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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is using NVIDIA RAPIDS to perform statistical analysis as part of exploratory data analysis (EDA) on a dataset containing millions of product reviews. They need to compute basic descriptive statistics such as mean, median, and variance efficiently.
Which of the following methods is the most appropriate for performing these calculations on GPUs?

A) Use cuDF's built-in statistical functions like .mean(), .median(), and .var()
B) Use a traditional SQL database to compute statistics and then transfer results to the GPU
C) Convert the dataset into a PyTorch tensor and use PyTorch's statistical methods
D) Use NumPy's statistical functions, such as numpy.mean() and numpy.var()

2. A data scientist is training a deep learning model on an NVIDIA GPU-accelerated platform. The model is suffering from overfitting, leading to poor generalization on unseen data.
Which of the following techniques is the most effective for reducing overfitting in this scenario?

A) Reducing the learning rate
B) Increasing the number of layers in the model
C) Removing data augmentation techniques
D) Applying dropout regularization

3. Which of the following hardware components is most critical for accelerating the training of deep learning models?

A) Central Processing Unit (CPU)
B) Solid State Drive (SSD)
C) Random Access Memory (RAM)
D) Graphics Processing Unit (GPU)

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

5. A data science team is using NVIDIA GPUs to accelerate their AI workflow for a fraud detection system. They follow the CRISP-DM methodology to ensure a structured approach to the project.
During the Data Preparation phase, what is the most effective way to leverage NVIDIA technologies?

A) Ignore feature engineering and feed raw data directly into the model, relying on deep learning to extract relevant features.
B) Perform all data transformation on a single CPU core to ensure stability before deploying to GPU- accelerated training.
C) Use NVIDIA RAPIDS to preprocess large-scale transaction datasets efficiently on GPUs before training the model.
D) Manually clean and transform data using traditional CPU-based processing tools like pandas, then transfer the data to the GPU only for training.

Solutions: