NVIDIA NCP-ADS test insides dumps : NVIDIA-Certified-Professional Accelerated Data Science

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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. Which of the following best describes the purpose of the NVIDIA TensorRT library?

A) Accelerates training of neural networks
B) Provides hardware abstraction for AI model development
C) Optimizes and accelerates inference of trained models
D) Manages GPU resources for deep learning models

2. When deciding whether to use GPU acceleration or a traditional CPU approach for a machine learning task, which of the following factors should be considered to determine if the data qualifies as "big data" and whether GPU acceleration is beneficial? (Select two)

A) CPU-based machine learning methods are always more effective for small datasets, regardless of the algorithm used.
B) The complexity of the algorithm being used plays a crucial role in deciding whether to use GPU acceleration, with more complex algorithms benefiting from parallel computation.
C) The size of the dataset in terms of rows and columns is irrelevant when determining if it qualifies as big data.
D) GPU acceleration is beneficial when the dataset can be divided into independent chunks that can be processed in parallel.
E) The dataset must be over 100GB in size to qualify as big data and warrant GPU acceleration.

3. 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

4. You are tasked with optimizing the performance of an MLOps pipeline that uses GPU-accelerated workflows. After running initial benchmarks, you notice that the training time is higher than expected, despite the use of multiple GPUs.
What are the best strategies to optimize the GPU-accelerated workflow in this case? (Select two)

A) Ensure efficient multi-GPU communication and synchronization strategies, such as using NCCL for distributed training.
B) Disable gradient accumulation when using multi-GPU setups to increase communication efficiency.
C) Increase the batch size to better utilize the multiple GPUs and reduce the number of updates to the model during training.
D) Ensure that the model is distributed evenly across GPUs to prevent some GPUs from being underutilized.
E) Reduce the number of GPUs used and focus on fine-tuning the hyperparameters for optimal performance on a single GPU.

5. You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
Which of the following is the most memory-efficient data type choice for this column?

A) df['item_id'] = df['item_id'].astype('int32')
B) df['item_id'] = df['item_id'].astype('int64')
C) df['item_id'] = df['item_id'].astype('string')
D) df['item_id'] = df['item_id'].astype('float64')

Solutions: