Prepare with latest NVIDIA NCP-ADS Pass-King Actual Torrent

NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. 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) cuGraph does not support weighted graphs, making it less useful for real-world social network analysis.
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) Running graph algorithms on a GPU leads to higher latency due to the overhead of data movement between CPU and GPU.

2. You have deployed a deep learning model for image classification in a production environment, but inference latency is high. You need to optimize the model to reduce response time while maintaining accuracy.
Which NVIDIA technology is best suited for this task?

A) NVIDIA Clara Imaging to improve deep learning inference for image classification workloads.
B) NVIDIA DeepStream to process image classification models for low-latency inference in batch mode.
C) NVIDIA TensorRT to optimize and accelerate deep learning inference by reducing model size and execution time.
D) NVIDIA RAPIDS cuML to optimize deep learning inference using GPU-accelerated ML algorithms.

3. You are training a machine learning model using scikit-learn-like API on a dataset with millions of samples and thousands of features. You need to optimize both training time and inference speed using NVIDIA technologies.
Which solution is the most appropriate?

A) Use NVIDIA Triton Inference Server to train the model efficiently on a single GPU.
B) Use NVIDIA RAPIDS cuML for GPU-accelerated machine learning model training.
C) Use NVIDIA Magnum IO to optimize machine learning model parameters on the GPU.
D) Use NVIDIA Modulus to accelerate machine learning training and feature selection.

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

5. You have a massive time-series dataset containing millions of records per day, and you need to perform forecasting at scale.
Which of the following techniques best utilizes NVIDIA technologies to optimize time-series forecasting?

A) Convert time-series data to tensors and train a deep learning model using PyTorch with mixed precision on an NVIDIA GPU.
B) Use RAPIDS cuML's XGBoost-GPU implementation to forecast time-series patterns with improved training efficiency.
C) Run Facebook Prophet on a multi-core CPU environment to leverage parallel forecasting.
D) Use RAPIDS cuDF and Dask-cuDF to preprocess the data and train an LSTM model with PyTorch Lightning on multiple GPUs.

Solutions: