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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are a data scientist working with a large dataset containing millions of records. You want to perform exploratory data analysis (EDA) efficiently using NVIDIA RAPIDS on a GPU-accelerated system.
Which of the following approaches is the most efficient way to handle large-scale EDA using RAPIDS?

A) Load the dataset into an Apache Spark DataFrame and run .show() to inspect the data.
B) Perform all EDA using NumPy and SciPy for optimized array computations.
C) Use Pandas directly for data manipulation and visualization.
D) Convert the dataset into a cuDF DataFrame and perform operations like .describe() and
.value_counts() on the GPU.

2. Which of the following is the primary benefit of using NVIDIA RAPIDS in the context of data science workflows?

A) It simplifies the creation of visualizations for large datasets
B) It speeds up the preprocessing and analysis of large datasets by utilizing GPUs
C) It enables real-time data streaming and analytics
D) It allows for the implementation of reinforcement learning algorithms

3. You are tasked with designing an ETL workflow for a large-scale data processing pipeline using NVIDIA technologies. You need to ensure that the extraction, transformation, and loading phases are optimized for performance using hardware acceleration.
Which of the following NVIDIA technologies would be most suitable for accelerating the ETL process?

A) TensorRT
B) RAPIDS
C) TensorFlow
D) CUDA

4. Which of the following techniques are commonly used to identify and acquire datasets for machine learning and data science projects? (Select three)

A) Data augmentation
B) Crowdsourcing
C) Public APIs
D) Web scraping
E) Manual data entry

5. You are running a data science project on a cloud environment, where you need to optimize the GPU utilization for real-time data processing tasks.
Which of the following practices should you consider to maximize GPU performance? (Select two)

A) Use NVIDIA CUDA libraries optimized for data science tasks to accelerate computations.
B) Implement model inference in a batch mode rather than in a real-time streaming mode for better performance.
C) Scale the cloud infrastructure vertically by increasing the size of the GPU in use.
D) Use data parallelism techniques to split the workload evenly across multiple GPUs.
E) Prioritize using CPU for computationally expensive tasks and reserve GPU for I/O operations.

Solutions: