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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You need to process a dataset containing 10 billion rows, applying complex transformations and aggregations. The dataset does not fit into RAM, and you need an efficient, scalable solution for parallel processing.
Which of the following is the best choice?

A) PySpark
B) Pandas
C) Dask
D) Vaex

2. You are preprocessing a dataset using NVIDIA RAPIDS cuDF and need to handle missing values in the column temperature by replacing them with the column's median value.
Which of the following approaches correctly achieves this in an optimized manner?

A) 1. df['temperature'] = df['temperature'].map(2. lambda x: df['temperature'].median() if x is None else x
3.)
B) df['temperature'].fillna(df['temperature'].mean(), inplace=True)
C) df['temperature'].dropna(inplace=True)
D) df['temperature'].fillna(df['temperature'].median(), inplace=True)

3. You are working on a large dataset (several terabytes in size) and need to perform data preprocessing, filtering, and transformations before training a machine learning model.
Given the dataset size and the requirement to optimize for GPU acceleration using NVIDIA technologies, which of the following is the most appropriate data processing library to use?

A) pandas
B) Dask DataFrame with Dask-CUDA
C) Modin with Ray backend
D) NumPy with CuPy acceleration

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

5. You need to generate synthetic data to augment an imbalanced dataset using RAPIDS™ and cuDF.
Which of the following strategies would be most effective in producing high-quality synthetic data for the minority class?

A) Use only the majority class data to train a model and generate synthetic data using a GAN (Generative Adversarial Network) in the RAPIDS ecosystem.
B) Create synthetic data by applying random transformations to the minority class, such as scaling, rotation, or flipping, using cuDF.
C) Use synthetic data generation libraries like SDV (Synthetic Data Vault) in conjunction with cuDF to create synthetic data that mimics the distribution of the minority class.
D) Generate synthetic data by duplicating entries from the minority class using cudf.DataFrame.sample().

Solutions: