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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. 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) Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining
C) Perform the join operation entirely in a CPU-based Spark environment for better stability
D) Convert both DataFrames to Pandas before performing the join for compatibility

2. Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?

A) NVIDIA Jetson
B) TensorRT
C) NVIDIA A100
D) NVIDIA Tesla

3. A data engineering team is designing an ETL pipeline to process large-scale financial transaction data. They want to leverage NVIDIA-accelerated ETL tools to extract data from a data lake, transform it by filtering and aggregating key fields, and load it into a data warehouse.
Which of the following approaches provides the most efficient ETL processing using NVIDIA technologies?

A) Use RAPIDS cuDF to preprocess data in-memory and BlazingSQL to accelerate SQL-based transformations
B) Use Dask on CPUs for distributed ETL processing and later move results to a GPU-based database
C) Write a custom ETL script in pure Python to handle data extraction, transformation, and loading
D) Perform all transformations using Pandas DataFrames before loading the data into the GPU

4. A company is deploying an MLOps pipeline for training and serving deep learning models. The data scientists want to leverage GPU acceleration at multiple stages of the pipeline to enhance efficiency.
Which of the following steps would benefit the most from GPU acceleration?

A) Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.
B) Storing and retrieving models from a centralized object storage system.
C) Model monitoring by logging metadata and performance metrics in a database.
D) Training and inference workloads using deep learning models with TensorFlow or PyTorch.

5. You have trained a machine learning model using cuML as part of the Modeling phase in the CRISP- DM framework. Now, you need to assess how well the model performs before moving forward with deployment.
Which of the following steps aligns best with the Evaluation phase of CRISP-DM using NVIDIA technologies?

A) Deploy the model to an edge device using TensorRT for real-time inference.
B) Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.
C) Define the problem statement and collect relevant datasets before training the model.
D) Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().

Solutions: