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[General] Get Success in Huawei H13-321_V2.0 Exam in the Easiest Way

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【General】 Get Success in Huawei H13-321_V2.0 Exam in the Easiest Way

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Huawei H13-321_V2.0 exam is divided into two parts. The first part focuses on the theory of AI, machine learning, deep learning, and the Huawei MindSpore platform. The second part of the exam is a practical test where candidates are required to develop an AI application using Huawei MindSpore. The test is designed to evaluate the candidate's practical skills in AI development and their proficiency in working with the Huawei tools and technologies.
Huawei H13-321_V2.0 (HCIP-AI-EI Developer V2.0) Certification Exam is a professional-level certification exam designed to test the knowledge and skills of developers in the field of Artificial Intelligence and Enterprise Intelligence. H13-321_V2.0 exam is conducted by Huawei, which is a leading global provider of information and communication technology (ICT) infrastructure and smart devices.
Huawei HCIP-AI-EI Developer V2.0 Sample Questions (Q353-Q358):NEW QUESTION # 353
We can use the spectrogram function in scipy to perform fast Fourier transform on the entire audio signal to extract the spectrogram.
  • A. False
  • B. True
Answer: B

NEW QUESTION # 354
The facial features detection API detects facial features of the input image and returns the coordinates of key points representing the location of the facial features. How many landmark points are currently supported?
  • A. 0
  • B. 1
  • C. 2
  • D. 3
Answer: D

NEW QUESTION # 355
Which of the following options makes GPU suitable for accelerating the operation of neural networks?
  • A. Control and Scheduling Capabilities
  • B. Parallel computing capabilities
  • C. The ability to call data from memory
  • D. Serial computing capability
Answer: B

NEW QUESTION # 356
The annotation work in data management support third-party annotation.
  • A. False
  • B. True
Answer: B

NEW QUESTION # 357
Use OpenCV to directly mirror the image. Complete the following horizontal mirror code: im_flip = cv2.flip(im, ())
Answer:
Explanation:
1

NEW QUESTION # 358
......
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