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NVIDIA NCP-ADS Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Data Manipulation and Software Literacy19%- Performance profiling and optimization tools
- GPU-accelerated ETL workflows
- Data processing libraries selection and usage
- Dependency management and containerization
Topic 2: GPU and Cloud Computing16%- Resource management and scaling strategies
- GPU architecture and acceleration principles
- Cloud GPU environments and deployment
- CRISP-DM and data science methodology
Topic 3: Data Analysis14%- Exploratory Data Analysis (EDA)
- Distributed and parallel data processing
- Data visualization and graph analytics
- Time-series analysis and anomaly detection
Topic 4: MLOps19%- Monitoring, logging and maintenance
- Model deployment and serving
- End-to-end workflow management
- Pipeline automation and orchestration
Topic 5: Machine Learning15%- Model evaluation and validation
- GPU-accelerated ML frameworks and algorithms
- Distributed training strategies
- Model training and hyperparameter tuning
Topic 6: Data Preparation17%- Data cleaning, preprocessing and transformation
- Feature engineering and data type optimization
- Data validation and quality assurance
- Workflow monitoring and bottleneck identification

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q99-Q104):

NEW QUESTION # 99
Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?

Answer: D


NEW QUESTION # 100
You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?

Answer: A


NEW QUESTION # 101
You are selecting a dataset for GPU-accelerated data science using NVIDIA RAPIDS.
Which dataset characteristic is most suitable for taking advantage of GPU acceleration?

Answer: D


NEW QUESTION # 102
You are working on a large-scale graph analysis project using NVIDIA cuGraph for accelerated computations. Your dataset consists of millions of nodes and edges representing social network interactions. You need to efficiently compute PageRank while minimizing memory usage.
Which of the following techniques would be the most effective?

Answer: C


NEW QUESTION # 103
You are designing a data pipeline for a healthcare analytics company that processes petabytes of structured and semi-structured patient data. Your goal is to optimize performance while maintaining accuracy.
Which of the following strategies is the most appropriate choice for accelerating this workload?

Answer: D


NEW QUESTION # 104
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