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TensorFlow Certificate Network TensorFlow-Developer Questions

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  • Exam Name:    TensorFlow Developer Certificate
  • Certification:     TensorFlow Certificate Network
  • Vendor Name:  Google
  • Exam Code:       TensorFlow-Developer
  • Update on: July 18, 2024
  • Questions: 300

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If you are struggling for a successful professional career then it is necessary to boost your skills in the field of IT. TensorFlow Developer Certificate exam certification can help you to proceed successfully in your professional career. Prepare through updated and verified TensorFlow-Developer exam dumps that will not only increase the worth of your resume but it will also open doors to new job opportunities. TensorFlow Certificate Network will help you to proceed successfully in your professional career.

The TensorFlow Developer Certificate exam assesses your ability to solve problems by building models using TensorFlow 2.x. Here’s what you need to know:

  1. Exam Details:
    • The exam tests your skills in developing software programs using TensorFlow.
    • You’ll demonstrate your understanding of TensorFlow APIs, debugging, and model creation.
    • The exam focuses on building and training neural network models using TensorFlow 2.x.
    • You’ll work with machine learning (ML) models, preprocess data, and predict results.
    • Strategies to prevent overfitting, batch loading of data, and using callbacks are also covered.
    • The exam duration is up to 5 hours.
  2. Skills Checklist:
    • TensorFlow Developer Skills:
      • Proficiency in Python programming, resolving Python issues, and running Python programs.
      • Finding information about TensorFlow APIs (guides, references, etc.).
      • Debugging and solving errors from the TensorFlow API.
      • Creating ML models with reasonable model sizes.
      • Understanding compatibility differences between TensorFlow versions.
    • Building and Training Neural Network Models:
      • Using TensorFlow 2.x.
      • Compiling and training ML models.
      • Preprocessing data for model use.
      • Building sequential models with multiple layers.
      • Handling binary classification and multi-class categorization.
      • Plotting loss and accuracy.
      • Preventing overfitting (augmentation, dropout).
      • Using pretrained models (transfer learning).
      • Ensuring correct input and output shapes.
      • Working with datasets in different formats (JSON, CSV).

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