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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 millions of high-resolution images for a deep learning project. The dataset needs to be processed efficiently on a GPU before training a model.
Which NVIDIA technology is best suited for preprocessing, augmenting, and efficiently loading the dataset into memory?

A) NVIDIA RAPIDS cuDF to transform image data into tabular format for analysis.
B) NVIDIA Nsight Compute to optimize image dataset processing.
C) NVIDIA Triton Inference Server to preprocess the dataset before model training.
D) NVIDIA DALI (Data Loading Library) to accelerate data loading and preprocessing on the GPU.

2. A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A) Manually generate random values using Python's built-in random module and load them into a cuDF DataFrame.
B) Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
C) Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
D) Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.

3. A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.
The dataset contains:
A low-cardinality categorical feature (Product Type) with 10 unique values.
A high-cardinality categorical feature (User ID) with 100,000 unique values.
A numerical feature (Price) that requires transformation.
Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

A) Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision.
B) Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.
C) Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.
D) Frequency encoding for User ID is an efficient alternative to one-hot encoding, as it replaces each category with its frequency in the dataset, reducing dimensionality while preserving useful information.
E) Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.
F) Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.

4. A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A) Train a generative adversarial network (GAN) using PyTorch and then use the generated samples in RAPIDS AI without any additional processing.
B) Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
C) Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
D) Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.

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

Solutions: