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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are tasked with cleansing a dataset containing numerical data that has significant outliers.
You're using pandas to identify and appropriately handle these outliers before applying CuDF for accelerated downstream analysis.
Which method effectively manages the numerical outliers while preserving the dataset's integrity for subsequent accelerated analytics?

A) Replace outliers with zero using pandas.Series.replace().
B) Fill outlier values with mean using pandas.Series.fillna().
C) Clip outliers using pandas.Series.clip() based on percentile thresholds (e.g., 5th and 95th percentile).
D) Remove outliers completely using pandas.DataFrame.drop().

2. You are analyzing a large financial dataset containing stock market tick-by-tick data stored in a cuDF DataFrame. Since the dataset contains billions of data points, you need to aggregate it at the minute level before visualizing price trends efficiently.
Which of the following is the best approach for aggregating and visualizing this time-series data using NVIDIA technologies?

A) Use cuDF's .groupby() function to aggregate at the minute level, then visualize using hvPlot
B) Use cuML's TSNE function to reduce dimensionality before visualizing with Bokeh
C) Convert cuDF to Pandas, aggregate using .resample() in Pandas, and visualize using Matplotlib
D) Load the data into a relational database (e.g., PostgreSQL), run an SQL query for aggregation, and visualize using Seaborn

3. You are setting up a deep learning model for training on a multi-GPU cluster. You want to maximize training efficiency while maintaining model convergence.
Which of the following strategies is most effective in ensuring efficient multi-GPU training?

A) Train each GPU independently on a different dataset to reduce the communication bottleneck.
B) Use Model Parallelism, where different layers of the model are placed on different GPUs to reduce communication overhead.
C) Use Data Parallelism, where each GPU gets a different portion of the dataset, but gradients are averaged across all GPUs.
D) Reduce batch size to ensure each GPU receives only a small portion of the training data.

4. You are working on a large-scale graph analysis project using NVIDIA cuGraph for accelerated computations. Your dataset consists of millions of nodes and edges representing social network interactions. You need to efficiently compute PageRank while minimizing memory usage.
Which of the following techniques would be the most effective?

A) Use cuGraph's sparse matrix representation to store graph data and perform computations efficiently.
B) Use Pandas DataFrames to manage the graph structure and leverage Pandas indexing for efficient queries.
C) Convert the graph into an adjacency list format and store it in Python dictionaries for faster lookups.
D) Manually unroll loops and precompute node ranks in a NumPy array before feeding them into cuGraph.

5. You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?

A) Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution
B) Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs
C) Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
D) Use Dask DataFrame to parallelize deep learning model training across multiple GPUs

Solutions: