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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation	17%	- GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling
Data Manipulation and Software Literacy	19%	- GPU-accelerated data manipulation using cuDF 1. Groupby, apply, and aggregation operations 2. Data integration, joining, merging, and filtering 3. cuDF vs pandas API mapping and usage - Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency - Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing
Machine Learning	15%	- Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - Model training with GPU acceleration 1. Multi-GPU training strategies 2. Training models using cuML and GPU-accelerated XGBoost 3. Selection of appropriate algorithms for GPU execution - Feature engineering and hyperparameter tuning 1. Feature engineering for ML models 2. Batching and memory-efficient training methods 3. Hyperparameter tuning techniques
MLOps	19%	- Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - Model deployment and serving 1. Production deployment strategies 2. Model saving, loading, and prediction generation - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management
GPU and Cloud Computing	16%	- Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - Performance optimization 1. Memory profiling with DLProf 2. Single and multi-GPU performance optimization 3. Mixed precision and bottleneck analysis - GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science - GPU resource management 1. Efficient GPU resource allocation and scheduling
Data Analysis	14%	- Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting

>> NCP-ADS Exam Topics <<

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q256-Q261):

NEW QUESTION # 256
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. Use RAPIDS cuDF with batch processing to analyze historical anomalies every few hours
B. Apply cuML's Isolation Forest on incoming streaming data for real-time anomaly detection
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: B

NEW QUESTION # 257
A data scientist needs to process a dataset containing 10 million records, performing transformations and exploratory data analysis (EDA). The processing needs to be efficient but does not require high- performance multi-GPU execution.
Which of the following libraries provides the best balance between usability and performance?

A. Dask DataFrame, since it automatically parallelizes computations even when the dataset fits in memory.
B. Spark DataFrame, as it is optimized for distributed processing and scales well even for 10 million records.
C. Pandas, as it provides a simple API and works well for datasets that fit within system memory.
D. cuDF, since GPU acceleration will still provide a speedup even for moderately sized datasets.

Answer: C

NEW QUESTION # 258
You need to train a deep learning model using PyTorch on a dataset too large for a single GPU. You decide to use Dask with NVIDIA GPUs for multi-GPU scaling.
Which approach is the most effective for distributing the workload?

A. Use Dask's built-in deep learning API to automatically distribute PyTorch models across GPUs
B. Use Dask Bag to shard the dataset and train separate PyTorch models on each shard
C. Use Dask.delayed to wrap PyTorch training functions and schedule them across multiple GPUs
D. Use Dask-CUDA workers with PyTorch's DistributedDataParallel (DDP) for training across multiple GPUs

Answer: D

NEW QUESTION # 259
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. Use a system-wide CUDA installation and mount the /usr/local/cuda directory into the container to provide GPU support.
C. Build a container from an Ubuntu base image and manually install all dependencies without specifying versions.
D. Use the nvidia/cuda base image and specify the required RAPIDS and deep learning libraries in a Dockerfile.

Answer: D

NEW QUESTION # 260
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. Manually install CUDA and cuDNN inside the container by downloading them from NVIDIA's website and setting environment variables.
B. Use a base Ubuntu image and install TensorFlow, PyTorch, and CUDA using pip install inside the container.
C. Disable GPU acceleration in Docker and force computations on the CPU to avoid CUDA compatibility issues.
D. Use NVIDIA's official Docker images from NVIDIA GPU Cloud (NGC), which come with pre-installed CUDA and AI frameworks.

Answer: D

NEW QUESTION # 261
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