その中で、MLS-C01対応受験認定試験は最も重要な一つです。では、この試験に合格するためにどのように試験の準備をしているのですか。がむしゃらに試験に関連する知識を勉強しているのですか。 我々は多くの受験生にAmazonのMLS-C01対応受験試験に合格させたことに自慢したことがないのです。IT業界での競争が激しいですから、我々は発展のために改善し続けなければなりません。 まだ何を待っていますか。
AWS Certified Specialty MLS-C01 それは正確性が高くて、カバー率も広いです。
短時間でMLS-C01 - AWS Certified Machine Learning - Specialty対応受験試験に一発合格したいなら、我々社のAmazonのMLS-C01 - AWS Certified Machine Learning - Specialty対応受験資料を参考しましょう。 もちろん、我々はあなたに一番安心させるのは我々の開発する多くの受験生に合格させるAmazonのMLS-C01 合格受験記試験のソフトウェアです。我々はあなたに提供するのは最新で一番全面的なAmazonのMLS-C01 合格受験記問題集で、最も安全な購入保障で、最もタイムリーなAmazonのMLS-C01 合格受験記試験のソフトウェアの更新です。
MLS-C01対応受験試験備考資料の整理を悩んでいますか。専業化のIT認定試験資料提供者Omgzlookとして、かねてより全面的の資料を準備します。あなたの資料を探す時間を節約し、Amazon MLS-C01対応受験試験の復習をやっています。
Amazon MLS-C01対応受験 - 迷ってないください。
今の社会の中で、ネット上で訓練は普及して、弊社は試験問題集を提供する多くのネットの一つでございます。Omgzlookが提供したのオンライン商品がIT業界では品質の高い学習資料、受験生の必要が満足できるサイトでございます。
それに、AmazonのMLS-C01対応受験の試験の実践経験やテストダンプにも含まれています。Omgzlookは受験生たちを助けて試験の準備をして、試験に合格するサイトですから、受験生のトレーニングにいろいろな便利を差し上げます。
MLS-C01 PDF DEMO:
QUESTION NO: 1
A Data Scientist wants to gain real-time insights into a data stream of GZIP files. Which solution would allow the use of SQL to query the stream with the LEAST latency?
A. Amazon Kinesis Data Firehose to transform the data and put it into an Amazon S3 bucket.
B. Amazon Kinesis Data Analytics with an AWS Lambda function to transform the data.
C. AWS Glue with a custom ETL script to transform the data.
D. An Amazon Kinesis Client Library to transform the data and save it to an Amazon ES cluster.
Answer: B
QUESTION NO: 2
Amazon Connect has recently been tolled out across a company as a contact call center The solution has been configured to store voice call recordings on Amazon S3 The content of the voice calls are being analyzed for the incidents being discussed by the call operators Amazon Transcribe is being used to convert the audio to text, and the output is stored on Amazon S3 Which approach will provide the information required for further analysis?
A. Use Amazon Comprehend with the transcribed files to build the key topics
B. Use the AWS Deep Learning AMI with Gluon Semantic Segmentation on the transcribed files to train and build a model for the key topics
C. Use Amazon Translate with the transcribed files to train and build a model for the key topics
D. Use the Amazon SageMaker k-Nearest-Neighbors (kNN) algorithm on the transcribed files to generate a word embeddings dictionary for the key topics
Answer: C
QUESTION NO: 3
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: 4
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: 5
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
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Updated: May 28, 2022