Amazon AWS-Certified-Machine-Learning-Specialty 덤프는 Amazon AWS-Certified-Machine-Learning-Specialty 시험의 모든 문제를 커버하고 있어 시험적중율이 아주 높습니다. Pass4Test는 Paypal과 몇년간의 파트너 관계를 유지하여 왔으므로 신뢰가 가는 안전한 지불방법을 제공해드립니다. Amazon AWS-Certified-Machine-Learning-Specialty시험탈락시 제품비용 전액환불조치로 고객님의 이익을 보장해드립니다.
AWS Certified Machine Learning- 특수 시험은 기계 학습, 데이터 탐색 및 시각화, 기능 엔지니어링, 모델 선택 및 평가 및 딥 러닝의 기본 사항을 포함하여 광범위한 주제를 다룹니다. 또한 데이터 준비, 데이터 전처리 및 모델 최적화와 같은 주제도 포함됩니다. 이 시험은 이러한 개념을 실제 시나리오에 적용하고 기계 학습 기술을 사용하여 비즈니스 문제를 해결하는 후보자의 능력을 테스트하도록 설계되었습니다.
AWS Certified Machine Learning -Specialty Certification은 머신 러닝 기술을 검증하고 전문 지식에 대한 인정을 구하는 사람들을 위해 설계되었습니다. 이 시험에는 데이터 준비 및 기능 엔지니어링, 모델 교육 및 최적화, AWS에서 기계 학습 모델의 배포 및 관리를 포함하여 기계 학습과 관련된 광범위한 주제가 다루고 있습니다.
>> AWS-Certified-Machine-Learning-Specialty합격보장 가능 공부자료 <<
Pass4Test에서는 IT인증시험에 관한 모든 덤프를 제공해드립니다. 우선 시험센터에서 정확한 시험코드를 확인하시고 그 코드와 동일한 코드로 되어있는 덤프를 구매하셔서 덤프에 있는 문제와 답을 기억하시면 시험을 쉽게 패스하실수 있습니다.AWS-Certified-Machine-Learning-Specialty시험은 IT인증시험중에서 많은 인기를 가지고 있는 시험입니다.AWS-Certified-Machine-Learning-Specialty시험을 패스하여 자격증을 취득하시면 취업이나 승진에 많은 가산점이 되어드릴것입니다.
AWS 인증 머신 러닝 - 특수 인증 시험은 데이터 준비, 기능 엔지니어링, 모델링, 튜닝 및 배포를 포함한 기계 학습과 관련된 다양한 주제를 다룹니다. 또한 딥 러닝, 강화 학습 및 자연어 처리와 같은 주제도 포함됩니다. 이 시험은 기계 학습 개념을 실제 시나리오에 적용하고 AWS 플랫폼에서 기계 학습 솔루션 구현 능력을 평가하는 후보자의 능력을 테스트하도록 설계되었습니다.
질문 # 271
A Machine Learning Specialist is configuring Amazon SageMaker so multiple Data Scientists can access notebooks, train models, and deploy endpoints. To ensure the best operational performance, the Specialist needs to be able to track how often the Scientists are deploying models, GPU and CPU utilization on the deployed SageMaker endpoints, and all errors that are generated when an endpoint is invoked.
Which services are integrated with Amazon SageMaker to track this information? (Choose two.)
정답：A,C
설명：
https://aws.amazon.com/sagemaker/faqs/
질문 # 272
A Data Scientist is working on an application that performs sentiment analysis. The validation accuracy is poor and the Data Scientist thinks that the cause may be a rich vocabulary and a low average frequency of words in the dataset Which tool should be used to improve the validation accuracy?
정답：A
질문 # 273
An ecommerce company has developed a XGBoost model in Amazon SageMaker to predict whether a customer will return a purchased item. The dataset is imbalanced. Only 5% of customers return items A data scientist must find the hyperparameters to capture as many instances of returned items as possible. The company has a small budget for compute.
How should the data scientist meet these requirements MOST cost-effectively?
정답：A
설명：
The best solution to meet the requirements is to tune the csv_weight hyperparameter and the scale_pos_weight hyperparameter by using automatic model tuning (AMT). Optimize on
{"HyperParameterTuningJobObjective": {"MetricName": "validation:f1", "Type": "Maximize"}}.
The csv_weight hyperparameter is used to specify the instance weights for the training data in CSV format.
This can help handle imbalanced data by assigning higher weights to the minority class examples and lower weights to the majority class examples. The scale_pos_weight hyperparameter is used to control the balance of positive and negative weights. It is the ratio of the number of negative class examples to the number of positive class examples. Setting a higher value for this hyperparameter can increase the importance of the positive class and improve the recall. Both of these hyperparameters can help the XGBoost model capture as many instances of returned items as possible.
Automatic model tuning (AMT) is a feature of Amazon SageMaker that automates the process of finding the best hyperparameter values for a machine learning model. AMT uses Bayesian optimization to search the hyperparameter space and evaluate the model performance based on a predefined objective metric. The objective metric is the metric that AMT tries to optimize by adjusting the hyperparameter values. For imbalanced classification problems, accuracy is not a good objective metric, as it can be misleading and biased towards the majority class. A better objective metric is the F1 score, which is the harmonic mean of precision and recall. The F1 score can reflect the balance between precision and recall and is more suitable for imbalanced data. The F1 score ranges from 0 to 1, where 1 is the best possible value. Therefore, the type of the objective should be "Maximize" to achieve the highest F1 score.
By tuning the csv_weight and scale_pos_weight hyperparameters and optimizing on the F1 score, the data scientist can meet the requirements most cost-effectively. This solution requires tuning only two hyperparameters, which can reduce the computation time and cost compared to tuning all possible hyperparameters. This solution also uses the appropriate objective metric for imbalanced classification, which can improve the model performance and capture more instances of returned items.
References:
*XGBoost Hyperparameters
*Automatic Model Tuning
*How to Configure XGBoost for Imbalanced Classification
*Imbalanced Data
질문 # 274
A network security vendor needs to ingest telemetry data from thousands of endpoints that run all over the world. The data is transmitted every 30 seconds in the form of records that contain 50 fields. Each record is up to 1 KB in size. The security vendor uses Amazon Kinesis Data Streams to ingest the data. The vendor requires hourly summaries of the records that Kinesis Data Streams ingests. The vendor will use Amazon Athena to query the records and to generate the summaries. The Athena queries will target 7 to 12 of the available data fields.
Which solution will meet these requirements with the LEAST amount of customization to transform and store the ingested data?
정답：A
설명：
The solution that will meet the requirements with the least amount of customization to transform and store the ingested data is to use Amazon Kinesis Data Analytics to read and aggregate the data hourly, transform the data and store it in Amazon S3 by using Amazon Kinesis Data Firehose. This solution leverages the built-in features of Kinesis Data Analytics to perform SQL queries on streaming data and generate hourly summaries.
Kinesis Data Analytics can also output the transformed data to Kinesis Data Firehose, which can then deliver the data to S3 in a specified format and partitioning scheme. This solution does not require any custom code or additional infrastructure to process the data. The other solutions either require more customization (such as using Lambda or EMR) or do not meet the requirement of aggregating the data hourly (such as using Lambda to read the data from Kinesis Data Streams). References:
* 1: Boosting Resiliency with an ML-based Telemetry Analytics Architecture | AWS Architecture Blog
* 2: AWS Cloud Data Ingestion Patterns and Practices
* 3: IoT ingestion and Machine Learning analytics pipeline with AWS IoT ...
* 4: AWS IoT Data Ingestion Simplified 101: The Complete Guide - Hevo Data
질문 # 275
A machine learning specialist is running an Amazon SageMaker endpoint using the built-in object detection algorithm on a P3 instance for real-time predictions in a company's production application. When evaluating the model's resource utilization, the specialist notices that the model is using only a fraction of the GPU.
Which architecture changes would ensure that provisioned resources are being utilized effectively?
정답：A
질문 # 276
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