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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?

A) Convert the dataset to a NumPy array and manually replace missing values with the mean
B) Apply a deep learning-based imputation model before moving data to the GPU
C) Drop all rows containing missing values using Pandas before transferring data to the GPU
D) Use fillna() with a fixed value on the GPU using cuDF

2. A data science team is developing a machine learning pipeline requiring specific CUDA, cuDNN, and RAPIDS versions for compatibility across environments. They need a framework to manage dependencies and version conflicts.
Which approach is best for managing software dependencies using NVIDIA technologies?

A) Manually installing each package and its dependencies using pip
B) Using a single system-wide installation of CUDA and forcing all projects to use the same version
C) Using only virtual environments (venv) without managing GPU dependencies separately
D) Using Conda with NVIDIA Conda channels to manage CUDA and cuDNN dependencies

3. You are processing a dataset of high-resolution images for deep learning training and want to optimize image loading, augmentation, and transformation on an NVIDIA GPU.
Which of the following statements correctly describes the role of NVIDIA DALI (Data Loading Library) in accelerating data preparation?

A) NVIDIA DALI is an alternative to cuDF for GPU-accelerated tabular data processing.
B) NVIDIA DALI can offload image preprocessing tasks to the GPU, reducing CPU bottlenecks and improving training throughput.
C) NVIDIA DALI does not support data augmentation and is only used for raw data loading.
D) NVIDIA DALI only supports preprocessing images and does not include any functionality for video or text data.

4. A data engineer is tasked with processing a 5 TB dataset stored in Apache Parquet format. The dataset consists of user activity logs and needs to be filtered, aggregated, and processed for feature engineering before training an ML model. The engineer is deciding between Dask and Apache Spark.
Which statement best describes a key difference between the two frameworks?

A) Spark is better suited for structured and semi-structured data, while Dask excels at unstructured data processing.
B) Spark cannot utilize GPUs, whereas Dask has built-in GPU acceleration by default.
C) Dask is optimized for in-memory processing, while Spark requires disk-based storage for computation.
D) Dask is a more lightweight solution, often preferred for Python-centric workflows, whereas Spark provides broader ecosystem integrations and supports SQL-like operations natively.

5. You are working with a 10-terabyte dataset containing structured and unstructured data. Your goal is to perform ETL (Extract, Transform, Load) operations efficiently while leveraging GPU acceleration for distributed processing.
Which of the following frameworks would be the best choice for handling this workload?

A) Hadoop MapReduce
B) Apache Spark with its default CPU-based execution
C) RAPIDS + Dask for distributed GPU-accelerated ETL
D) Pandas with multiprocessing

Solutions: