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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are training a large-scale random forest model on a dataset with millions of rows and hundreds of features. The training time is significantly high when using traditional CPU-based machine learning frameworks.
Which NVIDIA technology should you use to accelerate training while maintaining compatibility with common ML frameworks like scikit-learn?

A) NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.
B) NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.
C) NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.
D) NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.

2. A machine learning engineer is working with a financial dataset that contains multiple numerical features, including income, loan amount, and transaction frequency. Some features are normally distributed, while others have a highly skewed distribution with extreme outliers.
Which of the following approaches best ensures uniformity across features before training a model?

A) Apply log transformation to skewed features before standardizing them with z-score normalization
B) Use one-hot encoding to transform numerical features into categorical representations
C) Scale all numerical features using min-max normalization
D) Remove outliers before applying standardization

3. You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

A) Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format
B) Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations
C) Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence
D) Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold

4. You are tasked with profiling a PyTorch-based deep learning model to identify performance bottlenecks using NVIDIA DLProf. Your goal is to analyze kernel execution times and identify operations causing excessive memory consumption.
Which of the following steps is the MOST appropriate sequence for profiling using DLProf?

A) Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.
B) Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
C) Execute the training script under DLProf TensorBoard mode to visualize performance insights, then re-run the model with automatic mixed precision (AMP) to reduce memory usage.
D) Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.

5. Which tools or technologies from NVIDIA are essential for implementing an efficient MLOps pipeline in production environments? (Select two)

A) NVIDIA CUDA for model training in cloud environments
B) NVIDIA Triton Inference Server for managing deployment and serving models
C) NVIDIA TensorRT for efficient model inference
D) NVIDIA DLA (Deep Learning Accelerator) for model deployment
E) NVIDIA NGC for storing and sharing machine learning datasets

Solutions: