NVIDIA NVIDIA-Certified Professional NCP-ADS

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A research team is analyzing a large transportation network and wants to identify groups of locations that are closely connected based on travel routes.
Which NVIDIA cuGraph function would be the most effective for this task?

A) Apply cuGraph's bfs() (Breadth-First Search) to find clusters of locations.
B) Use cuGraph's shortest_path() to compute all shortest paths and derive clusters manually.
C) Use cuGraph's louvain() function to detect communities within the graph.
D) Use cuGraph's pagerank() to rank the most connected locations.

2. A machine learning team needs to process terabytes of image metadata stored in a distributed storage system. They want to leverage GPU acceleration to speed up preprocessing and transformation while ensuring efficient parallel access.
Which of the following approaches best aligns with NVIDIA's accelerated data science ecosystem?

A) Store metadata in a traditional SQL database and query it using Pandas.
B) Use Dask-cuDF with Parquet files stored in an object storage system like S3.
C) Process metadata using PySpark on a CPU cluster before sending it to a GPU.
D) Use SQLite for metadata storage and process it with RAPIDS cuDF.

3. You are training a convolutional neural network (CNN) model with a large dataset on a single GPU.
The model is consuming too much GPU memory, and training is slow.
Which of the following techniques would help you reduce GPU memory consumption while maintaining or improving the efficiency of training? (Select two)

A) Use batch normalization to reduce memory usage by skipping some of the computations during training.
B) Use a smaller batch size to reduce the memory requirements per training iteration, allowing for faster training.
C) Decrease the size of the model by reducing the number of layers or the number of filters per layer.
D) Implement mixed precision training to lower memory usage and speed up computation without losing accuracy.
E) Use automatic differentiation for every operation to save memory during the backpropagation phase.

4. You are processing a large dataset using NVIDIA Dask-cuDF to distribute GPU-accelerated computation across multiple nodes. Users report inconsistent execution times, with some jobs taking significantly longer than expected.
Which of the following actions would best help diagnose the performance bottleneck?

A) Switch to using Pandas with Dask to compare execution speed differences.
B) Use Dask's dashboard and NVTX markers to analyze task execution times and GPU utilization.
C) Limit GPU memory usage to force more frequent spilling to disk and observe performance differences.
D) Reduce the number of Dask workers to minimize parallel execution overhead.

5. You are performing data cleansing on a large dataset using CuDF. The dataset contains numerical values, some of which are outliers. You need to remove or adjust these outliers to make your model training more robust.
Which of the following approaches should you consider for handling outliers efficiently in CuDF? (Select two)

A) Using applymap() to apply a custom function for handling outliers
B) Using clip() to set a maximum and minimum threshold for numerical values
C) Using dropna() to remove rows with outliers
D) Using quantile() to calculate the interquartile range (IQR) and filter out outliers

Solutions: