NCP-ADS Practice Online | Certification NCP-ADS Exam

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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                                        Quiz 2026 NCP-ADS: NVIDIA-Certified-Professional Accelerated Data Science – Trustable Practice Online

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                                        NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q17-Q22):

                                        NEW QUESTION # 17
                                        You are working with a dataset containing billions of rows and need to perform data transformations, aggregations, and joins efficiently on a single-node GPU-enabled workstation.
                                        Which NVIDIA technology is best suited to optimize performance for these operations?

                                        Answer: A


                                        NEW QUESTION # 18
                                        You are working on a data processing pipeline using NVIDIA GPUs for accelerating computations.
                                        You need to monitor the pipeline's performance to identify bottlenecks.
                                        Which of the following tools or techniques can be used to efficiently recognize bottlenecks in such a GPU-accelerated pipeline? (Select two)

                                        Answer: A,E


                                        NEW QUESTION # 19
                                        Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?

                                        Answer: C


                                        NEW QUESTION # 20
                                        You are working with a large dataset containing numeric and categorical features, which will be processed using NVIDIA RAPIDS cuDF for accelerated analytics.
                                        To optimize performance while minimizing memory usage, which data type is the most appropriate for storing a categorical variable with a small number of unique values?

                                        Answer: A


                                        NEW QUESTION # 21
                                        You are working on a structured dataset of around 10GB and need to perform exploratory data analysis (EDA), feature engineering, and filtering operations efficiently using NVIDIA technologies. The dataset fits into a single GPU's memory.
                                        Which data processing library should you use to achieve the best performance?

                                        Answer: A


                                        NEW QUESTION # 22
                                        ......

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