NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is preparing a dataset containing numerical features with varying scales and distributions. Some features range from 0 to 1, while others have values ranging from -5000 to 5000. The dataset will be used in a machine learning model that relies on gradient-based optimization.
What is the best approach to standardizing the data to ensure uniformity across features?

A) Normalize the dataset by dividing each feature by its maximum absolute value
B) Keep the features as they are since machine learning models can handle varying scales naturally
C) Use min-max scaling to transform all features into the range [0,1]
D) Apply z-score normalization (standardization) to scale features to have a mean of 0 and a standard deviation of 1

2. You are implementing a Dask-based solution for distributed data parallelism across a multi-GPU system.
Which configuration steps would ensure effective use of GPUs for parallel computation? (Select two)

A) Use dask_cudf to convert DataFrame computations into GPU-accelerated operations using cuDF
B) Create a LocalCUDACluster and manually specify the GPUs you want to use for each Dask worker
C) Use dask_cuda's LocalCUDACluster and let Dask automatically allocate GPUs without any configuration
D) Use Dask's Cluster class with the distributed scheduler and specify CPU cores only for GPU workloads
E) Use dask_cuda's LocalCUDACluster with proper GPU memory management to handle multiple GPUs

3. You are conducting rapid experimentation on an NVIDIA GPU to determine the best trade-off between model accuracy and inference latency.
Which approach is the most efficient for systematically evaluating multiple configurations?

A) Reduce training epochs significantly to save time, even if the model is underfitting
B) Train each possible model variation from scratch to evaluate accuracy and performance differences
C) Use automated hyperparameter tuning tools like Optuna or Ray Tune with mixed precision training
D) Test different model configurations on a CPU first before moving to the GPU for final evaluation

4. Which of the following best describes a key advantage of using cloud-based GPU instances for machine learning model training?

A) Cloud GPUs provide dynamically scalable resources, allowing users to increase or decrease compute power based on demand without upfront hardware investment.
B) Cloud GPU instances cannot support containerized workloads, limiting their applicability for MLOps and CI/CD pipelines.
C) Cloud-based GPU instances offer lower latency and better network performance compared to on- premise deployments, regardless of geographical location.
D) Cloud GPUs are always more cost-effective than on-premise GPUs, as they do not incur long-term usage costs.

5. You are tasked with optimizing the performance of an MLOps pipeline that uses GPU-accelerated workflows. After running initial benchmarks, you notice that the training time is higher than expected, despite the use of multiple GPUs.
What are the best strategies to optimize the GPU-accelerated workflow in this case? (Select two)

A) Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.
B) Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
C) Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.
D) Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.
E) Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.

Solutions: