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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are tasked with designing and implementing a benchmark to compare the performance of different deep learning frameworks, including TensorFlow, PyTorch, and JAX, using NVIDIA GPUs.
Which of the following is the most effective approach to ensure an accurate and fair comparison?

A) Run each framework with default settings to compare their out-of-the-box performance without any optimizations.
B) Use mixed precision (FP16) training only in TensorFlow to maximize performance while keeping other frameworks at FP32.
C) Compare training times only without considering throughput, power efficiency, or memory utilization.
D) Ensure identical hardware configurations, dataset preprocessing, and model architectures while leveraging NVIDIA's Nsight Systems and DLProf for analysis.

2. You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

A) df = (df - df.min()) / (df.max() - df.min())
B) df = (df - df.mean()) / df.std()
C) 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)
D) df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)

3. You are setting up a GPU-accelerated data science environment on a cloud-based instance that utilizes NVIDIA GPUs.
To ensure compatibility between CUDA, RAPIDS, and Python libraries, which of the following is the most effective approach for managing dependencies and avoiding version conflicts?

A) Install RAPIDS, PyTorch, TensorFlow, and other libraries in the same Conda base environment for simplified access
B) Use a virtual machine with all dependencies pre-installed to avoid any software conflicts
C) Manually install required libraries in a system-wide Python environment using pip install without version specifications
D) Use Conda environments with version-pinned dependencies to create an isolated environment for RAPIDS and CUDA

4. Which of the following tools can be used for profiling deep learning models to identify performance bottlenecks and optimize execution on NVIDIA GPUs? (Select two)

A) TensorBoard
B) DLProf
C) NVIDIA Nsight Systems
D) Python's cProfile

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

Solutions: