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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You need to deploy a machine learning model on a GPU-equipped system. The GPU has 16GB of VRAM, and the model requires approximately 12GB of memory during inference. However, additional system processes and other applications consume 5GB of VRAM.
What would happen if you attempt to run inference without making any optimizations, and how should you resolve the issue?

A) Switching from a GPU to CPU inference will resolve memory issues without performance loss
B) The model will fail to run due to out-of-memory (OOM) errors, and using a smaller batch size can help reduce memory usage
C) The model will run without issues because 16GB of VRAM is sufficient for a 12GB model
D) The model will run successfully but with reduced performance due to memory fragmentation

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) Convert both DataFrames to Pandas before performing the join for compatibility
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) Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF

3. You are training a deep learning model on a large dataset and are deciding whether to use a single GPU or multiple GPUs.
Which of the following are true considerations when comparing single-GPU and multi-GPU training setups? (Select two)

A) Multi-GPU training requires modifications to the model architecture to make it compatible with parallel processing.
B) Single-GPU training is generally more cost-effective and should be preferred unless scaling is absolutely necessary.
C) Multi-GPU training can significantly reduce training time when the dataset is large and the model is computationally intensive.
D) Single-GPU training is limited by the VRAM (video memory) on the GPU, so larger models or datasets may require multi-GPU setups.
E) Multi-GPU setups perform better only when the batch size is reduced.

4. You are tasked with processing a large dataset using multiple GPUs to accelerate computation. You decide to use Dask to implement data parallelism with NVIDIA's RAPIDS framework to maximize GPU utilization.
Which of the following steps is essential for efficiently distributing the workload across multiple GPUs in Dask?

A) Set up a single Dask dataframe without partitioning and rely on automatic workload balancing.
B) Manually allocate GPU memory using cupy for each worker instead of using Dask's scheduler.
C) Use dask_cuda.LocalCUDACluster() to create a multi-GPU cluster and dask.distributed.Client() to manage execution.
D) Use dask.dataframe.repartition() to distribute data evenly across multiple GPUs.

5. Your data science team is performing exploratory data analysis (EDA) on a large GPU-accelerated environment using cuDF and Dask-cuDF. During analysis, queries on categorical columns are performing poorly.
Which approach will most effectively improve query performance for categorical data in GPU-accelerated DataFrames?

A) Use CPU-based DataFrames (pandas) instead of GPU-based DataFrames
B) Increase GPU memory allocation
C) Convert categorical columns to integer codes
D) Store data in compressed file formats like Parquet

Solutions: