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

  • Exam Code: NCP-ADS
  • Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
  • Updated: Jul 25, 2026     Q & A: 303 Questions and Answers

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Free Download NCP-ADS exam dumps pdf

NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: MLOps19%- Deployment and Monitoring
  • 1. Performance benchmarking and optimization
    • 2. Model deployment in production environments
      • 3. Memory and capacity evaluation
        Topic 2: Machine Learning15%- Model Development and Optimization
        • 1. Hyperparameter tuning
          • 2. Memory optimization techniques (mixed precision, batching)
            • 3. Multi-GPU training comparison
              • 4. Feature engineering
                Topic 3: GPU and Cloud Computing16%- GPU Optimization and Infrastructure
                • 1. Benchmarking GPU workflows
                  • 2. CRISP-DM workflow execution
                    • 3. Docker and Conda environment management
                      Topic 4: Data Manipulation and Software Literacy19%- ETL and Data Processing Workflows
                      • 1. Distributed data processing frameworks (Dask)
                        • 2. GPU-accelerated ETL design and implementation
                          • 3. Data caching and performance optimization
                            Topic 5: Data Preparation17%- Data Cleaning and Transformation
                            • 1. Data normalization and standardization
                              • 2. cuDF and pandas data preprocessing
                                • 3. Synthetic data generation with RAPIDS
                                  Topic 6: 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

                                        NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

                                        1. You are working on an MLOps project where GPU-accelerated workflows are being used for model training. You want to benchmark and optimize these workflows to ensure the best performance.
                                        Which of the following steps should you consider to effectively benchmark and optimize GPU- accelerated workflows? (Select two)

                                        A) Increase the batch size and learning rate simultaneously to maximize GPU usage and reduce training time.
                                        B) Use a dynamic batch size strategy that adjusts the batch size based on available GPU memory to maximize throughput.
                                        C) Optimize data loading by using data augmentation techniques during training to reduce the time spent on I/O operations.
                                        D) Use profiling tools to measure the GPU utilization and memory usage during training to identify performance bottlenecks.


                                        2. Which of the following is the main advantage of using TensorRT for inference in an accelerated data science pipeline?

                                        A) TensorRT is mainly used for data visualization and not for model inference.
                                        B) TensorRT automatically builds training models from raw data without requiring pre-trained models.
                                        C) TensorRT is only compatible with image classification models and does not support other model types.
                                        D) TensorRT optimizes deep learning models to run efficiently on NVIDIA GPUs by reducing precision while maintaining accuracy.


                                        3. You are working with a large dataset containing missing values, and you need to clean and preprocess the data efficiently.
                                        Which of the following methods provides the best performance when handling missing values in a GPU- accelerated EDA workflow using RAPIDS?

                                        A) Drop all missing values using df.dropna() in Pandas before using RAPIDS.
                                        B) Ignore missing values since GPU acceleration can handle incomplete data without performance degradation.
                                        C) Use pandas.DataFrame.fillna() on the dataset before converting it to a cuDF DataFrame.
                                        D) Convert the dataset to a cuDF DataFrame and use cudf.DataFrame.fillna() to fill missing values.


                                        4. A machine learning engineer runs NVIDIA DLProf to analyze the performance of a deep learning model and receives a report indicating high GPU idle time.
                                        What is the most likely cause of this issue?

                                        A) The CUDA cores are overheating, leading to automatic throttling of computations.
                                        B) The batch size is too large, leading to excessive GPU memory utilization and slow processing.
                                        C) The model is experiencing data loading bottlenecks, causing the GPU to wait for input batches.
                                        D) The GPU is not powerful enough to process the deep learning model efficiently.


                                        5. Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)

                                        A) Using Dask for parallel processing of large datasets
                                        B) Using Excel to manipulate large datasets
                                        C) Using Apache Spark for distributed data processing
                                        D) Using SQLAlchemy to interact with databases for large data processing
                                        E) Using SQLite as a local database for large-scale data analysis


                                        Solutions:

                                        Question # 1
                                        Answer: B,D
                                        Question # 2
                                        Answer: D
                                        Question # 3
                                        Answer: D
                                        Question # 4
                                        Answer: C
                                        Question # 5
                                        Answer: A,C

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