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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A financial analyst is working with an irregularly spaced time-series dataset containing cryptocurrency transactions. The timestamps are not evenly distributed, with some periods having dense data and others having sparse entries. The analyst wants to visualize the data efficiently using GPU acceleration.
What is the best preprocessing approach before visualization?

A) Resample the time-series to a fixed frequency using cuDF.resample() and fill missing values.
B) Ignore the irregularity and plot the raw timestamps directly without any preprocessing.
C) Convert the dataset to Pandas and use df.resample() to aggregate by fixed time intervals.
D) Sort the data by timestamp and drop all sparse regions using df.dropna().

2. You are developing an accelerated ETL workflow that requires data transformations such as filtering, aggregating, and joining large datasets. You decide to leverage NVIDIA GPUs to accelerate the transformation phase of your ETL pipeline.
Which of the following approaches will provide the greatest performance improvements when working with large-scale tabular datasets?

A) Using TensorFlow for data transformation tasks
B) Performing transformations using SQL-based queries on CPU
C) Using RAPIDS cuDF to perform transformations on a GPU
D) Relying on traditional pandas for in-memory transformations

3. You are working with a large dataset in a GPU-accelerated environment, and one of the columns, revenue, contains numeric values representing the annual revenue for companies. The revenue values are in the billions of dollars.
Which of the following is the most memory-efficient data type for the revenue column in a cuDF DataFrame?

A) df['revenue'] = df['revenue'].astype('float32')
B) df['revenue'] = df['revenue'].astype('int64')
C) df['revenue'] = df['revenue'].astype('float64')
D) df['revenue'] = df['revenue'].astype('uint32')

4. A data scientist is working with a large dataset for a machine learning model and wants to accelerate feature engineering using a GPU.
Which of the following approaches will provide the most significant performance boost when using GPU acceleration?

A) Using traditional pandas DataFrames and NumPy operations optimized for CPU processing.
B) Using RAPIDS cuDF and cuML libraries to perform feature transformations on a GPU.
C) Reducing dataset size by randomly removing data points without considering class balance.
D) Using a single-threaded feature extraction approach to avoid overhead from parallelization.

5. A data scientist is using NVIDIA RAPIDS cuDF to process a large dataset of customer transactions.
The dataset contains numerical, categorical, and timestamp-based features.
To optimize memory usage and performance on NVIDIA GPUs, which approach should they take when selecting data types?

A) Store all numerical columns as float64 to preserve maximum precision, even if lower precision suffices.
B) Convert categorical variables into cuDF categorical data types and downcast numerical columns to the smallest possible precision without losing information.
C) Avoid downcasting integer columns, as lower-bit integer types (e.g., int8) are not supported in GPU- accelerated computations.
D) Convert all timestamp features into object (string) format to maintain readability and ensure compatibility with GPU processing.

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