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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| Machine Learning | 15% | - Model Development and Optimization
|
| MLOps | 19% | - Deployment and Monitoring
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are training a large-scale random forest model on a dataset with millions of rows and hundreds of features. The training time is significantly high when using traditional CPU-based machine learning frameworks.
Which NVIDIA technology should you use to accelerate training while maintaining compatibility with common ML frameworks like scikit-learn?
- A. NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.
- B. NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.
- C. NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.
- D. NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.
Correct Answer: A 🗳️
You are working on a machine learning project that requires training a large XGBoost model on a dataset containing millions of records. Due to the dataset size, training on a CPU-based environment takes an excessively long time. To accelerate the training process, you decide to use NVIDIA RAPIDS.
Which of the following is the best approach to leverage GPU acceleration for training the XGBoost model?
- A. Use cuML to replace scikit-learn's XGBoost implementation, as cuML supports GPU-accelerated XGBoost training.
- B. Install and use dask-xgboost, which automatically optimizes XGBoost training using GPU acceleration.
- C. Store the dataset in Apache Parquet format and load it using pandas to improve training performance.
- D. Use XGBoost with the "tree_method": "gpu_hist" parameter to enable GPU acceleration.
Correct Answer: D 🗳️
A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?
- A. Train a generative adversarial network (GAN) using PyTorch and then use the generated samples in RAPIDS AI without any additional processing.
- B. Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
- C. Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
- D. Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
Correct Answer: C 🗳️
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. GPU acceleration is beneficial when the dataset can be divided into independent chunks that can be processed in parallel.
- 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. CPU-based machine learning methods are always more effective for small datasets, regardless of the algorithm used.
- D. The size of the dataset in terms of rows and columns is irrelevant when determining if it qualifies as big data.
- E. The dataset must be over 100GB in size to qualify as big data and warrant GPU acceleration.
Correct Answer: A,B 🗳️
A data scientist is training a deep learning model on an NVIDIA GPU and wants to profile the model to identify performance bottlenecks. The scientist chooses to use NVIDIA DLProf.
Which of the following steps is the most effective way to profile the model using DLProf?
- A. Rely on general CPU profiling tools like perf and gprof to analyze GPU performance.
- B. Modify the training script to manually insert timing functions for each layer and compare execution times.
- C. Run the model using dlprof --mode profile to collect performance metrics and generate a report.
- D. Use nvprof instead of DLProf since it provides more detailed profiling for deep learning workloads.
Correct Answer: C 🗳️
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