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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

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

2. In the CRISP-DM process, which NVIDIA technology is commonly used during the "Data Preparation" phase to handle large datasets efficiently?

A) NVIDIA Jetson
B) NVIDIA Deep Learning AI
C) NVIDIA RAPIDS
D) NVIDIA CUDA Toolkit

3. You are working with a dataset containing 2 billion rows of financial transactions, and you need to perform exploratory data analysis (EDA) before building a predictive model.
Which of the following approaches is the most appropriate for handling this data efficiently?

A) Use SQLite to store the data locally and run queries sequentially to minimize memory consumption.
B) Convert the dataset to JSON format and use Python's built-in json module to parse and analyze it efficiently.
C) Use an accelerated data science framework such as RAPIDS cuDF or Dask to distribute computations across GPUs.
D) Load the entire dataset into a Pandas DataFrame and analyze it using Pandas built-in methods.

4. You are analyzing a large financial dataset containing stock market tick-by-tick data stored in a cuDF DataFrame. Since the dataset contains billions of data points, you need to aggregate it at the minute level before visualizing price trends efficiently.
Which of the following is the best approach for aggregating and visualizing this time-series data using NVIDIA technologies?

A) Use cuDF's .groupby() function to aggregate at the minute level, then visualize using hvPlot
B) Convert cuDF to Pandas, aggregate using .resample() in Pandas, and visualize using Matplotlib
C) Use cuML's TSNE function to reduce dimensionality before visualizing with Bokeh
D) Load the data into a relational database (e.g., PostgreSQL), run an SQL query for aggregation, and visualize using Seaborn

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

Solutions: