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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A) Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly
B) Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib
C) Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively
D) Load the data into a Spark DataFrame and visualize using Apache Zeppelin

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 CUDA Toolkit
D) NVIDIA RAPIDS

3. You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A) Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
B) Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.
C) Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
D) Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.

4. You are working on an accelerated data science project and need to acquire a large dataset stored in a Parquet file format and load it efficiently for GPU processing using NVIDIA RAPIDS.
Which of the following approaches is the most efficient way to load the dataset into a GPU-accelerated DataFrame?

A) df = pd.read_parquet("data.parquet")
B) df = cudf.to_gpu(pd.read_parquet("data.parquet"))
C) df = cudf.read_parquet("data.parquet")
D) df = cudf.read_csv("data.parquet")

5. A data engineer is tasked with processing a 5 TB dataset stored in Apache Parquet format. The dataset consists of user activity logs and needs to be filtered, aggregated, and processed for feature engineering before training an ML model. The engineer is deciding between Dask and Apache Spark.
Which statement best describes a key difference between the two frameworks?

A) Spark cannot utilize GPUs, whereas Dask has built-in GPU acceleration by default.
B) Spark is better suited for structured and semi-structured data, while Dask excels at unstructured data processing.
C) Dask is a more lightweight solution, often preferred for Python-centric workflows, whereas Spark provides broader ecosystem integrations and supports SQL-like operations natively.
D) Dask is optimized for in-memory processing, while Spark requires disk-based storage for computation.

Solutions: