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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following is the main advantage of using TensorRT for inference in an accelerated data science pipeline?

A) TensorRT automatically builds training models from raw data without requiring pre-trained models.
B) TensorRT optimizes deep learning models to run efficiently on NVIDIA GPUs by reducing precision while maintaining accuracy.
C) TensorRT is mainly used for data visualization and not for model inference.
D) TensorRT is only compatible with image classification models and does not support other model types.

2. You are analyzing a dataset that contains missing values.
Which of the following techniques is most appropriate when dealing with missing numerical data in a dataset, ensuring minimal impact on model performance?

A) Replacing missing values with the mean of the column
B) Replacing missing values with a constant value (e.g., zero)
C) Removing rows with missing values
D) Using k-nearest neighbors (KNN) imputation

3. 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

4. You are managing a data processing pipeline that utilizes NVIDIA RAPIDS on GPUs for accelerated data transformations. During execution, you notice that the pipeline is not achieving expected performance gains.
What is the most effective approach to monitor and diagnose bottlenecks in this pipeline using NVIDIA technologies?

A) Reduce the dataset size and rerun the pipeline without profiling tools to check for performance improvements.
B) Run the pipeline on CPU instead of GPU to compare execution times.
C) Use NVIDIA Nsight Systems to profile kernel execution times and memory transfers.
D) Enable RAPIDS memory pool logging to check for memory fragmentation and out-of-memory errors.

5. You need to train a deep learning model using PyTorch on a dataset too large for a single GPU. You decide to use Dask with NVIDIA GPUs for multi-GPU scaling.
Which approach is the most effective for distributing the workload?

A) Use Dask.delayed to wrap PyTorch training functions and schedule them across multiple GPUs
B) Use Dask's built-in deep learning API to automatically distribute PyTorch models across GPUs
C) Use Dask-CUDA workers with PyTorch's DistributedDataParallel (DDP) for training across multiple GPUs
D) Use Dask Bag to shard the dataset and train separate PyTorch models on each shard

Solutions: