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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of- memory (OOM) errors.
To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?

A) Increasing batch size without adjusting the optimizer settings
B) Using mixed precision training with automatic loss scaling
C) Storing all training data in GPU memory at once
D) Using single-precision (FP32) calculations for better accuracy

2. You need to train a deep learning model using PyTorch on a dataset too large for a single GPU. You decide to use Dask with NVIDIA GPUs for multi-GPU scaling.
Which approach is the most effective for distributing the workload?

A) Use Dask's built-in deep learning API to automatically distribute PyTorch models across GPUs
B) Use Dask Bag to shard the dataset and train separate PyTorch models on each shard
C) Use Dask.delayed to wrap PyTorch training functions and schedule them across multiple GPUs
D) Use Dask-CUDA workers with PyTorch's DistributedDataParallel (DDP) for training across multiple GPUs

3. You are optimizing a data pipeline for a large-scale machine learning project using NVIDIA RAPIDS and Apache Spark. The pipeline performs many expensive shuffle operations.
Which of the following is the most effective method to reduce shuffle and improve performance using NVIDIA technologies?

A) Use RAPIDS cuDF to repartition the data in the GPU memory after each shuffle.
B) Implement a custom shuffle partitioning scheme using NVIDIA DALI for more control over data partitioning during shuffle operations.
C) Use RAPIDS cuDF to perform in-memory data processing on GPU before shuffling to avoid network communication.
D) Store data in HDFS before performing shuffle operations to reduce GPU memory overhead.

4. You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.
Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

A) df.memory_usage(deep=True).sum()
B) sys.getsizeof(df)
C) df.info(memory_usage='deep')
D) np.array(df).nbytes

5. You are building a deep learning model using TensorFlow with cuDNN acceleration on an NVIDIA GPU. Your dataset contains continuous numerical features with vastly different ranges.
What is the best way to standardize the data efficiently to improve model convergence?

A) Apply batch normalization layers in the neural network to handle feature scaling dynamically during training.
B) Use cuml.StandardScaler() from RAPIDS to normalize the dataset before feeding it into the model.
C) Manually compute the feature mean and variance on the CPU and apply the transformation before training.
D) Use cupy.linalg.norm() to normalize each feature vector individually to unit length.

Solutions: