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NVIDIA NCP-ADS Exam Syllabus Topics:

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

1. You are working with a dataset containing billions of rows and need to perform data transformations, aggregations, and joins efficiently on a single-node GPU-enabled workstation.
Which NVIDIA technology is best suited to optimize performance for these operations?

A) NVIDIA TensorRT to optimize DataFrame transformations and aggregations using deep learning.
B) NVIDIA RAPIDS cuDF to leverage GPU acceleration for large-scale DataFrame operations.
C) NVIDIA Triton Inference Server to accelerate data processing workflows on a single GPU.
D) NVIDIA Nsight Compute to profile and optimize the performance of GPU-based aggregations.

2. A machine learning engineer is working on a multi-GPU workload using Dask and RAPIDS to process a massive dataset efficiently. However, they notice that GPU utilization is not optimal, and data transfer between GPUs is slowing down computation.
What is the best approach to minimize data transfer overhead and maximize parallel efficiency?

A) Transfer data between GPUs using NumPy's .copy() function
B) Load the dataset into a Pandas DataFrame and distribute computations across GPUs manually
C) Use NVLink to enable high-bandwidth inter-GPU communication and dask_cuda.LocalCUDACluster
D) Use Dask's single-threaded scheduler to avoid multi-threading overhead

3. You are tasked with acquiring a dataset for training a machine learning model in healthcare, predicting patient readmission rates. Before using the dataset, you must assess its quality.
Which of the following is the most important factor to evaluate before acquisition?

A) The number of missing values and inconsistencies in key columns
B) The programming language used to preprocess the dataset
C) The file format (CSV, JSON, or XML) of the dataset
D) Whether the dataset is stored on a cloud server or a local machine

4. A data science team is deploying a deep learning model for real-time inference. The model is optimized for inference on an NVIDIA A100 GPU, but the team notices that inference latency is higher than expected.
Which of the following optimizations is most effective in reducing inference latency?

A) Use mixed-precision inference with TensorRT to accelerate computation.
B) Enable CPU offloading to balance the workload between the CPU and GPU.
C) Increase the batch size significantly to improve GPU utilization.
D) Reduce the model size by randomly pruning neurons without retraining.

5. In the context of the CRISP-DM process, you are tasked with building a machine learning model on a large dataset. You decide to leverage GPU resources to accelerate training.
Which of the following steps in the CRISP-DM process are most likely to benefit from the use of GPU resources in the cloud environment? (Select two)

A) Data Understanding
B) Business Understanding
C) Model Deployment and Monitoring
D) Data Exploration
E) Model Training and Evaluation

Solutions: