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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q87-Q92):

NEW QUESTION # 87
A machine learning team is handling large-scale datasets that need to be efficiently stored and accessed within an NVIDIA RAPIDS workflow.
Which of the following storage formats and techniques provides the best performance for GPU-based data science pipelines?

Answer: B


NEW QUESTION # 88
You are working with a large dataset in NVIDIA RAPIDS cuDF and notice that the data processing pipeline is taking longer than expected.
Which of the following tools or techniques can help you identify and analyze bottlenecks in the pipeline?

Answer: C


NEW QUESTION # 89
You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?

Answer: C


NEW QUESTION # 90
You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

Answer: D,E


NEW QUESTION # 91
A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

Answer: B


NEW QUESTION # 92
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