NVIDIA NCP-ADS real exam prep : NVIDIA-Certified-Professional Accelerated Data Science

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. Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining
• D. Convert both DataFrames to Pandas before performing the join for compatibility

You are processing large-scale datasets in Dask-cuDF and observe that your computation involves excessive data shuffling, which slows down performance. You decide to implement data caching to reduce shuffle overhead.
Which of the following best describes a technique to reduce shuffle costs in a Dask-cuDF workflow?

• A. Splitting the DataFrame into multiple smaller DataFrames and recomputing each separately reduces shuffle operations in Dask-cuDF.
• B. Persisting intermediate results in distributed GPU memory using df.persist() ensures that Dask does not recompute partitions, reducing shuffle overhead.
• C. Disabling lazy execution using dask.config.set(lazy=False) forces all computations to execute eagerly, thereby avoiding shuffle.
• D. Using .compute() on each partition immediately forces Dask to materialize results and store them in memory, ensuring shuffle operations are avoided.

A data engineer is designing an Extract, Transform, Load (ETL) pipeline for a retail analytics platform that processes millions of customer transactions per day. The primary objective is to accelerate data ingestion, transformation, and storage while ensuring efficient scalability.
Which of the following approaches would be the most effective for optimizing this ETL workflow using NVIDIA-accelerated ETL tools?

• A. Use NVIDIA RAPIDS cuDF for data transformations and Dask-cuDF for parallelized processing across multiple GPUs.
• B. Implement ETL processes using only SQL-based transformations within a relational database system.
• C. Use Apache Spark with CPU-based processing instead of leveraging GPU acceleration.
• D. Perform all transformations using Pandas DataFrames and then use multiprocessing to parallelize the workload on CPUs.

A machine learning engineer is benchmarking a GPU-accelerated pipeline for data preprocessing and training. The pipeline consists of large-scale data transformations using RAPIDS cuDF and training a model with TensorFlow. The engineer wants to ensure that GPU utilization is maximized throughout the workflow.
Which approach is most effective in achieving this?

• A. Reduce the dataset size to fit entirely in GPU memory, even if it leads to data loss.
• B. Pipeline execution should be asynchronous, overlapping data preprocessing with model training.
• C. Precompute all features on the CPU before loading them into the GPU for training.
• D. Use a single GPU to avoid overhead associated with multi-GPU synchronization.

You are tasked with selecting the optimal data processing library for an AI project that involves handling varying dataset sizes. The project must be flexible enough to scale from small datasets (a few GBs) to large datasets (hundreds of GBs or more) using NVIDIA technologies.
Which of the following libraries would you choose for optimal performance at both small and large scales?

• A. cuDF
• B. Dask
• C. PyTorch
• D. CUDA Toolkit