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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Machine Learning | 15% | - Model Development and Optimization
|
| Topic 2: Data Preparation | 17% | - Data Cleaning and Transformation
|
| Topic 3: MLOps | 19% | - Deployment and Monitoring
|
| Topic 4: Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| Topic 5: Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Topic 6: GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. You are tasked with designing a benchmark to compare the performance of different GPU- accelerated machine learning frameworks, such as TensorFlow, PyTorch, and RAPIDS.
Which of the following factors is the most critical to ensure a fair and meaningful comparison?
A) Allowing each framework to run with its default settings, as tuning hyperparameters would bias the results.
B) Using the latest software versions of each framework, regardless of compatibility with the GPU hardware.
C) Ensuring all frameworks use the same dataset, batch size, and GPU hardware during benchmarking.
D) Using different model architectures per framework to test a variety of scenarios and ensure a broad evaluation.
2. Which of the following best describes the role of MLOps in the context of NVIDIA technologies for deploying machine learning models in production? (Select two)
A) MLOps frameworks support version control and automation, ensuring reproducibility and scalability of ML workflows
B) MLOps replaces the need for data preprocessing during training and deployment
C) MLOps helps manage the lifecycle of machine learning models, ensuring efficient collaboration and model governance
D) MLOps ensures that models trained on GPUs can only run on GPUs during deployment
3. A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of- memory (OOM) errors.
To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?
A) Increasing batch size without adjusting the optimizer settings
B) Using mixed precision training with automatic loss scaling
C) Using single-precision (FP32) calculations for better accuracy
D) Storing all training data in GPU memory at once
4. In a typical MLOps pipeline, which of the following practices are essential to ensuring robust deployment and monitoring of machine learning models in production? (Select two)
A) Post-deployment data drift detection to assess model performance degradation.
B) Continuous integration and continuous deployment (CI/CD) pipelines for model updates.
C) Use of manual intervention for every model update to ensure accuracy.
D) Automated hyperparameter tuning during inference to optimize model performance.
5. 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 nvidia-smi
B) NVIDIA TensorRT Profiling
C) NVIDIA CUDA Profiler (nvprof)
D) NVIDIA DLA (Deep Learning Accelerator)
E) NVIDIA Nsight Systems
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A,C | Question # 3 Answer: B | Question # 4 Answer: A,B | Question # 5 Answer: C,E |
