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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question #1
A data scientist wants to process a large dataset using multiple GPUs on an NVIDIA-supported system. They decide to use Dask to enable parallelism.
Which of the following steps is most essential for leveraging Dask for multi-GPU scaling?

A. Use dask.array or dask.dataframe with dask_cuda.CUDACluster to distribute computations across multiple GPUs
B. Train a deep learning model on a single GPU first, then switch to Dask for scaling
C. Use ThreadPoolExecutor to manage parallel computations on GPUs
D. Convert all data into Pandas DataFrames before distributing computations

Question #2
You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A. Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively
B. Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly
C. Load the data into a Spark DataFrame and visualize using Apache Zeppelin
D. Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib

Question #3
You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A. Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
B. Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.
C. Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
D. Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.

Question #4
You are training a deep learning model on a large dataset. Initially, you train the model on a single GPU and achieve a training time of 10 hours. To speed up training, you switch to a multi-GPU setup with four GPUs. However, after testing, you notice that the training time is only reduced to 3.5 hours instead of the expected 2.5 hours (a linear speedup).
What is the most likely reason for this sublinear speedup?

A. The learning rate is too low, causing slower convergence despite multiple GPUs.
B. Increased memory bandwidth usage causes a bottleneck in the system.
C. Data transfer and communication overhead between GPUs limit scalability.
D. The optimizer is not designed for multi-GPU training, causing inefficiency.

Question #5
A data scientist is working with a large dataset containing missing values and outliers. The dataset will be used for training a machine learning model. The scientist decides to preprocess the data using RAPIDS cuDF, an accelerated dataframe library.
Which of the following is the most efficient approach to handle missing values while maintaining data integrity?

A. Replace missing values with zero using df.fillna(0).
B. Convert missing values to a separate categorical class using df.fillna("missing").
C. Use df.fillna(df.mean()) to replace missing values with the column mean.
D. Use df.dropna() to remove all rows with missing values.

Solutions: