NCP-ADS Exam Papers, New NCP-ADS Test Objectives

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q191-Q196):

NEW QUESTION # 191
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. Betweenness Centrality, as it identifies airports that act as major transit hubs by measuring how frequently they appear in shortest paths.
B. Degree Centrality, as it measures the number of direct connections an airport has, indicating its importance in the network.
C. Closeness Centrality, as it measures how close an airport is to all other airports, making it the best indicator of global connectivity.
D. Triangle Count, as it determines the number of three-node cycles an airport is part of, indicating its importance in network structure.

Answer: A

NEW QUESTION # 192
A machine learning engineer wants to deploy a GPU-accelerated inference model in a containerized environment while ensuring compatibility with NVIDIA libraries.
Which of the following is the best approach for managing dependencies?

A. Install GPU drivers directly inside the container instead of on the host system to avoid dependency conflicts.
B. Run Docker containers without any special configurations, as Docker automatically detects and utilizes GPUs.
C. Disable the --gpus flag when running Docker containers, as RAPIDS AI libraries do not require explicit GPU selection.
D. Use the official NVIDIA Docker base images (nvidia/cuda) and install RAPIDS AI libraries within the container to ensure GPU compatibility.

Answer: D

NEW QUESTION # 193
You are working with a dataset containing billions of rows and need to perform data transformations, aggregations, and joins efficiently on a single-node GPU-enabled workstation.
Which NVIDIA technology is best suited to optimize performance for these operations?

A. NVIDIA Nsight Compute to profile and optimize the performance of GPU-based aggregations.
B. NVIDIA RAPIDS cuDF to leverage GPU acceleration for large-scale DataFrame operations.
C. NVIDIA TensorRT to optimize DataFrame transformations and aggregations using deep learning.
D. NVIDIA Triton Inference Server to accelerate data processing workflows on a single GPU.

Answer: B

NEW QUESTION # 194
You are working on a financial dataset that tracks stock prices over time, and you need to detect anomalies such as sudden spikes or drops using NVIDIA technologies.
Which of the following approaches would be the most effective for anomaly detection in a time-series dataset using NVIDIA's RAPIDS AI and TensorRT?

A. Use RAPIDS cuML's Isolation Forest for anomaly detection and deploy it with NVIDIA Triton Inference Server.
B. Apply a traditional rule-based thresholding method using pandas and NumPy for detecting sudden spikes in stock prices.
C. Perform anomaly detection by applying DBSCAN clustering with RAPIDS cuML without any feature engineering.
D. Use traditional ARIMA modeling with RAPIDS cuML to classify anomalies based on residual analysis.

Answer: A

NEW QUESTION # 195
You are working with a large dataset in a cloud environment for a deep learning model. The dataset consists of several features including numerical values, categorical data, and timestamps.
Which of the following choices would result in the most efficient use of GPU and cloud resources when determining the optimal data type for each feature? (Select three)

A. Use int32 for all numerical features to save memory.
B. Use datetime64[ns] for timestamp features to ensure high precision.
C. Use float64 for all numerical features to ensure maximum precision.
D. Use object data types for categorical features to avoid type conversion overhead.
E. Use int8 for categorical features where there are fewer than 256 categories.

Answer: A,B,E

NEW QUESTION # 196
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