NVIDIA-Certified-Professional Accelerated Data Science : NCP-ADS

NCP-ADS real exams

Exam Code: NCP-ADS

Exam Name: NVIDIA-Certified-Professional Accelerated Data Science

Updated: Aug 17, 2026

Q & A: 303 Questions and Answers

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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
MLOps19%- Deployment and Monitoring
  • 1. Memory and capacity evaluation
    • 2. Performance benchmarking and optimization
      • 3. Model deployment in production environments
        GPU and Cloud Computing16%- GPU Optimization and Infrastructure
        • 1. CRISP-DM workflow execution
          • 2. Docker and Conda environment management
            • 3. Benchmarking GPU workflows
              Data Manipulation and Software Literacy19%- ETL and Data Processing Workflows
              • 1. Data caching and performance optimization
                • 2. Distributed data processing frameworks (Dask)
                  • 3. GPU-accelerated ETL design and implementation
                    Machine Learning15%- Model Development and Optimization
                    • 1. Memory optimization techniques (mixed precision, batching)
                      • 2. Feature engineering
                        • 3. Multi-GPU training comparison
                          • 4. Hyperparameter tuning
                            Data Analysis14%- Exploratory Data Analysis (EDA)
                            • 1. Use cuGraph for graph analytics
                              • 2. Perform time series analysis and visualization
                                • 3. Detect anomalies in time series datasets
                                  Data Preparation17%- Data Cleaning and Transformation
                                  • 1. cuDF and pandas data preprocessing
                                    • 2. Synthetic data generation with RAPIDS
                                      • 3. Data normalization and standardization

                                        NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

                                        1. A manufacturing company wants to detect anomalies in a real-time IoT sensor dataset using NVIDIA technologies. The data arrives as a continuous stream, and the company needs to process it efficiently for immediate anomaly detection.
                                        Which NVIDIA-powered approach is the most suitable for this use case?

                                        A) Deploy an Autoencoder-based deep learning model using NVIDIA TensorRT for real-time inference
                                        B) Apply cuML's Isolation Forest on incoming streaming data for real-time anomaly detection
                                        C) Leverage cuGraph to model the sensor readings as a graph and detect anomalies using centrality measures
                                        D) Use RAPIDS cuDF with batch processing to analyze historical anomalies every few hours


                                        2. You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
                                        Which of the following is the best approach to achieve this goal?

                                        A) Load the data into a Spark DataFrame and visualize using Apache Zeppelin
                                        B) Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively
                                        C) Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib
                                        D) Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly


                                        3. A machine learning engineer is tasked with optimizing an image classification model on a cloud platform. The engineer must select a GPU-accelerated instance that balances cost and performance while ensuring compatibility with frameworks like TensorFlow and PyTorch.
                                        Which instance configuration is the most appropriate choice?

                                        A) A cloud instance with NVIDIA A100 GPUs and NVLink support.
                                        B) A CPU-only instance with 128 GB of RAM for increased data processing speed.
                                        C) A single-core CPU instance with high disk I/O throughput for faster data loading.
                                        D) A cloud instance with integrated graphics rather than dedicated NVIDIA GPUs.


                                        4. Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

                                        A) Business Understanding
                                        B) Model Building
                                        C) Data Understanding
                                        D) Data Preparation


                                        5. You are selecting a dataset for GPU-accelerated data science using NVIDIA RAPIDS.
                                        Which dataset characteristic is most suitable for taking advantage of GPU acceleration?

                                        A) A large, structured dataset stored in a Parquet format
                                        B) A dataset stored as an SQLite database file
                                        C) A dataset consisting of unstructured text stored as plain .txt files
                                        D) A small dataset with fewer than 1,000 rows stored in an Excel file


                                        Solutions:

                                        Question # 1
                                        Answer: B
                                        Question # 2
                                        Answer: B
                                        Question # 3
                                        Answer: A
                                        Question # 4
                                        Answer: A
                                        Question # 5
                                        Answer: A

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