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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q13-Q18):

NEW QUESTION # 13
You are analyzing a dataset that contains missing values.
Which of the following techniques is most appropriate when dealing with missing numerical data in a dataset, ensuring minimal impact on model performance?

A. Replacing missing values with a constant value (e.g., zero)
B. Removing rows with missing values
C. Replacing missing values with the mean of the column
D. Using k-nearest neighbors (KNN) imputation

Answer: D

NEW QUESTION # 14
A research team is analyzing large-scale social interactions and wants to identify strongly connected communities within a massive graph dataset using NVIDIA's cuGraph library.
Which method would be the most efficient for this task?

A. Apply cuGraph's Dijkstra's algorithm to find the shortest paths between all nodes and group them into communities.
B. Use cuGraph's Louvain method to detect hierarchical communities based on modularity optimization.
C. Apply cuGraph's Label Propagation Algorithm (LPA) to divide the graph into communities without predefining the number of clusters.
D. Run the cuGraph PageRank algorithm and classify nodes with high scores as community leaders.

Answer: B

NEW QUESTION # 15
Which of the following Nvidia technologies is primarily used for performing benchmarking and optimizing GPU-accelerated deep learning workflows, especially focusing on model training performance?

A. Nvidia CUDA
B. Nvidia Nsight Systems
C. Nvidia Triton Inference Server
D. Nvidia DeepStream SDK

Answer: B

NEW QUESTION # 16
You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.
Which approach best utilizes NVIDIA technologies for efficient forecasting?

A. Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
B. Use PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.
C. Use cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.
D. Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.

Answer: C

NEW QUESTION # 17
A data engineer is preparing a dataset for training a deep learning model. The dataset contains numerical features with missing values, outliers, and inconsistent units.
Which of the following strategies is the most appropriate for ensuring a standardized and clean dataset?

A. Use the median to fill missing values, convert all numerical values into categorical bins, and apply Min-Max scaling.
B. Replace missing values with the mean, apply z-score normalization, and clip extreme outliers based on a threshold (e.g., 3 standard deviations).
C. Remove all rows with missing values and outliers to ensure only clean data is used.
D. Standardize the dataset using the mean and standard deviation, but keep missing values and outliers unchanged to avoid data manipulation.

Answer: B

NEW QUESTION # 18
......

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Zachary Brooks Zachary Brooks

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