NVIDIA NCP-ADS Actual Free Exam Questions & Community Discussion

You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?

A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of- memory (OOM) errors.
To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?

Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?

You are developing an AI model for medical imaging that requires acquiring a large dataset of MRI scans from multiple sources.
Which NVIDIA technology would best assist in acquiring, standardizing, and efficiently handling the dataset?

You are tasked with selecting the optimal data processing library for an AI project that involves handling varying dataset sizes. The project must be flexible enough to scale from small datasets (a few GBs) to large datasets (hundreds of GBs or more) using NVIDIA technologies.
Which of the following libraries would you choose for optimal performance at both small and large scales?

You have a multi-GPU cluster running RAPIDS with Dask to process a large dataset stored in Apache Parquet format. During execution, you notice some GPUs are underutilized, while others are overloaded, leading to uneven processing times.
What is the most effective way to balance the workload across GPUs?

You are designing an ETL pipeline to process terabytes of financial transaction data in real time.
The pipeline consists of:
Extracting data from multiple sources (CSV, Parquet, and SQL databases), Transforming the data using operations such as filtering, joins, and aggregations, Loading the processed data into a data lake for analytics.
Given that you are using NVIDIA RAPIDS cuDF for GPU-accelerated ETL, which of the following approaches optimizes performance while ensuring scalability?

A data engineering team is designing an ETL pipeline to process large-scale financial transaction data. They want to leverage NVIDIA-accelerated ETL tools to extract data from a data lake, transform it by filtering and aggregating key fields, and load it into a data warehouse.
Which of the following approaches provides the most efficient ETL processing using NVIDIA technologies?

You need to train a deep learning model using PyTorch on a dataset too large for a single GPU. You decide to use Dask with NVIDIA GPUs for multi-GPU scaling.
Which approach is the most effective for distributing the workload?

You are comparing the performance of NVIDIA RAPIDS cuML, TensorFlow, and PyTorch for training and inference on a dataset with millions of records.
To design a fair and effective benchmark, which approach should you take?

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?