NCP-ADS Dumps | NCP-ADS Exam Book

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q244-Q249):

NEW QUESTION # 244
You are working with a dataset containing 2 billion rows of financial transactions, and you need to perform exploratory data analysis (EDA) before building a predictive model.
Which of the following approaches is the most appropriate for handling this data efficiently?

Answer: A


NEW QUESTION # 245
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 # 246
Which of the following best describes the role of MLOps in the context of NVIDIA technologies for deploying machine learning models in production? (Select two)

Answer: A,B


NEW QUESTION # 247
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?

Answer: A


NEW QUESTION # 248
Which of the following best describes the purpose of the NVIDIA TensorRT library?

Answer: D


NEW QUESTION # 249
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