NVIDIA NCP-ADS

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. When scaling data parallelism using Dask with multiple Nvidia GPUs, what is the key consideration to avoid memory issues when distributing large datasets?

A) Ensure that each GPU's memory usage is manually monitored and adjusted, as Dask does not manage memory allocation automatically across GPUs.
B) Split the dataset into smaller partitions that fit into each GPU's memory to prevent out-of-memory errors, and let Dask manage data distribution.
C) Allow Dask to allocate data chunks dynamically without partitioning the dataset first, letting the system handle memory distribution automatically.
D) Use dask_gpu instead of dask_cuda to manage memory automatically across GPUs.

2. You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A) Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.
B) Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
C) Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
D) Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.

3. Which of the following best describes the purpose of the NVIDIA TensorRT library?

A) Optimizes and accelerates inference of trained models
B) Accelerates training of neural networks
C) Manages GPU resources for deep learning models
D) Provides hardware abstraction for AI model development

4. A financial institution is developing an ETL pipeline to ingest and process large volumes of streaming data from various sources, including stock market feeds, real-time transactions, and economic indicators. The ETL process must be highly efficient to minimize latency while ensuring data integrity.
Which of the following strategies is best suited for implementing a high-performance, GPU-accelerated ETL pipeline?

A) Utilize NVIDIA Morpheus with RAPIDS to preprocess real-time streaming data using GPU acceleration.
B) Store all streaming data in a PostgreSQL database before performing batch transformations.
C) Load data directly into an Excel spreadsheet and use VBA macros to clean and transform it.
D) Use Pandas and Python's built-in threading library to handle concurrent data ingestion and transformation.

5. 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) df = (df - df.mean()) / df.std()
B) df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)
C) 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)
D) df = (df - df.min()) / (df.max() - df.min())

Solutions: