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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are preprocessing a dataset using NVIDIA RAPIDS cuDF and need to handle missing values in the column temperature by replacing them with the column's median value.
Which of the following approaches correctly achieves this in an optimized manner?

A) df['temperature'].dropna(inplace=True)
B) df['temperature'].fillna(df['temperature'].median(), inplace=True)
C) 1. df['temperature'] = df['temperature'].map(2. lambda x: df['temperature'].median() if x is None else x
3.)
D) df['temperature'].fillna(df['temperature'].mean(), inplace=True)

2. You are optimizing a data pipeline for a large-scale machine learning project using NVIDIA RAPIDS and Apache Spark. The pipeline performs many expensive shuffle operations.
Which of the following is the most effective method to reduce shuffle and improve performance using NVIDIA technologies?

A) Use RAPIDS cuDF to repartition the data in the GPU memory after each shuffle.
B) Use RAPIDS cuDF to perform in-memory data processing on GPU before shuffling to avoid network communication.
C) Implement a custom shuffle partitioning scheme using NVIDIA DALI for more control over data partitioning during shuffle operations.
D) Store data in HDFS before performing shuffle operations to reduce GPU memory overhead.

3. A data scientist is working with a large dataset containing millions of records and aims to accelerate the data preprocessing workflow using NVIDIA technologies.
Which of the following approaches is the most effective for optimizing data preprocessing performance using GPUs?

A) Using RAPIDS cuDF to replace pandas operations
B) Running data transformations in a multi-threaded CPU environment
C) Using NumPy for array computations and parallelizing with Python's multiprocessing
D) Using Dask to distribute processing across multiple CPU cores

4. You need to deploy a machine learning model on a GPU-equipped system. The GPU has 16GB of VRAM, and the model requires approximately 12GB of memory during inference. However, additional system processes and other applications consume 5GB of VRAM.
What would happen if you attempt to run inference without making any optimizations, and how should you resolve the issue?

A) Switching from a GPU to CPU inference will resolve memory issues without performance loss
B) The model will run successfully but with reduced performance due to memory fragmentation
C) The model will fail to run due to out-of-memory (OOM) errors, and using a smaller batch size can help reduce memory usage
D) The model will run without issues because 16GB of VRAM is sufficient for a 12GB model

5. A machine learning team needs to process terabytes of image metadata stored in a distributed storage system. They want to leverage GPU acceleration to speed up preprocessing and transformation while ensuring efficient parallel access.
Which of the following approaches best aligns with NVIDIA's accelerated data science ecosystem?

A) Use Dask-cuDF with Parquet files stored in an object storage system like S3.
B) Process metadata using PySpark on a CPU cluster before sending it to a GPU.
C) Store metadata in a traditional SQL database and query it using Pandas.
D) Use SQLite for metadata storage and process it with RAPIDS cuDF.

Solutions: