MLS-C01試験準備問題集を利用して試験に合格できます。この問題集の合格率は高いので、多くのお客様からMLS-C01試験準備問題集への好評をもらいました。MLS-C01試験準備問題集のカーバー率が高いので、勉強した問題は試験に出ることが多いです。 NewValidDumpsはあなたが楽に試験に合格することを助けます。NewValidDumpsを信頼してください。 NewValidDumpsにたくさんのIT専門人士がいって、弊社の問題集に社会のITエリートが認定されて、弊社の問題集は試験の大幅カーバして、合格率が１００％にまで達します。

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。

あなたはこの重要な試験を準備するのは時間とお金がかかると聞いたことがあるかもしれませんが、それは我々提供するAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty試験準備ソフトを利用しなかったからです。 NewValidDumpsのAmazonのMLS-C01 復習問題集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。

数年間の発展で我々NewValidDumpsはもっと多くの資源と経験を得ています。改善されているソフトはあなたのAmazonのMLS-C01試験準備試験の復習の効率を高めることができます。IT業界での競争がますます激しくなるうちに、あなたの能力をどのように証明しますか。

Amazon MLS-C01試験準備 - 我々の誠意を信じてください。

現在の社会で、MLS-C01試験準備試験に参加する人がますます多くなる傾向があります。市場の巨大な練習材料からMLS-C01試験準備の学習教材を手に入れようとする人も増えています。 私たちのMLS-C01試験準備試験問題を利用し、ほかの資料が克服できない障害を克服できます。 多くの受験者は、私たちのMLS-C01試験準備練習試験をすることに特権を感じています。 そして、私たちのウェブサイトは、市場でのとても有名で、インターネット上で簡単に見つけられます。

自分のIT業界での発展を希望したら、AmazonのMLS-C01試験準備試験に合格する必要があります。AmazonのMLS-C01試験準備試験はいくつ難しくても文句を言わないで、我々NewValidDumpsの提供する資料を通して、あなたはAmazonのMLS-C01試験準備試験に合格することができます。

MLS-C01 PDF DEMO:

QUESTION NO: 1
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: 2
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: 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