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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q290-Q295):

NEW QUESTION # 290
When utilizing GPU instances in a cloud environment for data science, which of the following are common considerations? (Select two)

A. GPU performance can be degraded by network latency in distributed training
B. Cost-effective GPU instances always provide the best performance
C. Cloud providers offer dedicated GPU instances for high-performance needs
D. GPUs are suitable for both training and inference tasks but not for preprocessing

Answer: A,C

NEW QUESTION # 291
A data scientist is setting up a RAPIDS AI environment for a machine learning project that requires CUDA-enabled libraries and specific package versions to avoid conflicts.
Which of the following approaches best ensures a stable and reproducible environment while leveraging NVIDIA technologies?

A. Install CUDA and RAPIDS AI libraries using OS-level package managers such as apt or yum instead of Conda or Docker.
B. Use a system-wide Python installation and install packages globally to ensure consistency across different users.
C. Use Conda with the rapidsai channel to create an isolated environment that includes cuDF, cuML, and other GPU-accelerated libraries.
D. Manually install each required package with pip to ensure the latest versions are used without interference from Conda.

Answer: C

NEW QUESTION # 292
Which of the following techniques are commonly used to identify and acquire datasets for machine learning and data science projects? (Select three)

A. Manual data entry
B. Crowdsourcing
C. Data augmentation
D. Public APIs
E. Web scraping

Answer: B,D,E

NEW QUESTION # 293
You are working on a data science project where you need to process a large dataset containing
500 million records. You want to determine whether GPU acceleration would significantly improve performance.
Which of the following factors best indicates that you should use an accelerated computing solution like RAPIDS?

A. The dataset is a structured table with less than 100,000 records and can be handled efficiently with a Pandas DataFrame.
B. The dataset is heavily structured but mainly requires text-based analysis using regex-based search and manipulation.
C. The dataset has high-dimensional sparse features and requires complex operations such as nearest neighbor search and clustering.
D. The dataset consists of simple arithmetic operations on a few columns and can be processed using vectorized NumPy operations.

Answer: C

NEW QUESTION # 294
You have deployed a deep learning model for image classification in a production environment, but inference latency is high. You need to optimize the model to reduce response time while maintaining accuracy.
Which NVIDIA technology is best suited for this task?

A. NVIDIA TensorRT to optimize and accelerate deep learning inference by reducing model size and execution time.
B. NVIDIA RAPIDS cuML to optimize deep learning inference using GPU-accelerated ML algorithms.
C. NVIDIA DeepStream to process image classification models for low-latency inference in batch mode.
D. NVIDIA Clara Imaging to improve deep learning inference for image classification workloads.

Answer: A

NEW QUESTION # 295
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