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Last Updated: Sep 17, 2026
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| GPU and Cloud Computing | 16% | - GPU architecture and fundamentals
|
| Data Manipulation and Software Literacy | 19% | - GPU-accelerated data manipulation using cuDF
|
| MLOps | 19% | - Model monitoring and management
|
| Data Preparation | 17% | - Feature engineering
|
| Data Analysis | 14% | - Exploratory data analysis
|
| Machine Learning | 15% | - Deep learning frameworks integration
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
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. Modify the training script to manually insert timing functions for each layer and compare execution times.
- B. Use nvprof instead of DLProf since it provides more detailed profiling for deep learning workloads.
- C. Run the model using dlprof --mode profile to collect performance metrics and generate a report.
- D. Rely on general CPU profiling tools like perf and gprof to analyze GPU performance.
Correct Answer: C 🗳️
You are working with a dataset where numerical features have different scales. To ensure uniformity across features, you decide to standardize the data using NVIDIA RAPIDS cuML.
Which of the following methods correctly standardizes the data in a GPU-accelerated manner?
- A. df = (df - df.mean()) / df.std()
- B. df = df.apply(lambda x: (x - x.mean()) / x.std(), axis=1)
- C. 1. scaler = cuml.preprocessing.StandardScaler() 2. df = scaler.fit_transform(df)
- D. df = (df - df.min()) / (df.max() - df.min())
Correct Answer: C 🗳️
You are working with a large dataset in RAPIDS cuDF and plan to standardize the numerical features using cuml.preprocessing.StandardScaler(). However, some columns contain missing values.
What is the best approach to handle the missing values before applying standardization?
- A. Replace missing values with zero before standardization.
- B. Use cudf.DataFrame.fillna(method='ffill') to forward-fill missing values.
- C. cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.
- D. Use cuml.impute.SimpleImputer(strategy='mean') to replace missing values with the column mean.
Correct Answer: D 🗳️
You are designing a reproducible benchmark to compare the performance of deep learning models across frameworks like PyTorch and TensorFlow using NVIDIA's A100 GPU.
Which step is most critical in ensuring fair benchmarking conditions?
- A. Ensuring the same CUDA/cuDNN and driver versions are installed when running benchmarks across frameworks.
- B. Measuring only forward pass latency to compare inference speed while ignoring backward pass computation.
- C. Using a different precision setting for each framework to maximize performance per framework's capabilities.
- D. Enabling XLA compiler optimizations only for TensorFlow to enhance its performance.
Correct Answer: A 🗳️
A data scientist is setting up a RAPIDS AI environment for a machine learning project that requires CUDA-enabled libraries and specific package versions to avoid conflicts.
Which of the following approaches best ensures a stable and reproducible environment while leveraging NVIDIA technologies?
- A. Manually install each required package with pip to ensure the latest versions are used without interference from Conda.
- B. Use a system-wide Python installation and install packages globally to ensure consistency across different users.
- C. Use Conda with the rapidsai channel to create an isolated environment that includes cuDF, cuML, and other GPU-accelerated libraries.
- D. Install CUDA and RAPIDS AI libraries using OS-level package managers such as apt or yum instead of Conda or Docker.
Correct Answer: C 🗳️
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