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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Manipulation and Software Literacy	19%	- ETL and Data Processing Workflows 1. Distributed data processing frameworks (Dask) 2. GPU-accelerated ETL design and implementation 3. Data caching and performance optimization
MLOps	19%	- Deployment and Monitoring 1. Model deployment in production environments 2. Memory and capacity evaluation 3. Performance benchmarking and optimization
GPU and Cloud Computing	16%	- GPU Optimization and Infrastructure 1. Benchmarking GPU workflows 2. Docker and Conda environment management 3. CRISP-DM workflow execution
Data Preparation	17%	- Data Cleaning and Transformation 1. Data normalization and standardization 2. Synthetic data generation with RAPIDS 3. cuDF and pandas data preprocessing
Machine Learning	15%	- Model Development and Optimization 1. Hyperparameter tuning 2. Multi-GPU training comparison 3. Memory optimization techniques (mixed precision, batching) 4. Feature engineering
Data Analysis	14%	- Exploratory Data Analysis (EDA) 1. Use cuGraph for graph analytics 2. Perform time series analysis and visualization 3. Detect anomalies in time series datasets

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q78-Q83):

NEW QUESTION # 78
You are working on a machine learning problem that involves training a deep learning model on a dataset with billions of records. The dataset is stored in a distributed cloud storage system.
Given the need for acceleration, which is the most effective approach?

A. Reduce the dataset to a small representative sample to avoid the need for specialized acceleration.
B. Load the entire dataset into RAM on a single powerful CPU-based machine before starting model training.
C. Use GPU acceleration with libraries like RAPIDS AI or TensorFlow to leverage parallel processing.
D. Store the dataset in a relational database and query it sequentially using SQL before training the model.

Answer: C

NEW QUESTION # 79
You are developing an AI model for medical imaging that requires acquiring a large dataset of MRI scans from multiple sources.
Which NVIDIA technology would best assist in acquiring, standardizing, and efficiently handling the dataset?

A. Use NVIDIA AI Enterprise to directly acquire and store MRI images from multiple hospitals.
B. Use NVIDIA Clara Imaging to standardize, curate, and preprocess MRI datasets for AI model training.
C. Use NVIDIA Nsight Systems to collect MRI data and visualize potential performance bottlenecks.
D. Use NVIDIA Morpheus to analyze MRI data for cybersecurity threats before storing it.

Answer: B

NEW QUESTION # 80
Which of the following best describes the purpose of the NVIDIA TensorRT library?

A. Provides hardware abstraction for AI model development
B. Manages GPU resources for deep learning models
C. Accelerates training of neural networks
D. Optimizes and accelerates inference of trained models

Answer: D

NEW QUESTION # 81
You need to set up an isolated, GPU-accelerated environment for a deep learning project that requires specific CUDA, cuDNN, and RAPIDS versions.
Which of the following best ensures a reproducible environment using Docker?

A. Install NVIDIA drivers manually inside a Docker container every time it runs.
B. Build a container from an Ubuntu base image and manually install all dependencies without specifying versions.
C. Use a system-wide CUDA installation and mount the /usr/local/cuda directory into the container to provide GPU support.
D. Use the nvidia/cuda base image and specify the required RAPIDS and deep learning libraries in a Dockerfile.

Answer: D

NEW QUESTION # 82
A financial services company is deploying an AI-driven risk assessment model using NVIDIA GPUs on a cloud platform. To optimize resource utilization and cost efficiency, they need to determine the best GPU deployment strategy.
Which of the following is the most effective approach?

A. Run all AI workloads on a single large GPU instance without any partitioning or workload separation.
B. Deploy the model using NVIDIAAI Enterprise with MIG (Multi-Instance GPU) to allocate multiple workloads on a single GPU efficiently.
C. Deploy separate full-GPU instances for each workload, even if they have variable compute demands.
D. Choose an on-demand cloud instance with an outdated GPU model to reduce costs, even if performance is compromised.

Answer: B

NEW QUESTION # 83
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