Prepare with NVIDIA NCP-ADS exam torrent, pass for sure

NVIDIA NCP-ADS Exam Overview:

NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are analyzing a large financial dataset containing stock market tick-by-tick data stored in a cuDF DataFrame. Since the dataset contains billions of data points, you need to aggregate it at the minute level before visualizing price trends efficiently.
Which of the following is the best approach for aggregating and visualizing this time-series data using NVIDIA technologies?

A) Convert cuDF to Pandas, aggregate using .resample() in Pandas, and visualize using Matplotlib
B) Use cuDF's .groupby() function to aggregate at the minute level, then visualize using hvPlot
C) Load the data into a relational database (e.g., PostgreSQL), run an SQL query for aggregation, and visualize using Seaborn
D) Use cuML's TSNE function to reduce dimensionality before visualizing with Bokeh

2. You are tasked with profiling a PyTorch-based deep learning model to identify performance bottlenecks using NVIDIA DLProf. Your goal is to analyze kernel execution times and identify operations causing excessive memory consumption.
Which of the following steps is the MOST appropriate sequence for profiling using DLProf?

A) Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
B) Execute the training script under DLProf TensorBoard mode to visualize performance insights, then re-run the model with automatic mixed precision (AMP) to reduce memory usage.
C) Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.
D) Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.

3. You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

A) Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations
B) Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence
C) Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold
D) Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format

4. You are working on a data processing pipeline using NVIDIA GPUs for accelerating computations.
You need to monitor the pipeline's performance to identify bottlenecks.
Which of the following tools or techniques can be used to efficiently recognize bottlenecks in such a GPU-accelerated pipeline? (Select two)

A) NVIDIA TensorRT Profiling
B) NVIDIA nvidia-smi
C) NVIDIA DLA (Deep Learning Accelerator)
D) NVIDIA CUDA Profiler (nvprof)
E) NVIDIA Nsight Systems

5. You are working on a large-scale machine learning project that requires preprocessing terabytes of structured and semi-structured data. You need a distributed data processing framework that can leverage NVIDIA GPUs efficiently to accelerate computations.
Which of the following approaches would best achieve this goal?

A) Using TensorFlow's Dataset API to load and preprocess the data on GPUs
B) Using Apache Spark with PySpark for CPU-based distributed data processing
C) Using Dask with RAPIDS cuDF and cuML for distributed GPU-accelerated processing
D) Using plain NumPy with CUDA extensions to manually parallelize computations across multiple GPUs

Solutions: