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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q223-Q228):

NEW QUESTION # 223
Which tools or technologies from NVIDIA are essential for implementing an efficient MLOps pipeline in production environments? (Select two)

A. NVIDIA CUDA for model training in cloud environments
B. NVIDIA Triton Inference Server for managing deployment and serving models
C. NVIDIA NGC for storing and sharing machine learning datasets
D. NVIDIA DLA (Deep Learning Accelerator) for model deployment
E. NVIDIA TensorRT for efficient model inference

Answer: B,E

NEW QUESTION # 224
You are working on a deep learning project that requires a large dataset of high-resolution satellite images for training a convolutional neural network (CNN). You want to leverage NVIDIA technologies to efficiently acquire and manage the dataset.
Which of the following approaches is the most suitable?

A. Use NVIDIA DeepStream to acquire satellite images and store them in a structured dataset for machine learning.
B. Use NVIDIA DALI (Data Loading Library) to stream and preprocess satellite image data efficiently for deep learning training.
C. Use NVIDIA RAPIDS cuDF to directly download and preprocess satellite images from an API in real time.
D. Use NVIDIA Modulus to generate synthetic satellite images instead of acquiring real-world data.

Answer: B

NEW QUESTION # 225
A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of- memory (OOM) errors.
To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?

A. Increasing batch size without adjusting the optimizer settings
B. Storing all training data in GPU memory at once
C. Using mixed precision training with automatic loss scaling
D. Using single-precision (FP32) calculations for better accuracy

Answer: C

NEW QUESTION # 226
Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?

A. TensorRT
B. NVIDIA Tesla
C. NVIDIA Jetson
D. NVIDIA A100

Answer: D

NEW QUESTION # 227
You are deploying an NVIDIA GPU-accelerated machine learning model in a Docker container and want to ensure that your application can leverage the GPU efficiently.
What is the best way to manage CUDA dependencies and avoid compatibility issues inside your Docker container?

A. Use NVIDIA's official Docker images from NVIDIA GPU Cloud (NGC), which come with pre-installed CUDA and AI frameworks.
B. Manually install CUDA and cuDNN inside the container by downloading them from NVIDIA's website and setting environment variables.
C. Disable GPU acceleration in Docker and force computations on the CPU to avoid CUDA compatibility issues.
D. Use a base Ubuntu image and install TensorFlow, PyTorch, and CUDA using pip install inside the container.

Answer: A

NEW QUESTION # 228
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