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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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                                        NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q160-Q165):

                                        NEW QUESTION # 160
                                        You are processing a large dataset with UNIX timestamps (seconds since Jan 1, 1970) ranging from Jan 1, 2000, to the present.
                                        What is the most memory-efficient data type for the timestamp column in a GPU-accelerated cloud environment?

                                        Answer: C


                                        NEW QUESTION # 161
                                        A data scientist wants to compare the performance of two different GPU-accelerated data science frameworks, NVIDIA RAPIDS (cuDF, cuML) and TensorFlow, for a tabular data classification task.
                                        Which of the following approaches would be the best practice for designing an unbiased and effective benchmark?

                                        Answer: C


                                        NEW QUESTION # 162
                                        You are processing a large dataset in a distributed computing environment using RAPIDS and Dask.
                                        Your workflow involves frequent shuffling of data between partitions, leading to significant slowdowns.
                                        Which of the following strategies is the best way to implement data caching to reduce shuffle overhead using NVIDIA technologies?

                                        Answer: C


                                        NEW QUESTION # 163
                                        A data science team is using NVIDIA GPUs to accelerate their AI workflow for a fraud detection system. They follow the CRISP-DM methodology to ensure a structured approach to the project.
                                        During the Data Preparation phase, what is the most effective way to leverage NVIDIA technologies?

                                        Answer: A


                                        NEW QUESTION # 164
                                        You are working on a large-scale social network analysis project using cuGraph. Your goal is to identify the most influential individuals in the network based on their connectivity and reach.
                                        Which of the following cuGraph algorithms would be the most appropriate choice for this task?

                                        Answer: C


                                        NEW QUESTION # 165
                                        ......

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