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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Data cleaning, preprocessing and transformation - Feature engineering and data type optimization - Workflow monitoring and bottleneck identification - Data validation and quality assurance
Topic 2: Data Analysis	14%	- Time-series analysis and anomaly detection - Data visualization and graph analytics - Distributed and parallel data processing - Exploratory Data Analysis (EDA)
Topic 3: 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
Topic 4: GPU and Cloud Computing	16%	- CRISP-DM and data science methodology - Resource management and scaling strategies - Cloud GPU environments and deployment - GPU architecture and acceleration principles
Topic 5: MLOps	19%	- Pipeline automation and orchestration - Model deployment and serving - Monitoring, logging and maintenance - End-to-end workflow management
Topic 6: Machine Learning	15%	- GPU-accelerated ML frameworks and algorithms - Model evaluation and validation - Distributed training strategies - Model training and hyperparameter tuning

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q250-Q255):

NEW QUESTION # 250
You are working with a large dataset containing millions of high-resolution images for a deep learning project. The dataset needs to be processed efficiently on a GPU before training a model.
Which NVIDIA technology is best suited for preprocessing, augmenting, and efficiently loading the dataset into memory?

A. NVIDIA Nsight Compute to optimize image dataset processing.
B. NVIDIA DALI (Data Loading Library) to accelerate data loading and preprocessing on the GPU.
C. NVIDIA Triton Inference Server to preprocess the dataset before model training.
D. NVIDIA RAPIDS cuDF to transform image data into tabular format for analysis.

Answer: B

NEW QUESTION # 251
You are a data scientist analyzing a social media network with NVIDIA cuGraph to identify the most influential users using the PageRank algorithm.
Which option best describes how cuGraph PageRank operates on a directed graph?

A. PageRank assigns equal importance to all nodes in the graph initially and updates values only based on outgoing edges, ignoring incoming edges.
B. PageRank in cuGraph is a label propagation algorithm that clusters nodes into communities rather than ranking their importance.
C. PageRank in cuGraph uses an iterative power method to update node importance values based on incoming edges, incorporating a damping factor to handle random jumps.
D. PageRank in cuGraph operates only on undirected graphs and cannot be applied to networks where edges have a direction.

Answer: C

NEW QUESTION # 252
You are processing a large dataset in a distributed computing environment using RAPIDS and Dask.
Your workflow involves frequent shuffling of data between partitions, leading to significant slowdowns.
Which of the following strategies is the best way to implement data caching to reduce shuffle overhead using NVIDIA technologies?

A. Use traditional disk-based caching by writing intermediate results to CSV files and reloading when needed.
B. Disable caching altogether to force a recomputation of results, ensuring up-to-date data processing.
C. Use a CPU-based caching solution like Memcached to store intermediate data before reloading into cuDF.
D. Enable GPU-accelerated caching with RAPIDS cuDF and persist intermediate results in GPU memory.

Answer: D

NEW QUESTION # 253
Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)

A. Using SQLAlchemy to interact with databases for large data processing
B. Using SQLite as a local database for large-scale data analysis
C. Using Dask for parallel processing of large datasets
D. Using Excel to manipulate large datasets
E. Using Apache Spark for distributed data processing

Answer: C,E

NEW QUESTION # 254
Which of the following is the primary benefit of using NVIDIA RAPIDS in the context of data science workflows?

A. It simplifies the creation of visualizations for large datasets
B. It speeds up the preprocessing and analysis of large datasets by utilizing GPUs
C. It enables real-time data streaming and analytics
D. It allows for the implementation of reinforcement learning algorithms

Answer: B

NEW QUESTION # 255
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