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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q278-Q283):

NEW QUESTION # 278
You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.
Which approach best utilizes NVIDIA technologies for efficient forecasting?

Answer: A


NEW QUESTION # 279
Which of the following Nvidia technologies is primarily used for performing benchmarking and optimizing GPU-accelerated deep learning workflows, especially focusing on model training performance?

Answer: C


NEW QUESTION # 280
A data scientist is processing a dataset that is too large to fit into the memory of a single GPU. They decide to use Dask with cuDF to leverage multiple GPUs for accelerated computation.
Which of the following approaches ensures efficient parallelism when working with dask_cudf?

Answer: A


NEW QUESTION # 281
You are working with a large-scale social network dataset and need to analyze relationships between users to detect communities using the Louvain algorithm. Given the benefits of GPU acceleration, you decide to use cuGraph for this task.
Which of the following statements best describes why cuGraph is beneficial for this workload?

Answer: D


NEW QUESTION # 282
When performing benchmarking and optimization for GPU-accelerated workflows, which of the following tools is best suited for analyzing the memory utilization and computational efficiency of deep learning models running on Nvidia GPUs?

Answer: B


NEW QUESTION # 283
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