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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q272-Q277):

NEW QUESTION # 272
You are tasked with selecting the optimal data processing library for an AI project that involves handling varying dataset sizes. The project must be flexible enough to scale from small datasets (a few GBs) to large datasets (hundreds of GBs or more) using NVIDIA technologies.
Which of the following libraries would you choose for optimal performance at both small and large scales?

A. PyTorch
B. cuDF
C. CUDA Toolkit
D. Dask

Answer: B

NEW QUESTION # 273
Which of the following is the most efficient way to implement data parallelism using Dask for multi- GPU scaling on an Nvidia platform?

A. Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.
B. Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.
C. Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.
D. Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.

Answer: A

NEW QUESTION # 274
You are analyzing a transportation network where airports represent nodes and flight routes represent edges. You need to determine the most critical airports in the network based on how many shortest paths pass through them.
Which cuGraph centrality algorithm should you use for this task?

A. Triangle Count, as it determines the number of three-node cycles an airport is part of, indicating its importance in network structure.
B. Betweenness Centrality, as it identifies airports that act as major transit hubs by measuring how frequently they appear in shortest paths.
C. Degree Centrality, as it measures the number of direct connections an airport has, indicating its importance in the network.
D. Closeness Centrality, as it measures how close an airport is to all other airports, making it the best indicator of global connectivity.

Answer: B

NEW QUESTION # 275
A machine learning engineer is training a large transformer-based model for natural language processing (NLP). They want to maximize training speed and efficiency using NVIDIA GPUs.
Which of the following techniques would most effectively enhance GPU utilization and reduce training time?

A. Prefetching data with the CPU while training on the GPU
B. Running training exclusively on CPU
C. Using mixed-precision training with Tensor Cores
D. Disabling data parallelism

Answer: C

NEW QUESTION # 276
You are analyzing a large-scale transportation network using cuGraph and notice that query times are longer than expected when running graph algorithms.
What is the best way to optimize graph processing performance using GPU-accelerated tools?

A. Store the graph in COO (Coordinate List) format instead of CSR (Compressed Sparse Row) format for faster traversal.
B. Use cugraph.to_directed() to convert the graph into a directed format, which improves GPU parallelism.
C. Use cugraph.filter_unconnected_nodes() to remove unconnected nodes before processing.
D. Convert the graph to CSR (Compressed Sparse Row) format before running computations to improve memory efficiency.

Answer: D

NEW QUESTION # 277
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