• Exam Code: NCP-ADS
  • Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
  • Certification Provider: NVIDIA
  • Corresponding Certification: NVIDIA-Certified Professional
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  • Exam Code: NCP-ADS
  • Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
  • No. of Questions: 303 Questions and Answers
  • Updated: Oct 07, 2026
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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Analysis14%- Exploratory Data Analysis (EDA)
  • 1. Use cuGraph for graph analytics
    • 2. Detect anomalies in time series datasets
      • 3. Perform time series analysis and visualization
        Topic 2: Data Manipulation and Software Literacy19%- ETL and Data Processing Workflows
        • 1. GPU-accelerated ETL design and implementation
          • 2. Distributed data processing frameworks (Dask)
            • 3. Data caching and performance optimization
              Topic 3: MLOps19%- Deployment and Monitoring
              • 1. Memory and capacity evaluation
                • 2. Model deployment in production environments
                  • 3. Performance benchmarking and optimization
                    Topic 4: Data Preparation17%- Data Cleaning and Transformation
                    • 1. Synthetic data generation with RAPIDS
                      • 2. Data normalization and standardization
                        • 3. cuDF and pandas data preprocessing
                          Topic 5: GPU and Cloud Computing16%- GPU Optimization and Infrastructure
                          • 1. Docker and Conda environment management
                            • 2. Benchmarking GPU workflows
                              • 3. CRISP-DM workflow execution
                                Topic 6: Machine Learning15%- Model Development and Optimization
                                • 1. Hyperparameter tuning
                                  • 2. Feature engineering
                                    • 3. Multi-GPU training comparison
                                      • 4. Memory optimization techniques (mixed precision, batching)

                                        NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

                                        You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
                                        Which of the following is the most memory-efficient data type choice for this column?

                                        • A. df['item_id'] = df['item_id'].astype('float64')
                                        • B. df['item_id'] = df['item_id'].astype('int64')
                                        • C. df['item_id'] = df['item_id'].astype('string')
                                        • D. df['item_id'] = df['item_id'].astype('int32')
                                        Reveal Solution  Discussion  0

                                        Correct Answer: B  🗳️

                                        You are working with a cuDF DataFrame and need to convert a column named sales from float64 to int32 to save memory.
                                        Which of the following is the correct and most efficient way to perform this conversion in cuDF?

                                        • A. df['sales'].apply(lambda x: int(x))
                                        • B. df['sales'] = df['sales'].astype('int32')
                                        • C. df['sales'] = df['sales'].to_numeric('int32')
                                        • D. df['sales'].convert_dtypes('int32')
                                        Reveal Solution  Discussion  0

                                        Correct Answer: B  🗳️

                                        You are comparing the performance of GPU-accelerated deep learning models on two cloud platforms: AWS EC2 and Google Cloud Platform (GCP). You want to design a benchmark that evaluates GPU resource utilization, processing time, and cost-efficiency for training models with large datasets.
                                        Which actions should you take to implement an effective benchmark? (Select two)

                                        • A. Only consider the cost of GPU resources for each cloud provider without factoring in data storage and transfer costs.
                                        • B. Measure both GPU utilization and network latency between the cloud platform and your data storage location.
                                        • C. Limit the benchmarking tests to a single model architecture for each cloud platform.
                                        • D. Run each model on the same GPU type and instance size across both cloud platforms.
                                        Reveal Solution  Discussion  0

                                        Correct Answer: B,D  🗳️

                                        When scaling data parallelism using Dask with multiple Nvidia GPUs, what is the key consideration to avoid memory issues when distributing large datasets?

                                        • A. Allow Dask to allocate data chunks dynamically without partitioning the dataset first, letting the system handle memory distribution automatically.
                                        • B. Ensure that each GPU's memory usage is manually monitored and adjusted, as Dask does not manage memory allocation automatically across GPUs.
                                        • C. Split the dataset into smaller partitions that fit into each GPU's memory to prevent out-of-memory errors, and let Dask manage data distribution.
                                        • D. Use dask_gpu instead of dask_cuda to manage memory automatically across GPUs.
                                        Reveal Solution  Discussion  0

                                        Correct Answer: C  🗳️

                                        You are working on a machine learning project that requires selecting the optimal data types for each feature in your dataset to maximize performance and efficiency in an MLOps pipeline.
                                        Which of the following data types is most suitable for GPU-accelerated machine learning workflows when working with large datasets on NVIDIA platforms?

                                        • A. String
                                        • B. Float32
                                        • C. Int32
                                        • D. Float64
                                        Reveal Solution  Discussion  0

                                        Correct Answer: B  🗳️

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