尾よく試験に合格させることもできますから、Goldmile-Infobizを選ばない理由はないです。 試験を怖く感じるのはかなり正常です。特にAmazonのMLS-C01 関連受験参考書のような難しい試験です。
AmazonのMLS-C01認定資格試験問題集試験は挑戦がある認定試験です。現在、書籍の以外にインターネットは知識の宝庫として見られています。Goldmile-Infobiz で、あなたにあなたの宝庫を見つけられます。
Amazon MLS-C01認定資格試験問題集 - 人の職業の発展は彼の能力によって進めます。
逆境は人をテストすることができます。困難に直面するとき、勇敢な人だけはのんびりできます。あなたは勇敢な人ですか。もしIT認証の準備をしなかったら、あなたはのんびりできますか。もちろんです。 Goldmile-InfobizのAmazonのMLS-C01認定資格試験問題集試験トレーニング資料を持っていますから、どんなに難しい試験でも成功することができます。
これなので、今から我々社Goldmile-InfobizのMLS-C01認定資格試験問題集試験に合格するのに努力していきます。弊社のAmazonのMLS-C01認定資格試験問題集真題によって、資格認定証明書を受け取れて、仕事の昇進を実現できます。
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 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: 3
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: 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