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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. In Python, when working with large datasets using pandas, which of the following methods are best for improving performance and efficiency when applying operations on DataFrames? (Select two)

A) Using for loops to apply operations row by row
B) Using apply() function over DataFrame rows
C) Using iterrows() for iterating through DataFrame rows
D) Using vectorized operations (e.g., element-wise arithmetic)
E) Using map() function to apply a function element-wise

2. A retail company is deploying an AI-driven demand forecasting system using NVIDIA GPUs. The team follows the CRISP-DM framework and is currently in the Evaluation phase.
Which approach best leverages NVIDIA technologies to assess model performance effectively?

A) Assume that a high training accuracy guarantees excellent real-world performance, skipping the evaluation phase.
B) Use RAPIDS cuML to rapidly compute evaluation metrics like RMSE and R-squared on large datasets using GPUs.
C) Rely only on training loss as the primary evaluation metric without considering validation performance.
D) Perform evaluation on a small CPU-based subset of the dataset instead of using full GPU-accelerated inference.

3. A data scientist is working with a large dataset containing millions of records and aims to accelerate the data preprocessing workflow using NVIDIA technologies.
Which of the following approaches is the most effective for optimizing data preprocessing performance using GPUs?

A) Running data transformations in a multi-threaded CPU environment
B) Using NumPy for array computations and parallelizing with Python's multiprocessing
C) Using RAPIDS cuDF to replace pandas operations
D) Using Dask to distribute processing across multiple CPU cores

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

5. A data scientist is working with large-scale tabular datasets and wants to optimize data ingestion and storage for accelerated processing on NVIDIA GPUs. The scientist is considering different file formats and storage optimizations to maximize performance in a RAPIDS-based workflow.
Which of the following approaches is the most suitable for optimizing both storage and processing performance?

A) Use JSON format for easy readability and process it directly in cuDF.
B) Load data directly into NumPy arrays before using RAPIDS cuDF for processing.
C) Convert datasets into CSV format and store them in local disk storage.
D) Store data in Parquet format and load it using cuDF in RAPIDS.

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