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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are processing a large dataset in a distributed computing environment using RAPIDS and Dask.
Your workflow involves frequent shuffling of data between partitions, leading to significant slowdowns.
Which of the following strategies is the best way to implement data caching to reduce shuffle overhead using NVIDIA technologies?

A) Enable GPU-accelerated caching with RAPIDS cuDF and persist intermediate results in GPU memory.
B) Use a CPU-based caching solution like Memcached to store intermediate data before reloading into cuDF.
C) Disable caching altogether to force a recomputation of results, ensuring up-to-date data processing.
D) Use traditional disk-based caching by writing intermediate results to CSV files and reloading when needed.

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

3. A data scientist is working on a social network analysis project where they need to find the most influential users in a large-scale graph dataset. The dataset consists of millions of users connected through directed edges.
Which of the following approaches would be the best choice for this task using NVIDIA GPU-accelerated tools?

A) Use cuGraph's pagerank() function to identify influential nodes based on link structure.
B) Use cuDF's groupby().sum() function to count the number of connections per user.
C) Use cuGraph's bfs() (Breadth-First Search) to find the most influential nodes.
D) Convert the graph into a pandas DataFrame and apply NetworkX's PageRank implementation.

4. A data scientist is analyzing a large dataset of financial transactions containing millions of records.
To efficiently perform exploratory data analysis (EDA) using RAPIDS cuDF, which approach provides the most optimized performance while ensuring comprehensive insights?

A) Perform all analysis on the CPU to avoid potential GPU memory limitations.
B) Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.
C) Use RAPIDS cuDF functions like .describe() and .value_counts() to perform statistical summaries directly on the GPU.
D) Downsample the dataset and analyze a subset using Pandas for efficiency.

5. You are tasked with implementing a multi-GPU data pipeline using Dask-CUDA to process large datasets stored in Parquet format. Your goal is to achieve optimal GPU memory utilization and minimize inter-GPU communication overhead.
Which of the following approaches best aligns with these goals?

A) Use dask.array instead of dask_cudf because it provides better performance for structured tabular data.
B) Use dask_cudf.read_parquet() with split_row_groups=True to evenly distribute data across GPUs.
C) Use dask.persist() instead of dask.compute() to force immediate execution of tasks before distribution to GPUs.
D) Set dask.config.set({'distributed.worker.memory.target': 0.9}) to allocate 90% of the total CPU memory for GPU operations.

Solutions: