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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are using RAPIDS cuML to train a regression model on a dataset with features of varying scales (temperature in Celsius, revenue in thousands, customer age). To improve model performance, you decide to standardize the data.
Which approach correctly standardizes the data using NVIDIA technologies?

A) Use cuml.StandardScaler() to transform the features to have a mean of zero and a standard deviation of one.
B) Use cuml.MinMaxScaler() to scale the features to a range of [0,1] without adjusting for mean and variance.
C) Use cuml.PCA() to reduce the dimensionality of the dataset, which also standardizes feature variance.
D) Use numpy.mean() and numpy.std() to manually standardize the dataset before feeding it into the GPU.

2. A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?

A) Using only virtual environments (venv) without managing GPU dependencies separately
B) Manually installing each package and its dependencies using pip
C) Using a single system-wide installation of CUDA and forcing all projects to use the same version
D) Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies

3. A data scientist wants to compare the performance of two different GPU-accelerated data science frameworks, NVIDIA RAPIDS (cuDF, cuML) and TensorFlow, for a tabular data classification task.
Which of the following approaches would be the best practice for designing an unbiased and effective benchmark?

A) Run all benchmarks on a CPU to ensure fairness across frameworks.
B) Ignore preprocessing and focus only on model training speed when comparing performance.
C) Measure execution time and memory usage for each framework using NVIDIA Nsight Systems (nsys).
D) Use TensorFlow's built-in training time metrics without comparing equivalent RAPIDS-based operations.

4. A data engineering team is tasked with processing terabytes of log data every hour using an ETL pipeline. Due to the large data volume, they need a scalable GPU-accelerated solution that can distribute data processing across multiple GPUs.
Which approach best meets their needs?

A) Process data using Pandas, then export the results to a CSV file for GPU-accelerated analytics.
B) Use Dask-cuDF to distribute cuDF DataFrame operations across multiple GPUs, enabling parallel ETL processing.
C) Use NumPy for data transformations before converting the dataset into cuDF for final storage.
D) Use cuDF alone for processing log data, as it provides optimal performance on a single GPU.

5. Which of the following best describes the functionality of DLProf in deep learning model profiling?

A) DLProf can only profile the CPU side of a deep learning model's performance.
B) DLProf tracks GPU utilization, memory bandwidth, and kernel execution time to identify performance bottlenecks.
C) DLProf uses statistical methods to predict future model performance based on historical data.
D) DLProf is used for visualizing model predictions, similar to TensorBoard.

Solutions: