NVIDIA NCP-ADS Exam : NVIDIA-Certified-Professional Accelerated Data Science

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data science team is deploying a deep learning model for real-time inference. The model is optimized for inference on an NVIDIA A100 GPU, but the team notices that inference latency is higher than expected.
Which of the following optimizations is most effective in reducing inference latency?

A) Reduce the model size by randomly pruning neurons without retraining.
B) Use mixed-precision inference with TensorRT to accelerate computation.
C) Increase the batch size significantly to improve GPU utilization.
D) Enable CPU offloading to balance the workload between the CPU and GPU.

2. You are working on a large-scale graph analysis problem that involves computing the shortest paths between nodes in a massive social network dataset. You decide to leverage NVIDIA RAPIDS cuGraph for accelerated computation.
Which of the following cuGraph functions should you use?

A) cugraph.k_truss()
B) cugraph.label_propagation()
C) cugraph.pagerank()
D) cugraph.sssp()

3. You are working on a medium-sized dataset (~500,000 rows, 20 columns) and need to perform fast exploratory data analysis (EDA) with filtering, aggregations, and transformations.
Which of the following Python libraries would be the most efficient choice for this task?

A) PySpark
B) Vaex
C) Pandas
D) Dask

4. Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?

A) Visualize GPU memory utilization over time
B) Increase batch size to improve accuracy
C) Modify the training dataset during model execution
D) Automatically adjust the learning rate based on the model's convergence

5. You are tasked with optimizing an ETL pipeline that processes petabytes of data daily. Your organization is already using Apache Spark for distributed data processing but is experiencing performance bottlenecks. You need a solution that improves execution speed without requiring extensive code modifications.
Which of the following solutions best meets your needs?

A) Using the NVIDIA Spark RAPIDS Accelerator to run Spark workloads on GPUs
B) Rewriting the entire ETL pipeline in CUDA to maximize GPU efficiency
C) Using Apache Arrow to optimize in-memory columnar processing within Spark
D) Switching to Dask and RAPIDS to fully utilize GPU acceleration

Solutions: