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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a machine learning dataset with millions of rows and want to optimize performance on a GPU using CuDF. One of the features in your dataset represents categorical data with a limited number of unique values.
Which of the following data types should you use to maximize performance while reducing memory usage?

A) category
B) bool
C) object (string)
D) int64

2. Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?

A) Number of model parameters.
B) GPU utilization and memory usage.
C) Hyperparameter tuning results.
D) Training dataset bias.

3. A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A) Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
B) Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
C) Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
D) Train a generative adversarial network (GAN) using PyTorch and then use the generated samples in RAPIDS AI without any additional processing.

4. A machine learning engineer is working on a multi-GPU workload using Dask and RAPIDS to process a massive dataset efficiently. However, they notice that GPU utilization is not optimal, and data transfer between GPUs is slowing down computation.
What is the best approach to minimize data transfer overhead and maximize parallel efficiency?

A) Transfer data between GPUs using NumPy's .copy() function
B) Use Dask's single-threaded scheduler to avoid multi-threading overhead
C) Load the dataset into a Pandas DataFrame and distribute computations across GPUs manually
D) Use NVLink to enable high-bandwidth inter-GPU communication and dask_cuda.LocalCUDACluster

5. A data scientist is analyzing large-scale sensor readings from an industrial IoT system and wants to visualize high-frequency time-series data efficiently.
Which approach using NVIDIA technologies would be the most effective for interactive visualization of this dataset?

A) Apply TensorRT to compress the time-series data and use PyTorch's native visualization functions.
B) Process the data using RAPIDS cuDF, then convert it to CSV and visualize it with Excel charts.
C) Use NVIDIA OptiX to ray-trace the time-series dataset for visualization.
D) Use RAPIDS cuDF with Datashader to render large time-series data on the GPU efficiently.

Solutions: