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AWS Certified Specialty MLS-C01 何の問題があったらお気軽に聞いてください。
AWS Certified Specialty MLS-C01資格取得 - AWS Certified Machine Learning - Specialty Goldmile-Infobizの問題と解答は初めに試験を受けるあなたが気楽に成功することを助けるだけではなく、あなたの貴重な時間を節約することもできます。 それに、あなたに極大な便利と快適をもたらせます。実践の検査に何度も合格したこのサイトは試験問題と解答を提供しています。
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Amazon MLS-C01資格取得 - いろいろな受験生に通用します。
あなたのキャリアでいま挑戦に直面していますか。自分のスキルを向上させ、よりよく他の人に自分の能力を証明したいですか。昇進する機会を得たいですか。そうすると、はやくMLS-C01資格取得認定試験を申し込んで認証資格を取りましょう。Amazonの認定試験はIT領域における非常に大切な試験です。AmazonのMLS-C01資格取得認証資格を取得すると、あなたは大きなヘルプを得ることができます。では、どのようにはやく試験に合格するかを知りたいですか。Goldmile-InfobizのMLS-C01資格取得参考資料はあなたの目標を達成するのに役立ちます。
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MLS-C01 PDF DEMO:
QUESTION NO: 1
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: 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 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: 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 built an image classification deep learning model. However the
Specialist ran into an overfitting problem in which the training and testing accuracies were 99% and
75%r respectively.
How should the Specialist address this issue and what is the reason behind it?
A. The learning rate should be increased because the optimization process was trapped at a local minimum.
B. The dimensionality of dense layer next to the flatten layer should be increased because the model is not complex enough.
C. The epoch number should be increased because the optimization process was terminated before it reached the global minimum.
D. The dropout rate at the flatten layer should be increased because the model is not generalized enough.
Answer: C
AmazonのMicrosoft AZ-204J問題集を購入したら、私たちは一年間で無料更新サービスを提供することができます。 Workday Workday-Pro-Compensation - 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。 IBM C1000-182 - あなたはGoldmile-Infobizの学習教材を購入した後、私たちは一年間で無料更新サービスを提供することができます。 Salesforce Analytics-Con-301 - 時間が経つとともに、我々はインタネット時代に生活します。 SAP C-ARP2P-2508 - 我々のデモから感じられます。
Updated: May 28, 2022