現在IT技術会社に通勤しているあなたは、AmazonのMLS-C01対策学習試験認定を取得しましたか？MLS-C01対策学習試験認定は給料の増加とジョブのプロモーションに役立ちます。短時間でMLS-C01対策学習試験に一発合格したいなら、我々社のAmazonのMLS-C01対策学習資料を参考しましょう。また、MLS-C01対策学習問題集に疑問があると、メールで問い合わせてください。 お客様の満足は我々の進む力です。我々社のAmazon　MLS-C01対策学習問題集を使用して試験に合格しないで全額での返金を承諾するのは弊社の商品に不自信ではなく、行為でもって我々の誠意を示します。 だから、我々社は力の限りで弊社のAmazon　MLS-C01対策学習試験資料を改善し、改革の変更に応じて更新します。

AWS Certified Specialty MLS-C01 早くNewValidDumpsの問題集を君の手に入れましょう。

AWS Certified Specialty MLS-C01対策学習 - AWS Certified Machine Learning - Specialty たくさんのひとは弊社の商品を使って、試験に順調に合格しました。 君が後悔しないようにもっと少ないお金を使って大きな良い成果を取得するためにNewValidDumpsを選択してください。NewValidDumpsはまた一年間に無料なサービスを更新いたします。

この情報の時代の中に、たくさんのIT機構はAmazonのMLS-C01対策学習認定試験に関する教育資料がありますけれども、受験生がこれらのサイトを通じて詳細な資料を調べられなくて、対応性がなくて受験生の注意 に惹かれなりません。

Amazon MLS-C01対策学習 - そうすると、我々の信頼性をテストできます。

なぜ受験生のほとんどはNewValidDumpsを選んだのですか。それはNewValidDumpsがすごく便利で、広い通用性があるからです。NewValidDumpsのITエリートたちは彼らの専門的な目で、最新的なAmazonのMLS-C01対策学習試験トレーニング資料に注目していて、うちのAmazonのMLS-C01対策学習問題集の高い正確性を保証するのです。もし君はいささかな心配することがあるなら、あなたはうちの商品を購入する前に、NewValidDumpsは無料でサンプルを提供することができます。

このトレーニング資料を持っていたら、試験のために充分の準備をすることができます。そうしたら、試験に受かる信心も持つようになります。

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