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

2. You are working on a data science project that requires processing a large-scale dataset stored in CSV format. The dataset contains hundreds of millions of rows, and you want to load it efficiently into NVIDIA RAPIDS cuDF for accelerated processing on a GPU.
Which of the following approaches is the most optimal way to load the dataset?

A) import cudf 2. df = cudf.DataFrame.from_pandas(pd.read_csv("large_dataset.csv"))
B) import dask_cudf 2. df = dask_cudf.read_csv("large_dataset.csv")
C) import cudf 2. df = cudf.read_csv("large_dataset.csv", chunksize=100000)
D) import pandas as pd 2. df = pd.read_csv("large_dataset.csv")

3. You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

A) Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
B) Ensuring the ETL pipeline uses only batch processing for data ingestion.
C) Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.
D) Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.
E) Using a single storage location for both raw and transformed data to simplify the workflow.

4. You are working with cloud-based GPUs to process a large dataset (terabytes in size) stored in Parquet format. One column represents a unique identifier (e.g., product ID), and it contains only positive integers ranging from 1 to 100,000.
Which of the following data types provides the best balance of memory efficiency and performance?

A) uint16
B) float32
C) int8
D) float64

5. What is the primary advantage of using NVIDIA Triton Inference Server for deploying and monitoring machine learning models in production?

A) It only supports TensorFlow models for inference.
B) It automatically tunes hyperparameters for all models.
C) It is designed solely for edge devices and not for data centers.
D) It provides GPU optimization to handle high-throughput inference workloads.

Solutions: