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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.
Which approach best utilizes NVIDIA technologies for efficient forecasting?

A) Use PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.
B) Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
C) Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.
D) Use cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.

2. You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?

A) Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF
B) Perform the join operation entirely in a CPU-based Spark environment for better stability
C) Convert both DataFrames to Pandas before performing the join for compatibility
D) Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining

3. You are building a fraud detection system and have a dataset that includes a feature representing transaction amounts. The values range from a few cents to several thousand dollars.
What is the most appropriate data type for this feature?

A) Categorical (grouping transactions into bins such as "low", "medium", "high").
B) Boolean (True for transactions above $100, False otherwise).
C) Floating-point (with precision to store cents or fractions of a cent).
D) Integer (rounding all transaction amounts to whole dollars).

4. You are training a large-scale random forest model on a dataset with millions of rows and hundreds of features. The training time is significantly high when using traditional CPU-based machine learning frameworks.
Which NVIDIA technology should you use to accelerate training while maintaining compatibility with common ML frameworks like scikit-learn?

A) NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.
B) NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.
C) NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.
D) NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.

5. You need to process a dataset containing 10 billion rows, applying complex transformations and aggregations. The dataset does not fit into RAM, and you need an efficient, scalable solution for parallel processing.
Which of the following is the best choice?

A) Dask
B) PySpark
C) Vaex
D) Pandas

Solutions: