NVIDIA NCP-ADS : NVIDIA-Certified-Professional Accelerated Data Science
- Exam Code: NCP-ADS
- Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
- Updated: Oct 16, 2026
- Q & A: 303 Questions and Answers
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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: Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Topic 4: MLOps | 19% | - Deployment and Monitoring
|
| Topic 5: GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Topic 6: Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
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. Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
- B. Train a generative adversarial network (GAN) using PyTorch and then use the generated samples in RAPIDS AI without any additional processing.
- C. Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
- D. Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
Correct Answer: A 🗳️
You have trained a machine learning model using cuML as part of the Modeling phase in the CRISP- DM framework. Now, you need to assess how well the model performs before moving forward with deployment.
Which of the following steps aligns best with the Evaluation phase of CRISP-DM using NVIDIA technologies?
- A. Define the problem statement and collect relevant datasets before training the model.
- B. Deploy the model to an edge device using TensorRT for real-time inference.
- C. Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().
- D. Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.
Correct Answer: C 🗳️
You are working with a large-scale social network dataset and need to analyze relationships between users to detect communities using the Louvain algorithm. Given the benefits of GPU acceleration, you decide to use cuGraph for this task.
Which of the following statements best describes why cuGraph is beneficial for this workload?
- A. cuGraph does not support weighted graphs, making it less useful for real-world social network analysis.
- B. Running graph algorithms on a GPU leads to higher latency due to the overhead of data movement between CPU and GPU.
- C. cuGraph primarily accelerates deep learning tasks and is not optimized for graph-based algorithms.
- D. cuGraph enables parallel computation on the GPU, significantly speeding up community detection tasks compared to traditional CPU-based implementations.
Correct Answer: D 🗳️
You have a pandas DataFrame with a column containing floating-point numbers, but it takes up too much memory. You want to convert it into a lower-precision type using CuDF or pandas while ensuring computational efficiency.
Which function would you use?
- A. df.to_float16()
- B. df.convert_dtypes()
- C. df['col'].apply(lambda x: np.float16(x))
- D. df.astype('float16')
Correct Answer: D 🗳️
You are working on a financial dataset that tracks stock prices over time, and you need to detect anomalies such as sudden spikes or drops using NVIDIA technologies.
Which of the following approaches would be the most effective for anomaly detection in a time-series dataset using NVIDIA's RAPIDS AI and TensorRT?
- A. Use RAPIDS cuML's Isolation Forest for anomaly detection and deploy it with NVIDIA Triton Inference Server.
- B. Perform anomaly detection by applying DBSCAN clustering with RAPIDS cuML without any feature engineering.
- C. Use traditional ARIMA modeling with RAPIDS cuML to classify anomalies based on residual analysis.
- D. Apply a traditional rule-based thresholding method using pandas and NumPy for detecting sudden spikes in stock prices.
Correct Answer: A 🗳️
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