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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a large dataset containing missing values, and you need to clean and preprocess the data efficiently.
Which of the following methods provides the best performance when handling missing values in a GPU- accelerated EDA workflow using RAPIDS?

A) Use pandas.DataFrame.fillna() on the dataset before converting it to a cuDF DataFrame.
B) Drop all missing values using df.dropna() in Pandas before using RAPIDS.
C) Ignore missing values since GPU acceleration can handle incomplete data without performance degradation.
D) Convert the dataset to a cuDF DataFrame and use cudf.DataFrame.fillna() to fill missing values.

2. You are analyzing a large-scale transportation network using cuGraph and notice that query times are longer than expected when running graph algorithms.
What is the best way to optimize graph processing performance using GPU-accelerated tools?

A) Use cugraph.filter_unconnected_nodes() to remove unconnected nodes before processing.
B) Convert the graph to CSR (Compressed Sparse Row) format before running computations to improve memory efficiency.
C) Use cugraph.to_directed() to convert the graph into a directed format, which improves GPU parallelism.
D) Store the graph in COO (Coordinate List) format instead of CSR (Compressed Sparse Row) format for faster traversal.

3. Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A) Log Transformation
B) Min-Max Scaling
C) Z-score Standardization
D) Robust Scaling

4. A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of- memory (OOM) errors.
To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?

A) Increasing batch size without adjusting the optimizer settings
B) Using mixed precision training with automatic loss scaling
C) Storing all training data in GPU memory at once
D) Using single-precision (FP32) calculations for better accuracy

5. You are working with a large-scale social network dataset and need to analyze relationships between users to detect communities using the Louvain algorithm. Given the benefits of GPU acceleration, you decide to use cuGraph for this task.
Which of the following statements best describes why cuGraph is beneficial for this workload?

A) Running graph algorithms on a GPU leads to higher latency due to the overhead of data movement between CPU and GPU.
B) cuGraph enables parallel computation on the GPU, significantly speeding up community detection tasks compared to traditional CPU-based implementations.
C) cuGraph primarily accelerates deep learning tasks and is not optimized for graph-based algorithms.
D) cuGraph does not support weighted graphs, making it less useful for real-world social network analysis.

Solutions: