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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on an MLOps project where GPU-accelerated workflows are being used for model training. You want to benchmark and optimize these workflows to ensure the best performance.
Which of the following steps should you consider to effectively benchmark and optimize GPU- accelerated workflows? (Select two)

A) Use profiling tools to measure the GPU utilization and memory usage during training to identify performance bottlenecks.
B) Use a dynamic batch size strategy that adjusts the batch size based on available GPU memory to maximize throughput.
C) Optimize data loading by using data augmentation techniques during training to reduce the time spent on I/O operations.
D) Increase the batch size and learning rate simultaneously to maximize GPU usage and reduce training time.

2. Which of the following best describes the role of MLOps in the context of NVIDIA technologies for deploying machine learning models in production? (Select two)

A) MLOps replaces the need for data preprocessing during training and deployment
B) MLOps ensures that models trained on GPUs can only run on GPUs during deployment
C) MLOps frameworks support version control and automation, ensuring reproducibility and scalability of ML workflows
D) MLOps helps manage the lifecycle of machine learning models, ensuring efficient collaboration and model governance

3. You are comparing the performance of NVIDIA RAPIDS cuML, TensorFlow, and PyTorch for training and inference on a dataset with millions of records.
To design a fair and effective benchmark, which approach should you take?

A) Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.
B) Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.
C) Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.

4. You are working with a large dataset using NVIDIA RAPIDS cuDF and need to normalize a numerical column (price) to scale its values between 0 and 1.
Which of the following approaches correctly normalizes the column using cuDF?

A) df["price"] = df["price"] / df["price"].max()
B) df["price"] = (df["price"] - df["price"].mean()) / df["price"].std()
C) df["price"] = df["price"].applymap( 2. lambda x: (x - df["price"].min()) 3. / (df["price"].max() - df["price"].min()) 4. )
D) df["price"] = ( 2. df["price"] - df["price"].min() 3. ) / (df["price"].max() - df["price"].min())

5. You are setting up a deep learning model for training on a multi-GPU cluster. You want to maximize training efficiency while maintaining model convergence.
Which of the following strategies is most effective in ensuring efficient multi-GPU training?

A) Use Data Parallelism, where each GPU gets a different portion of the dataset, but gradients are averaged across all GPUs.
B) Use Model Parallelism, where different layers of the model are placed on different GPUs to reduce communication overhead.
C) Reduce batch size to ensure each GPU receives only a small portion of the training data.
D) Train each GPU independently on a different dataset to reduce the communication bottleneck.

Solutions: