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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. A data scientist is preparing a dataset containing numerical features with varying scales and distributions. Some features range from 0 to 1, while others have values ranging from -5000 to 5000. The dataset will be used in a machine learning model that relies on gradient-based optimization.
What is the best approach to standardizing the data to ensure uniformity across features?

A) Use min-max scaling to transform all features into the range [0,1]
B) Normalize the dataset by dividing each feature by its maximum absolute value
C) Keep the features as they are since machine learning models can handle varying scales naturally
D) Apply z-score normalization (standardization) to scale features to have a mean of 0 and a standard deviation of 1

3. You are working on a large-scale graph analysis project using NVIDIA cuGraph for accelerated computations. Your dataset consists of millions of nodes and edges representing social network interactions. You need to efficiently compute PageRank while minimizing memory usage.
Which of the following techniques would be the most effective?

A) Use Pandas DataFrames to manage the graph structure and leverage Pandas indexing for efficient queries.
B) Manually unroll loops and precompute node ranks in a NumPy array before feeding them into cuGraph.
C) Convert the graph into an adjacency list format and store it in Python dictionaries for faster lookups.
D) Use cuGraph's sparse matrix representation to store graph data and perform computations efficiently.

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

5. You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

A) Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
B) Ensuring the ETL pipeline uses only batch processing for data ingestion.
C) Using a single storage location for both raw and transformed data to simplify the workflow.
D) Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.
E) Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.

Solutions: