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NEW QUESTION: 1
A 54-year-old male with a normal ECG at rest develops dyspnea during an exercise (stress) test, and the following ECG pattern is noted at 25 watts:
25 mm/sec A pulmonary function technologist should
A. Continue the test until the subject reaches target heart rate.
B. Stop the test immediately; there is evidence of heart block.
C. Continue the test and obtain an arterial blood sample.
D. Stop the test immediately; there is evidence of ischemia.
Answer: B
NEW QUESTION: 2
A. WLAN
B. Management
C. Controller
D. Wireless
Answer: D
NEW QUESTION: 3
The REST API is a widely used standard for communications of web-based services between clients and the servers hosting them.
Which protocol does the REST API depend on?
A. SSH
B. HTTP
C. SAML
D. XML
Answer: B
Explanation:
Explanation
Representational State Transfer (REST) is a software architectural scheme that applies the components, connectors, and data conduits for many web applications used on the Internet. It uses and relies on the HTTP protocol and supports a variety of data formats. Extensible Markup Language (XML) and Security Assertion Markup Language (SAML) are both standards for exchanging encoded data between two parties, with XML being for more general use and SAML focused on authentication and authorization data. Secure Shell client (SSH) is a secure method for allowing remote login to systems over a network.
NEW QUESTION: 4
대형 소비재 제조업체는 다음과 같은 제품을 판매합니다
* 34 가지의 치약 변형
* 48 가지 칫솔 변형
* 43 가지 구강 세척제
이 모든 제품의 전체 판매 내역은 Amazon S3에서 확인할 수 있습니다. 현재이 회사는 맞춤형 자동 회귀 통합 이동 평균 (ARIMA) 모델을 사용하여 해당 제품에 대한 수요를 예측하고 있습니다. 회사는 곧 새로운 제품에 대한 수요를 예측하려고 합니다. 머신 러닝 전문가가 어떤 솔루션을 적용해야 합니까?
A. Amazon SageMaker DeepAR 알고리즘을 훈련하여 새로운 제품에 대한 수요 예측
B. 새로운 제품에 대한 수요를 예측하기 위해 사용자 정의 XGBoost 모델을 훈련
C. 맞춤형 ARIMA 모델을 교육하여 신제품에 대한 수요를 예측하십시오.
D. Amazon SageMaker k- 평균 클러스터링 알고리즘을 훈련시켜 신제품에 대한 수요를 예측하십시오.
Answer: A
Explanation:
The Amazon SageMaker DeepAR forecasting algorithm is a supervised learning algorithm for forecasting scalar (one-dimensional) time series using recurrent neural networks (RNN). Classical forecasting methods, such as autoregressive integrated moving average (ARIMA) or exponential smoothing (ETS), fit a single model to each individual time series. They then use that model to extrapolate the time series into the future.
https://docs.aws.amazon.com/sagemaker/latest/dg/deepar.html