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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are tasked with designing and implementing a benchmark to compare the performance of different deep learning frameworks, including TensorFlow, PyTorch, and JAX, using NVIDIA GPUs.
Which of the following is the most effective approach to ensure an accurate and fair comparison?

A) Run each framework with default settings to compare their out-of-the-box performance without any optimizations.
B) Ensure identical hardware configurations, dataset preprocessing, and model architectures while leveraging NVIDIA's Nsight Systems and DLProf for analysis.
C) Use mixed precision (FP16) training only in TensorFlow to maximize performance while keeping other frameworks at FP32.
D) Compare training times only without considering throughput, power efficiency, or memory utilization.

2. You are preparing a dataset for training a machine learning model using NVIDIA RAPIDS cuML. The dataset contains a feature representing timestamps in nanoseconds.
To optimize GPU performance while ensuring precision, which data type should you choose?

A) int32 - Uses less memory and can store high-precision timestamps efficiently.
B) bool - Provides a highly efficient way to store timestamps as binary values.
C) object - Allows flexibility in storing timestamps as strings for easier parsing.
D) datetime64[ns] - Optimizes storage and computation for timestamp data in RAPIDS.

3. You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?

A) Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution
B) Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF
C) Use cuDF's .fillna() method to replace missing values in GPU memory
D) Drop all missing values using .dropna() instead of filling them, as GPU memory is limited

4. You are working on a large dataset for a machine learning model that will be trained using RAPIDS cuML. The dataset includes categorical, integer, and floating-point features.
Which of the following approaches is the best practice for determining the optimal data type choice for each feature using NVIDIA's RAPIDS cuDF library?

A) Convert categorical variables into int8 to optimize GPU memory usage.
B) Convert all numerical data to float64 for maximum precision in calculations.
C) Use float32 instead of float64 for floating-point numbers when possible, and leverage int8, int16, or int32 for categorical and integer data based on their range.
D) Use float16 for all floating-point data to reduce memory usage and increase GPU processing speed.

5. You are training a machine learning model using scikit-learn-like API on a dataset with millions of samples and thousands of features. You need to optimize both training time and inference speed using NVIDIA technologies.
Which solution is the most appropriate?

A) Use NVIDIA Modulus to accelerate machine learning training and feature selection.
B) Use NVIDIA RAPIDS cuML for GPU-accelerated machine learning model training.
C) Use NVIDIA Triton Inference Server to train the model efficiently on a single GPU.
D) Use NVIDIA Magnum IO to optimize machine learning model parameters on the GPU.

Solutions: