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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

You are working on a machine learning pipeline using NVIDIA RAPIDS cuML and need to standardize the dataset to ensure that all features have a mean of 0 and a standard deviation of 1.
Which of the following methods should you use to achieve this in cuML?

A. cuml.preprocessing.LabelEncoder()
B. cuml.preprocessing.StandardScaler()
C. cuml.preprocessing.OneHotEncoder()
D. cuml.preprocessing.MinMaxScaler()

Question 2

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?

A. Using mixed precision training with automatic loss scaling
B. Increasing batch size without adjusting the optimizer settings
C. Using single-precision (FP32) calculations for better accuracy
D. Storing all training data in GPU memory at once

Question 3

A data engineer is using cuDF in NVIDIA RAPIDS to generate a large synthetic dataset for machine learning training. The dataset consists of numerical and categorical features. The engineer needs to generate millions of rows efficiently while preserving the relationships between features.
Which of the following approaches is the most optimal?

A. Use cudf.to_pandas(), generate synthetic data using pandas and Scikit-learn, and then convert it back to cuDF.
B. Leverage cuML's PCA.inverse_transform() after fitting PCA to the original dataset to generate new synthetic samples.
C. Use cudf.Series.random() to create independent random values for each column separately.
D. Train a cuML KMeans model on the original data and use the cluster centroids as new synthetic data points.

Question 4

A machine learning engineer is working with a 1 TB dataset stored in Apache Parquet format and wants to analyze the data for patterns before building a model. The engineer is considering various acceleration methods.
Which of the following approaches would be the best choice for efficient analysis?

A. Load the dataset into a relational database and query it using simple SQL statements.
B. Use a GPU-accelerated library such as RAPIDS cuDF to load and process the Parquet file efficiently.
C. Convert the Parquet file to a Pandas DataFrame and perform analysis using Pandas functions.
D. Read the Parquet file line by line using Python's built-in file handling functions to save memory.

Question 5

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?

A. Switch to a CPU-based framework like Spark to distribute the load evenly
B. Split the dataset into smaller chunks manually and assign them to GPUs
C. Use Dask's adaptive scaling to dynamically adjust the number of GPU workers
D. Increase the number of worker threads per GPU manually

Solutions: