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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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                                        NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q136-Q141):

                                        NEW QUESTION # 136
                                        You are designing an ETL workflow to process large-scale financial transaction data using GPU acceleration. The dataset is stored in a Parquet file and contains millions of records.
                                        Which of the following approaches is the most efficient for performing extract, transform, and load (ETL) operations using NVIDIA RAPIDS technologies?

                                        Answer: B


                                        NEW QUESTION # 137
                                        When comparing the required memory with the available memory on a GPU for an MLOps deployment using NVIDIA technologies, which of the following is the best method to optimize memory usage while training large models?

                                        Answer: B


                                        NEW QUESTION # 138
                                        You are analyzing a time-series dataset that represents temperature readings from an industrial sensor. Your goal is to detect anomalies that may indicate sensor failures or environmental changes.
                                        Which of the following methods would be the most appropriate statistical technique for detecting anomalies in this time-series dataset?

                                        Answer: B


                                        NEW QUESTION # 139
                                        A data engineer is tasked with processing a 5 TB dataset stored in Apache Parquet format. The dataset consists of user activity logs and needs to be filtered, aggregated, and processed for feature engineering before training an ML model. The engineer is deciding between Dask and Apache Spark.
                                        Which statement best describes a key difference between the two frameworks?

                                        Answer: B


                                        NEW QUESTION # 140
                                        A data scientist is using an NVIDIA RAPIDS-based data processing pipeline on a GPU cluster. They notice that the pipeline is not performing as expected and suspect a bottleneck.
                                        Which of the following approaches would best help identify the source of the bottleneck?

                                        Answer: C


                                        NEW QUESTION # 141
                                        ......

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