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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is working on training a deep learning model in a cloud-based environment. The dataset is large, and model convergence is taking too long on a standard CPU instance.
To optimize performance through GPU acceleration, which of the following strategies should the data scientist implement?

A) Store all training data in RAM and load it directly to the CPU for processing.
B) Use a cloud instance with multiple GPUs and enable mixed-precision training.
C) Disable CUDA and use only OpenMP to parallelize computations across CPU cores.
D) Increase the number of CPU cores and distribute training across multiple CPU threads.

2. Which of the following are key advantages of using cuGraph for analyzing graph data in GPU- accelerated environments? (Select two)

A) cuGraph does not support distributed graph processing and is only suitable for single-node systems.
B) cuGraph supports various graph algorithms, including PageRank, shortest path, and community detection, leveraging GPU parallelism.
C) cuGraph can efficiently handle larger graphs than traditional CPU-based methods, providing significant performance improvements.
D) cuGraph only works on small-scale graph datasets that can fit into memory.
E) cuGraph only works with cloud-based computing environments and is not optimized for local GPUs.

3. A data science team wants to leverage GPU acceleration for detecting anomalies in a massive IoT sensor dataset that is continuously streaming.
Which of the following NVIDIA-supported methods would be the best for handling real-time anomaly detection in a high-throughput environment?

A) Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.
B) Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.
C) Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.
D) Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.

4. Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?

A) NVIDIA Jetson
B) NVIDIA A100
C) TensorRT
D) NVIDIA Tesla

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

Solutions: