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Last Updated: Oct 25, 2026
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 15% | - Model training with GPU acceleration
|
| Data Analysis | 14% | - Graph analytics
|
| Data Manipulation and Software Literacy | 19% | - Software literacy and development tools
|
| GPU and Cloud Computing | 16% | - GPU resource management
|
| Data Preparation | 17% | - Data loading and preprocessing
|
| MLOps | 19% | - Model monitoring and management
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are training a deep learning model on a large dataset. Initially, you train the model on a single GPU and achieve a training time of 10 hours. To speed up training, you switch to a multi-GPU setup with four GPUs. However, after testing, you notice that the training time is only reduced to 3.5 hours instead of the expected 2.5 hours (a linear speedup).
What is the most likely reason for this sublinear speedup?
- A. The learning rate is too low, causing slower convergence despite multiple GPUs.
- B. Increased memory bandwidth usage causes a bottleneck in the system.
- C. Data transfer and communication overhead between GPUs limit scalability.
- D. The optimizer is not designed for multi-GPU training, causing inefficiency.
Correct Answer: C 🗳️
Which of the following Nvidia technologies is primarily used for performing benchmarking and optimizing GPU-accelerated deep learning workflows, especially focusing on model training performance?
- A. Nvidia DeepStream SDK
- B. Nvidia CUDA
- C. Nvidia Nsight Systems
- D. Nvidia Triton Inference Server
Correct Answer: C 🗳️
You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?
- A. Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
- B. Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.
- C. Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.
- D. Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
Correct Answer: D 🗳️
Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?
- A. Number of model parameters.
- B. Hyperparameter tuning results.
- C. GPU utilization and memory usage.
- D. Training dataset bias.
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 cuml.impute.SimpleImputer(strategy='mean') to replace missing values with the column mean.
- C. cuml.impute.KNNImputer() to replace missing values based on k-nearest neighbors.
- D. Use cudf.DataFrame.fillna(method='ffill') to forward-fill missing values.
Correct Answer: B 🗳️
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