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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following best describes the role of MLOps in the context of NVIDIA technologies for deploying machine learning models in production? (Select two)

A) MLOps helps manage the lifecycle of machine learning models, ensuring efficient collaboration and model governance
B) MLOps ensures that models trained on GPUs can only run on GPUs during deployment
C) MLOps replaces the need for data preprocessing during training and deployment
D) MLOps frameworks support version control and automation, ensuring reproducibility and scalability of ML workflows

2. A financial institution is training a fraud detection model on a GPU-powered NVIDIA RAPIDS cuML pipeline. Their dataset includes customer ages, transaction amounts, merchant names, and transaction timestamps.
To optimize GPU memory usage while preserving accuracy, how should they store these features?

A) Keep customer ages in float16 format to reduce memory consumption, as integer types are less efficient in GPU operations.
B) Store transaction amounts as float32, customer ages as int8, merchant names as categorical, and timestamps as datetime64.
C) Convert all numeric data to float64 for maximum precision, as rounding errors in lower precision could impact the model's accuracy.
D) Convert timestamps into UNIX epoch integers instead of using datetime64 for more efficient GPU computations.

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

4. 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) Convert the dataset to Pandas and use df.resample() to aggregate by fixed time intervals.
C) Sort the data by timestamp and drop all sparse regions using df.dropna().
D) Ignore the irregularity and plot the raw timestamps directly without any preprocessing.

5. A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A) Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
B) Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
C) Manually generate random values using Python's built-in random module and load them into a cuDF DataFrame.
D) Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.

Solutions: