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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 - GPU-accelerated ETL workflows - Performance profiling and optimization tools - Dependency management and containerization
Machine Learning	15%	- Distributed training strategies - Model evaluation and validation - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms
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
MLOps	19%	- End-to-end workflow management - Model deployment and serving - Pipeline automation and orchestration - Monitoring, logging and maintenance
Data Preparation	17%	- Data validation and quality assurance - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization
Data Analysis	14%	- Exploratory Data Analysis (EDA) - Data visualization and graph analytics - Time-series analysis and anomaly detection - Distributed and parallel data processing

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

NEW QUESTION # 278
You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

A. Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold
B. Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format
C. Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations
D. Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence

Answer: A

NEW QUESTION # 279
When comparing the required memory with the available memory on a GPU for an MLOps deployment using NVIDIA technologies, which of the following is the best method to optimize memory usage while training large models?

A. Use mixed-precision training to reduce memory requirements by using half-precision floating-point numbers.
B. Use a higher number of GPUs to distribute the model and memory load across the GPUs.
C. Decrease the number of training iterations to reduce memory consumption.
D. Increase the input data size to fully utilize available memory and improve training performance.

Answer: A

NEW QUESTION # 280
You are working on a medium-sized dataset (~500,000 rows, 20 columns) and need to perform fast exploratory data analysis (EDA) with filtering, aggregations, and transformations.
Which of the following Python libraries would be the most efficient choice for this task?

A. Vaex
B. Dask
C. Pandas
D. PySpark

Answer: C

NEW QUESTION # 281
A data scientist is working on a social network analysis project where they need to find the most influential users in a large-scale graph dataset. The dataset consists of millions of users connected through directed edges.
Which of the following approaches would be the best choice for this task using NVIDIA GPU-accelerated tools?

A. Use cuGraph's bfs() (Breadth-First Search) to find the most influential nodes.
B. Use cuGraph's pagerank() function to identify influential nodes based on link structure.
C. Convert the graph into a pandas DataFrame and apply NetworkX's PageRank implementation.
D. Use cuDF's groupby().sum() function to count the number of connections per user.

Answer: B

NEW QUESTION # 282
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 Triton Inference Server to preprocess the dataset before model training.
C. NVIDIA DALI (Data Loading Library) to accelerate data loading and preprocessing on the GPU.
D. NVIDIA RAPIDS cuDF to transform image data into tabular format for analysis.

Answer: C

NEW QUESTION # 283
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