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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are optimizing a deep learning model that runs on an NVIDIA GPU and notice that inference latency is unexpectedly high. You decide to use DLProf to analyze the model's execution profile. After running the profiler, you find that a significant portion of execution time is spent on a single GPU kernel.
Which of the following actions would best help you identify and optimize this performance bottleneck?

A) Reduce the batch size to minimize the time spent on memory-bound operations and improve kernel efficiency.
B) Switch to a CPU-based execution environment, as it will eliminate any potential GPU bottlenecks.
C) Use DLProf's Tensor Core Analysis feature to determine if Tensor Cores are being utilized effectively.
D) Modify the neural network architecture to use more convolutional layers, as this generally improves execution speed on NVIDIA GPUs.

2. You are training a large-scale random forest model on a dataset with millions of rows and hundreds of features. The training time is significantly high when using traditional CPU-based machine learning frameworks.
Which NVIDIA technology should you use to accelerate training while maintaining compatibility with common ML frameworks like scikit-learn?

A) NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.
B) NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.
C) NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.
D) NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.

3. Which of the following statements best describes the role of GPUs in accelerating data science workloads?

A) GPUs are designed primarily for rendering graphics and have limited utility in machine learning and deep learning applications.
B) GPUs are only effective for acceleration when used in conjunction with Tensor Processing Units (TPUs), as they cannot train deep learning models independently.
C) GPUs are optimized for sequential data processing tasks, making them more efficient than CPUs for database operations.
D) GPUs use thousands of smaller cores that can execute many parallel computations simultaneously, making them ideal for large-scale matrix operations.

4. Which of the following is the most efficient way to implement data parallelism using Dask for multi- GPU scaling on an Nvidia platform?

A) Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.
B) Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.
C) Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.
D) Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.

5. You are processing a large dataset using RAPIDS cuDF and Dask-cuDF on an NVIDIA GPU. Your profiling indicates that data transfer times between CPU and GPU are significantly slowing down your pipeline.
What is the most effective way to reduce this bottleneck?

A) Transfer data in multiple smaller chunks to the GPU instead of larger batches
B) Increase the CPU RAM allocation to store more data before transferring to the GPU
C) Convert the dataset into a CSV format before transferring it to the GPU
D) Use cudf.read_parquet() instead of Pandas to load data directly into GPU memory

Solutions: