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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A) Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
B) Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
C) Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.
D) Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.

2. You are working on a large dataset for a machine learning model that will be trained using RAPIDS cuML. The dataset includes categorical, integer, and floating-point features.
Which of the following approaches is the best practice for determining the optimal data type choice for each feature using NVIDIA's RAPIDS cuDF library?

A) Convert all numerical data to float64 for maximum precision in calculations.
B) Convert categorical variables into int8 to optimize GPU memory usage.
C) Use float16 for all floating-point data to reduce memory usage and increase GPU processing speed.
D) Use float32 instead of float64 for floating-point numbers when possible, and leverage int8, int16, or int32 for categorical and integer data based on their range.

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

4. You have deployed a deep learning model for image classification in a production environment, but inference latency is high. You need to optimize the model to reduce response time while maintaining accuracy.
Which NVIDIA technology is best suited for this task?

A) NVIDIA TensorRT to optimize and accelerate deep learning inference by reducing model size and execution time.
B) NVIDIA DeepStream to process image classification models for low-latency inference in batch mode.
C) NVIDIA RAPIDS cuML to optimize deep learning inference using GPU-accelerated ML algorithms.
D) NVIDIA Clara Imaging to improve deep learning inference for image classification workloads.

5. You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?

A) NVIDIA Nsight Systems for managing dataset storage and retrieval performance.
B) NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.
C) NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.
D) NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.

Solutions: