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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. A data scientist is using NVIDIA RAPIDS to perform statistical analysis as part of exploratory data analysis (EDA) on a dataset containing millions of product reviews. They need to compute basic descriptive statistics such as mean, median, and variance efficiently.
Which of the following methods is the most appropriate for performing these calculations on GPUs?

A) Use a traditional SQL database to compute statistics and then transfer results to the GPU
B) Use NumPy's statistical functions, such as numpy.mean() and numpy.var()
C) Use cuDF's built-in statistical functions like .mean(), .median(), and .var()
D) Convert the dataset into a PyTorch tensor and use PyTorch's statistical methods

2. A data science team wants to deploy a GPU-accelerated pipeline using cuGraph to analyze graph data on cloud infrastructure. They are evaluating different cloud-based GPU solutions.
Which of the following factors should they consider when selecting a cloud-based GPU instance for running cuGraph efficiently?

A) cuGraph runs equally well on CPU-based virtual machines, making GPU instances unnecessary.
B) Cloud-based GPUs are only useful for rendering graphics, not for running cuGraph algorithms.
C) The choice of GPU instance does not affect cuGraph performance since all GPUs execute graph algorithms at the same speed.
D) The availability of NVIDIA CUDA-enabled GPUs, as cuGraph requires CUDA for acceleration.

3. You are deploying a deep learning model on an edge device with 8GB of available RAM. The model's estimated peak memory usage, including model weights, intermediate tensors, and batch data, is 9.5GB.
What is the best course of action to ensure successful deployment while maintaining performance?

A) Offload some computation to cloud-based processing
B) Increase the device's swap space to compensate for insufficient RAM
C) Reduce the batch size during inference
D) Reduce the number of model parameters by removing layers from the architecture

4. A financial services company is deploying an AI-driven risk assessment model using NVIDIA GPUs on a cloud platform. To optimize resource utilization and cost efficiency, they need to determine the best GPU deployment strategy.
Which of the following is the most effective approach?

A) Deploy separate full-GPU instances for each workload, even if they have variable compute demands.
B) Choose an on-demand cloud instance with an outdated GPU model to reduce costs, even if performance is compromised.
C) Run all AI workloads on a single large GPU instance without any partitioning or workload separation.
D) Deploy the model using NVIDIAAI Enterprise with MIG (Multi-Instance GPU) to allocate multiple workloads on a single GPU efficiently.

5. A data scientist is training a deep learning model on an NVIDIA GPU and wants to profile the model to identify performance bottlenecks. The scientist chooses to use NVIDIA DLProf.
Which of the following steps is the most effective way to profile the model using DLProf?

A) Run the model using dlprof --mode profile to collect performance metrics and generate a report.
B) Rely on general CPU profiling tools like perf and gprof to analyze GPU performance.
C) Modify the training script to manually insert timing functions for each layer and compare execution times.
D) Use nvprof instead of DLProf since it provides more detailed profiling for deep learning workloads.

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