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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a large dataset containing customer transactions and want to perform exploratory data analysis (EDA) efficiently. Given the dataset's size, you decide to use NVIDIA RAPIDS to accelerate the process.
Which of the following approaches is the most effective for conducting EDA using NVIDIA technologies?

A) Use RAPIDS cuDF to perform fast dataframe operations and visualize results with cuXfilter
B) Load the dataset into Pandas and use Matplotlib for visualization
C) Use TensorFlow and Keras to preprocess the data before performing EDA
D) Perform SQL queries on a CPU-based database for initial data analysis before GPU acceleration

2. You are working with cloud-based GPUs to process a large dataset (terabytes in size) stored in Parquet format. One column represents a unique identifier (e.g., product ID), and it contains only positive integers ranging from 1 to 100,000.
Which of the following data types provides the best balance of memory efficiency and performance?

A) float32
B) uint16
C) float64
D) int8

3. A data scientist is training a deep learning model on an NVIDIA GPU but notices that the training speed is not significantly faster than when using a CPU.
Which of the following strategies is the best approach to fully utilize GPU acceleration and optimize training performance?

A) Increasing the batch size beyond the GPU's memory limit
B) Using only CPU-based data augmentation to keep the GPU dedicated to training
C) Disabling cuDNN optimizations to allow more flexibility in kernel execution
D) Using mixed precision training with NVIDIA Tensor Cores

4. Which of the following is the most appropriate way to perform large-scale data processing in a GPU- accelerated environment using NVIDIA RAPIDS?

A) Use NumPy exclusively for processing large datasets on GPUs.
B) Use pandas for all data manipulations and rely on multi-threading for parallel execution.
C) Use Dask on top of RAPIDS for distributed computing across multiple GPUs.
D) Use TensorFlow for all data manipulations in a GPU environment.

5. A machine learning engineer is working with a financial dataset that contains multiple numerical features, including income, loan amount, and transaction frequency. Some features are normally distributed, while others have a highly skewed distribution with extreme outliers.
Which of the following approaches best ensures uniformity across features before training a model?

A) Use one-hot encoding to transform numerical features into categorical representations
B) Scale all numerical features using min-max normalization
C) Remove outliers before applying standardization
D) Apply log transformation to skewed features before standardizing them with z-score normalization

Solutions: