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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Manipulation and Software Literacy	19%	- Data processing libraries selection and usage - Dependency management and containerization - Performance profiling and optimization tools - GPU-accelerated ETL workflows
GPU and Cloud Computing	16%	- Cloud GPU environments and deployment - Resource management and scaling strategies - CRISP-DM and data science methodology - GPU architecture and acceleration principles
Machine Learning	15%	- Distributed training strategies - Model training and hyperparameter tuning - Model evaluation and validation - GPU-accelerated ML frameworks and algorithms
Data Preparation	17%	- Feature engineering and data type optimization - Workflow monitoring and bottleneck identification - Data validation and quality assurance - Data cleaning, preprocessing and transformation
MLOps	19%	- Monitoring, logging and maintenance - Model deployment and serving - Pipeline automation and orchestration - End-to-end workflow management
Data Analysis	14%	- Distributed and parallel data processing - Exploratory Data Analysis (EDA) - Data visualization and graph analytics - Time-series analysis and anomaly detection

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q82-Q87):

NEW QUESTION # 82
A research team is analyzing a large transportation network and wants to identify groups of locations that are closely connected based on travel routes.
Which NVIDIA cuGraph function would be the most effective for this task?

A. Use cuGraph's louvain() function to detect communities within the graph.
B. Use cuGraph's shortest_path() to compute all shortest paths and derive clusters manually.
C. Use cuGraph's pagerank() to rank the most connected locations.
D. Apply cuGraph's bfs() (Breadth-First Search) to find clusters of locations.

Answer: A

NEW QUESTION # 83
You are setting up a deep learning model for training on a multi-GPU cluster. You want to maximize training efficiency while maintaining model convergence.
Which of the following strategies is most effective in ensuring efficient multi-GPU training?

A. Reduce batch size to ensure each GPU receives only a small portion of the training data.
B. Use Data Parallelism, where each GPU gets a different portion of the dataset, but gradients are averaged across all GPUs.
C. Train each GPU independently on a different dataset to reduce the communication bottleneck.
D. Use Model Parallelism, where different layers of the model are placed on different GPUs to reduce communication overhead.

Answer: B

NEW QUESTION # 84
You are working with a large dataset in a GPU-accelerated environment, and one of the columns, revenue, contains numeric values representing the annual revenue for companies. The revenue values are in the billions of dollars.
Which of the following is the most memory-efficient data type for the revenue column in a cuDF DataFrame?

A. df['revenue'] = df['revenue'].astype('float64')
B. df['revenue'] = df['revenue'].astype('int64')
C. df['revenue'] = df['revenue'].astype('float32')
D. df['revenue'] = df['revenue'].astype('uint32')

Answer: A

NEW QUESTION # 85
A data scientist is working with large-scale ETL (Extract, Transform, Load) pipelines on GPU- accelerated infrastructure using RAPIDS. The workload involves frequent shuffle operations, which significantly impact performance.
What is the best approach using NVIDIA technologies to reduce shuffle overhead and improve performance?

A. Store all intermediate shuffle data in CPU memory using pandas to ensure persistence and reduce GPU load.
B. Use RAPIDS cuML to replace shuffle-intensive operations with an ML model that predicts data distribution.
C. Enable CUDA Unified Memory to automatically optimize shuffle performance without manual intervention.
D. Use RAPIDS cuDF's GPU memory caching to store intermediate DataFrames and avoid redundant shuffle operations.

Answer: D

NEW QUESTION # 86
A data scientist is training a deep learning model on an NVIDIA GPU and wants to profile the model to identify performance bottlenecks. The scientist chooses to use NVIDIA DLProf.
Which of the following steps is the most effective way to profile the model using DLProf?

A. Modify the training script to manually insert timing functions for each layer and compare execution times.
B. Rely on general CPU profiling tools like perf and gprof to analyze GPU performance.
C. Run the model using dlprof --mode profile to collect performance metrics and generate a report.
D. Use nvprof instead of DLProf since it provides more detailed profiling for deep learning workloads.

Answer: C

NEW QUESTION # 87
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