NVIDIA NCP-ADS

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question #1
You are designing a machine learning pipeline and must decide whether your dataset qualifies as "big data" and requires specialized acceleration methods.
Which of the following characteristics best indicates that your dataset meets the definition of big data?

A. The dataset contains a large number of features (high dimensionality), even if it fits comfortably in local memory.
B. The dataset includes millions of small text files stored on a local disk but does not require complex computations.
C. The dataset primarily consists of real-time sensor data that streams continuously but does not exceed local storage limits.
D. The dataset is too large to fit into the memory (RAM) of a single machine and requires distributed processing.

Question #2
You are working with a large dataset on an NVIDIA GPU, where optimizing memory usage is a priority. Your dataset contains a column, transaction_id, which stores unique integer values ranging between 0 and 100,000.
Which of the following data types is the most memory-efficient choice for this column in cuDF?

A. df['transaction_id'] = df['transaction_id'].astype('int8')
B. df['transaction_id'] = df['transaction_id'].astype('int64')
C. df['transaction_id'] = df['transaction_id'].astype('int32')
D. df['transaction_id'] = df['transaction_id'].astype('float32')

Question #3
You are working with structured tabular data in a cloud-based GPU environment.
Your dataset contains the following columns:
Column Name Example Values Data Type Needed
user_id 15432, 98765, 43210 Integer
purchase_amt 12.99, 35.50, 100.75 Floating Point
category 'Books', 'Electronics' Categorical
Which of the following is the most optimal approach to assign data types to these columns to ensure efficient memory usage and computational performance?

A. 1. df['user_id'] = df['user_id'].astype('float32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
B. 1. df['user_id'] = df['user_id'].astype('int64')
2. df['purchase_amt'] = df['purchase_amt'].astype('float64')
3. df['category'] = df['category'].astype('string')
C. 1. df['user_id'] = df['user_id'].astype('int32')
2. df['purchase_amt'] = df['purchase_amt'].astype('float32')
3. df['category'] = df['category'].astype('category')
D. 1. df['user_id'] = df['user_id'].astype('int16')
2. df['purchase_amt'] = df['purchase_amt'].astype('float16')
3. df['category'] = df['category'].astype('string')

Question #4
A data scientist is setting up a RAPIDS AI environment for a machine learning project that requires CUDA-enabled libraries and specific package versions to avoid conflicts.
Which of the following approaches best ensures a stable and reproducible environment while leveraging NVIDIA technologies?

A. Install CUDA and RAPIDS AI libraries using OS-level package managers such as apt or yum instead of Conda or Docker.
B. Manually install each required package with pip to ensure the latest versions are used without interference from Conda.
C. Use Conda with the rapidsai channel to create an isolated environment that includes cuDF, cuML, and other GPU-accelerated libraries.
D. Use a system-wide Python installation and install packages globally to ensure consistency across different users.

Question #5
You are tasked with optimizing the performance of a large-scale data science project that involves deep learning models on a cloud infrastructure. Your organization is using GPUs for model training.
Which of the following strategies would be the most effective in optimizing GPU performance for data science tasks? (Select two)

A. Overclock the GPU to achieve higher computational speeds and improve training times.
B. Utilize multi-GPU training to parallelize the workload, reducing training time.
C. Use larger batch sizes to make better use of GPU memory during model training.
D. Use a single cloud instance with the largest GPU available to ensure maximum performance.
E. Optimize GPU performance by limiting the number of threads running on each GPU.

Solutions: