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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q218-Q223):

NEW QUESTION # 218
You are working on a large-scale social network analysis project using cuGraph. Your goal is to identify the most influential individuals in the network based on their connectivity and reach.
Which of the following cuGraph algorithms would be the most appropriate choice for this task?

A. Louvain Clustering
B. Single Source Shortest Path (SSSP)
C. Connected Components
D. PageRank

Answer: D

NEW QUESTION # 219
In the CRISP-DM process, which NVIDIA technology is commonly used during the "Data Preparation" phase to handle large datasets efficiently?

A. NVIDIA RAPIDS
B. NVIDIA CUDA Toolkit
C. NVIDIA Deep Learning AI
D. NVIDIA Jetson

Answer: A

NEW QUESTION # 220
You are working with a deep learning model for real-time inference on an NVIDIA GPU. Your objective is to optimize inference speed while maintaining acceptable model accuracy.
Which of the following techniques provides the best balance between inference performance and accuracy?

A. Use double-precision (FP64) computations to prevent numerical instability in inference
B. Use TensorRT for model optimization, including precision quantization and layer fusion
C. Disable batch inference to ensure each sample is processed independently for better accuracy
D. Increase the number of neurons in each layer to improve the model's accuracy

Answer: B

NEW QUESTION # 221
A data science team is deploying a deep learning model for real-time inference. The model is optimized for inference on an NVIDIA A100 GPU, but the team notices that inference latency is higher than expected.
Which of the following optimizations is most effective in reducing inference latency?

A. Reduce the model size by randomly pruning neurons without retraining.
B. Use mixed-precision inference with TensorRT to accelerate computation.
C. Enable CPU offloading to balance the workload between the CPU and GPU.
D. Increase the batch size significantly to improve GPU utilization.

Answer: B

NEW QUESTION # 222
You are working with a dataset containing 2 billion rows of financial transactions, and you need to perform exploratory data analysis (EDA) before building a predictive model.
Which of the following approaches is the most appropriate for handling this data efficiently?

A. Use an accelerated data science framework such as RAPIDS cuDF or Dask to distribute computations across GPUs.
B. Convert the dataset to JSON format and use Python's built-in json module to parse and analyze it efficiently.
C. Use SQLite to store the data locally and run queries sequentially to minimize memory consumption.
D. Load the entire dataset into a Pandas DataFrame and analyze it using Pandas built-in methods.

Answer: A

NEW QUESTION # 223
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