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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. You are training a deep learning model on a large dataset of images stored in an Amazon S3 bucket.
You want to optimize data loading, augmentation, and preprocessing on NVIDIA GPUs to avoid CPU bottlenecks.
Which of the following approaches is the most efficient for GPU-accelerated data preprocessing?

A) Use OpenCV to load and preprocess images on the CPU, then transfer the processed images to the GPU before training.
B) Use NVIDIA DALI to decode images, apply transformations such as resizing and normalization, and load batches directly to the GPU for training.
C) Load the dataset using PyTorch's torchvision.transforms and DataLoader, leveraging the CPU for data preprocessing and transferring batches to the GPU before training.
D) Use TensorFlow's tf.data API with tf.image transformations and ensure that the preprocessed images are transferred to GPU memory at the end of the pipeline.

3. A data scientist is training a deep learning model on an NVIDIA GPU but notices that the training speed is not significantly faster than when using a CPU.
Which of the following strategies is the best approach to fully utilize GPU acceleration and optimize training performance?

A) Using only CPU-based data augmentation to keep the GPU dedicated to training
B) Using mixed precision training with NVIDIA Tensor Cores
C) Increasing the batch size beyond the GPU's memory limit
D) Disabling cuDNN optimizations to allow more flexibility in kernel execution

4. Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A) Z-score Standardization
B) Log Transformation
C) Robust Scaling
D) Min-Max Scaling

5. You are analyzing a dataset containing millions of transaction records to detect fraudulent transactions. The dataset is highly imbalanced, with fraudulent cases making up only 0.5% of the data.
Which data analysis strategy will most effectively handle the imbalance while minimizing the risk of overfitting?

A) Stratified random sampling
B) Random oversampling of the minority class
C) SMOTE (Synthetic Minority Over-sampling Technique)
D) Random undersampling of the majority class

Solutions: