Working with common deep learning data types: Practice Questions — Software Development (NVIDIA-Certified Associate: Generative AI LLM)

Practice Questions: Working with Common Deep Learning Data Types

These multiple-choice questions are designed to help candidates prepare for the NVIDIA-Certified Associate: Generative AI LLM exam, focusing specifically on understanding and working with common deep learning data types.

1. Which data type is most commonly used to represent image data in deep learning frameworks?

   • A. Integer tensors with shape (batch_size, height, width, channels)
   • B. String tensors with shape (batch_size, sequence_length)
   • C. Sparse tensors with shape (features, samples)
   • D. Boolean tensors with shape (batch_size, features)

   Correct answer: A

   Explanation: Image data is typically represented as integer or floating-point tensors with dimensions corresponding to batch size, image height, width, and color channels (e.g., RGB). This format is standard in deep learning frameworks such as PyTorch and TensorFlow.

2. In natural language processing tasks using LLMs, which data type best represents tokenized text inputs?

   • A. Float tensors representing pixel intensities
   • B. Integer tensors representing token IDs
   • C. Boolean tensors representing presence or absence of features
   • D. Sparse tensors representing graph edges

   Correct answer: B

   Explanation: Tokenized text is converted into integer tensors where each integer corresponds to a token ID from the model's vocabulary. These integer tensors serve as inputs to LLMs.

3. Which of the following data types is most suitable for representing one-hot encoded categorical features in deep learning?

   • A. Dense float tensors
   • B. Sparse tensors
   • C. Integer tensors
   • D. String tensors

   Correct answer: B

   Explanation: One-hot encoded categorical data can be efficiently represented using sparse tensors, which store only non-zero elements, saving memory and computation.

4. When deploying a model on an inference server, which data type should inputs typically conform to for compatibility?

   • A. Raw text strings
   • B. Preprocessed numeric tensors matching the model's input signature
   • C. Image file paths
   • D. Unstructured JSON objects

   Correct answer: B

   Explanation: Models deployed on inference servers require inputs as numeric tensors that match the expected input shape and data type defined during model development.

5. Which Python library is commonly used to convert raw text into token ID tensors for LLM input?

   • A. NumPy
   • B. Transformers (Hugging Face)
   • C. Matplotlib
   • D. OpenCV

   Correct answer: B

   Explanation: The Hugging Face Transformers library provides tokenizers that convert raw text into token ID tensors compatible with large language models.

6. What is the typical data type used to represent model weights in deep learning frameworks?

   • A. Integer tensors
   • B. Float tensors (e.g., float32 or float16)
   • C. Boolean tensors
   • D. String tensors

   Correct answer: B

   Explanation: Model weights are stored as floating-point tensors to capture the continuous values learned during training, commonly using 32-bit or 16-bit precision.

7. Which data type is best suited for representing batched sequences of variable length in LLM inputs?

   • A. Padded integer tensors with attention masks
   • B. Sparse float tensors
   • C. Boolean tensors without padding
   • D. Raw string arrays

   Correct answer: A

   Explanation: Variable-length sequences are typically padded to a uniform length and represented as integer tensors, accompanied by attention masks to indicate valid tokens for the model.

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