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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q284-Q289):

NEW QUESTION # 284
You are working on a financial fraud detection system using NVIDIA RAPIDS cuML. You have a large time-series dataset of transaction amounts over time, and you need to identify anomalies such as unusual spikes in transactions.
Which of the following approaches is the most appropriate for detecting anomalies using NVIDIA technologies?

A. Apply cuML's PCA-based anomaly detection to detect outliers in the principal component space
B. Run standard Pandas-based anomaly detection methods on CPU for better precision
C. Use cuML's DBSCAN clustering to identify anomalies based on density differences
D. Train a deep learning model with TensorFlow and deploy it using NVIDIA Triton Inference Server for anomaly detection

Answer: A

NEW QUESTION # 285
A retail company is deploying an AI-driven demand forecasting system using NVIDIA GPUs. The team follows the CRISP-DM framework and is currently in the Evaluation phase.
Which approach best leverages NVIDIA technologies to assess model performance effectively?

A. Perform evaluation on a small CPU-based subset of the dataset instead of using full GPU-accelerated inference.
B. Rely only on training loss as the primary evaluation metric without considering validation performance.
C. Use RAPIDS cuML to rapidly compute evaluation metrics like RMSE and R-squared on large datasets using GPUs.
D. Assume that a high training accuracy guarantees excellent real-world performance, skipping the evaluation phase.

Answer: C

NEW QUESTION # 286
You are selecting a dataset for GPU-accelerated data science using NVIDIA RAPIDS.
Which dataset characteristic is most suitable for taking advantage of GPU acceleration?

A. A large, structured dataset stored in a Parquet format
B. A dataset stored as an SQLite database file
C. A small dataset with fewer than 1,000 rows stored in an Excel file
D. A dataset consisting of unstructured text stored as plain .txt files

Answer: A

NEW QUESTION # 287
You are training a convolutional neural network (CNN) model with a large dataset on a single GPU.
The model is consuming too much GPU memory, and training is slow.
Which of the following techniques would help you reduce GPU memory consumption while maintaining or improving the efficiency of training? (Select two)

A. Use batch normalization to reduce memory usage by skipping some of the computations during training.
B. Implement mixed precision training to lower memory usage and speed up computation without losing accuracy.
C. Decrease the size of the model by reducing the number of layers or the number of filters per layer.
D. Use automatic differentiation for every operation to save memory during the backpropagation phase.
E. Use a smaller batch size to reduce the memory requirements per training iteration, allowing for faster training.

Answer: B,E

NEW QUESTION # 288
A financial analyst wants to create an interactive GPU-accelerated dashboard to visualize stock price movements in real-time.
Which NVIDIA-supported tool is best suited for this purpose?

A. Rely on Matplotlib to generate static plots and update them every minute with a loop.
B. Precompute the time-series visualization with Dask and display it in a static HTML page.
C. Use Plotly Dash with RAPIDS cuDF to create an interactive GPU-powered dashboard.
D. Convert the stock price dataset into a NumPy array and visualize it using Seaborn's line plot.

Answer: C

NEW QUESTION # 289
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