Assessing dataset memory requirements: Practice Questions — MLOps (NVIDIA-Certified Professional: Accelerated Data Science)

Practice Questions: Assessing Dataset Memory Requirements for MLOps

These multiple-choice questions focus on evaluating dataset memory requirements, a key skill for the NVIDIA-Certified Professional: Accelerated Data Science exam under the MLOps domain.

1. Question 1: You have a dataset with 1 million rows and 10 columns. Each column is stored as a 64-bit floating-point number. Approximately how much memory will the dataset require?

   • A) 80 MB
   • B) 76 MB
   • C) 80 GB
   • D) 76 GB

   Answer: A) 80 MB

   Explanation: Each 64-bit float = 8 bytes. Total bytes = 1,000,000 rows × 10 columns × 8 bytes = 80,000,000 bytes = 80 MB.

2. Question 2: Which data type choice will minimize memory usage when storing categorical data with 50 unique categories?

   • A) 64-bit integer
   • B) 32-bit float
   • C) 8-bit unsigned integer
   • D) 64-bit float

   Answer: C) 8-bit unsigned integer

   Explanation: 8-bit unsigned integers can represent values 0-255, enough for 50 categories, minimizing memory compared to larger types.

3. Question 3: A dataset contains missing values represented as NaNs in a 32-bit float column. Which data type conversion can reduce memory usage without losing the ability to represent missing values?

   • A) Convert to 16-bit float with a mask for NaNs
   • B) Convert to 8-bit integer
   • C) Convert to 64-bit float
   • D) Convert to string type

   Answer: A) Convert to 16-bit float with a mask for NaNs

   Explanation: 16-bit floats reduce memory usage and can represent NaNs; a mask can track missing values efficiently.

4. Question 4: When assessing dataset memory requirements, which factor is least relevant?

   • A) Number of rows
   • B) Number of columns
   • C) Data types of columns
   • D) Number of CPU cores

   Answer: D) Number of CPU cores

   Explanation: CPU cores affect processing speed but not dataset memory size.

5. Question 5: You need to load a dataset into GPU memory for accelerated processing. The dataset size is 12 GB, but your GPU memory is 8 GB. What is the best initial approach?

   • A) Downcast data types to reduce memory footprint
   • B) Increase batch size for training
   • C) Use 64-bit floats for precision
   • D) Load entire dataset at once

   Answer: A) Downcast data types to reduce memory footprint

   Explanation: Reducing data type sizes can decrease memory usage, enabling the dataset to fit into GPU memory.

6. Question 6: Which tool or method can help benchmark memory usage of a dataset efficiently?

   • A) Profiling with memory_profiler in Python
   • B) Using print statements
   • C) Increasing GPU clock speed
   • D) Using a larger batch size

   Answer: A) Profiling with memory_profiler in Python

   Explanation: memory_profiler provides detailed memory usage metrics for datasets and code.

7. Question 7: What is the effect of using sparse data formats on memory requirements?

   • A) Increase memory usage for dense datasets
   • B) Reduce memory usage for datasets with many zeros
   • C) Have no effect on memory
   • D) Increase computational overhead without memory benefit

   Answer: B) Reduce memory usage for datasets with many zeros

   Explanation: Sparse formats store only non-zero elements, saving memory when zeros dominate.

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