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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- GPU architecture and acceleration principles - Cloud GPU environments and deployment - Resource management and scaling strategies - CRISP-DM and data science methodology
Data Manipulation and Software Literacy	19%	- Dependency management and containerization - Data processing libraries selection and usage - GPU-accelerated ETL workflows - Performance profiling and optimization tools
MLOps	19%	- End-to-end workflow management - Monitoring, logging and maintenance - Pipeline automation and orchestration - Model deployment and serving
Machine Learning	15%	- GPU-accelerated ML frameworks and algorithms - Model evaluation and validation - Distributed training strategies - Model training and hyperparameter tuning
Data Analysis	14%	- Exploratory Data Analysis (EDA) - Time-series analysis and anomaly detection - Data visualization and graph analytics - Distributed and parallel data processing
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 (Q81-Q86):

NEW QUESTION # 81
A manufacturing company wants to detect anomalies in a real-time IoT sensor dataset using NVIDIA technologies. The data arrives as a continuous stream, and the company needs to process it efficiently for immediate anomaly detection.
Which NVIDIA-powered approach is the most suitable for this use case?

A. Apply cuML's Isolation Forest on incoming streaming data for real-time anomaly detection
B. Use RAPIDS cuDF with batch processing to analyze historical anomalies every few hours
C. Deploy an Autoencoder-based deep learning model using NVIDIA TensorRT for real-time inference
D. Leverage cuGraph to model the sensor readings as a graph and detect anomalies using centrality measures

Answer: A

NEW QUESTION # 82
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 cuDF's groupby().sum() function to count the number of connections per user.
B. Use cuGraph's bfs() (Breadth-First Search) to find the most influential nodes.
C. Use cuGraph's pagerank() function to identify influential nodes based on link structure.
D. Convert the graph into a pandas DataFrame and apply NetworkX's PageRank implementation.

Answer: C

NEW QUESTION # 83
Which of the following hardware components is most critical for accelerating the training of deep learning models?

A. Graphics Processing Unit (GPU)
B. Central Processing Unit (CPU)
C. Random Access Memory (RAM)
D. Solid State Drive (SSD)

Answer: A

NEW QUESTION # 84
You have a structured dataset containing 20 million records with missing values in several columns.
You need to fill missing values while ensuring that the approach is optimal for execution on NVIDIA GPUs.
Which method should you use?

A. Use cuDF's .fillna() method to replace missing values in GPU memory
B. Convert the dataset to a pandas DataFrame, fill missing values, and then convert it back to cuDF
C. Load the dataset into Modin with a Dask backend and use .fillna() for parallel execution
D. Drop all missing values using .dropna() instead of filling them, as GPU memory is limited

Answer: A

NEW QUESTION # 85
You are building a fraud detection system and have a dataset that includes a feature representing transaction amounts. The values range from a few cents to several thousand dollars.
What is the most appropriate data type for this feature?

A. Categorical (grouping transactions into bins such as "low", "medium", "high").
B. Boolean (True for transactions above $100, False otherwise).
C. Floating-point (with precision to store cents or fractions of a cent).
D. Integer (rounding all transaction amounts to whole dollars).

Answer: C

NEW QUESTION # 86
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