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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is using NVIDIA RAPIDS to perform statistical analysis as part of exploratory data analysis (EDA) on a dataset containing millions of product reviews. They need to compute basic descriptive statistics such as mean, median, and variance efficiently.
Which of the following methods is the most appropriate for performing these calculations on GPUs?

A) Use NumPy's statistical functions, such as numpy.mean() and numpy.var()
B) Use a traditional SQL database to compute statistics and then transfer results to the GPU
C) Use cuDF's built-in statistical functions like .mean(), .median(), and .var()
D) Convert the dataset into a PyTorch tensor and use PyTorch's statistical methods

2. You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

A) Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format
B) Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold
C) Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence
D) Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations

3. Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?

A) Hyperparameter tuning results.
B) Number of model parameters.
C) GPU utilization and memory usage.
D) Training dataset bias.

4. You have a large-scale dataset consisting of IoT sensor readings collected at one-minute intervals across multiple locations. The dataset contains missing values and requires scaling before applying a machine learning model. You plan to use NVIDIA RAPIDS to preprocess and analyze the time-series data efficiently on GPUs.
Which of the following preprocessing steps is the most efficient approach using NVIDIA RAPIDS?

A) Use pandas to fill missing values and scale the data, then convert it to cuDF for training.
B) Use pandas for missing value imputation, then normalize the data using NumPy before converting to cuDF.
C) Use Dask for distributed missing value imputation and train a model using TensorFlow's CPU-based estimator.
D) Use cuDF to handle missing values with GPU-accelerated interpolation and apply cuML's StandardScaler for feature scaling.

5. Which of the following best describes the purpose of the NVIDIA TensorRT library?

A) Provides hardware abstraction for AI model development
B) Accelerates training of neural networks
C) Manages GPU resources for deep learning models
D) Optimizes and accelerates inference of trained models

Solutions: