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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following tools in the NVIDIA AI stack is specifically designed to accelerate the deployment of machine learning models for production by optimizing inference performance?

A) Triton Inference Server
B) DLA (Deep Learning Accelerator)
C) cuML
D) cuBLAS

2. You are working on a data processing pipeline using NVIDIA GPUs for accelerating computations.
You need to monitor the pipeline's performance to identify bottlenecks.
Which of the following tools or techniques can be used to efficiently recognize bottlenecks in such a GPU-accelerated pipeline? (Select two)

A) NVIDIA TensorRT Profiling
B) NVIDIA nvidia-smi
C) NVIDIA DLA (Deep Learning Accelerator)
D) NVIDIA Nsight Systems
E) NVIDIA CUDA Profiler (nvprof)

3. You are building a deep learning model using TensorFlow with cuDNN acceleration on an NVIDIA GPU. Your dataset contains continuous numerical features with vastly different ranges.
What is the best way to standardize the data efficiently to improve model convergence?

A) Use cuml.StandardScaler() from RAPIDS to normalize the dataset before feeding it into the model.
B) Apply batch normalization layers in the neural network to handle feature scaling dynamically during training.
C) Use cupy.linalg.norm() to normalize each feature vector individually to unit length.
D) Manually compute the feature mean and variance on the CPU and apply the transformation before training.

4. You are working on a large dataset (several terabytes in size) and need to perform data preprocessing, filtering, and transformations before training a machine learning model.
Given the dataset size and the requirement to optimize for GPU acceleration using NVIDIA technologies, which of the following is the most appropriate data processing library to use?

A) Modin with Ray backend
B) pandas
C) NumPy with CuPy acceleration
D) Dask DataFrame with Dask-CUDA

5. You are working on a large-scale machine learning project that requires preprocessing terabytes of structured and semi-structured data. You need a distributed data processing framework that can leverage NVIDIA GPUs efficiently to accelerate computations.
Which of the following approaches would best achieve this goal?

A) Using TensorFlow's Dataset API to load and preprocess the data on GPUs
B) Using Apache Spark with PySpark for CPU-based distributed data processing
C) Using Dask with RAPIDS cuDF and cuML for distributed GPU-accelerated processing
D) Using plain NumPy with CUDA extensions to manually parallelize computations across multiple GPUs

Solutions: