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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: MLOps	19%	- 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 - 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
Topic 2: GPU and Cloud Computing	16%	- GPU architecture and fundamentals 1. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization - Performance optimization 1. Single and multi-GPU performance optimization 2. Memory profiling with DLProf 3. Mixed precision and bottleneck analysis - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - GPU resource management 1. Efficient GPU resource allocation and scheduling
Topic 3: Data Preparation	17%	- 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 - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling
Topic 4: 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 - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets
Topic 5: Data Manipulation and Software Literacy	19%	- GPU-accelerated data manipulation using cuDF 1. Data integration, joining, merging, and filtering 2. cuDF vs pandas API mapping and usage 3. Groupby, apply, and aggregation operations - Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing - Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
Topic 6: Machine Learning	15%	- Model training with GPU acceleration 1. Selection of appropriate algorithms for GPU execution 2. Multi-GPU training strategies 3. Training models using cuML and GPU-accelerated XGBoost - Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Feature engineering for ML models 3. Hyperparameter tuning techniques - Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch

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NVIDIA-Certified-Professional Accelerated Data Science Training Pdf Vce & NCP-ADS Exam Study Guide & NVIDIA-Certified-Professional Accelerated Data Science Free Practice Pdf

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q91-Q96):

NEW QUESTION # 91
You are comparing the performance of NVIDIA RAPIDS cuML, TensorFlow, and PyTorch for training and inference on a dataset with millions of records.
To design a fair and effective benchmark, which approach should you take?

A. Run each framework on different GPUs to maximize available resources and compare execution times across different hardware configurations.
B. Use only a CPU baseline for comparison to demonstrate the benefits of GPU acceleration, ignoring GPU-specific optimizations.
C. Ensure all frameworks run on the same GPU, use optimized batch sizes, and measure execution time and memory usage with NVIDIA Nsight Systems.

Answer: C

NEW QUESTION # 92
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 Nsight Systems to collect MRI data and visualize potential performance bottlenecks.
C. Use NVIDIA Clara Imaging to standardize, curate, and preprocess MRI datasets for AI model training.
D. Use NVIDIA Morpheus to analyze MRI data for cybersecurity threats before storing it.

Answer: C

NEW QUESTION # 93
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. Use RAPIDS cuML to rapidly compute evaluation metrics like RMSE and R-squared on large datasets using GPUs.
C. Rely only on training loss as the primary evaluation metric without considering validation performance.
D. Assume that a high training accuracy guarantees excellent real-world performance, skipping the evaluation phase.

Answer: B

NEW QUESTION # 94
You are analyzing a dataset containing millions of transaction records to detect fraudulent transactions. The dataset is highly imbalanced, with fraudulent cases making up only 0.5% of the data.
Which data analysis strategy will most effectively handle the imbalance while minimizing the risk of overfitting?

A. Random oversampling of the minority class
B. SMOTE (Synthetic Minority Over-sampling Technique)
C. Random undersampling of the majority class
D. Stratified random sampling

Answer: C

NEW QUESTION # 95
Which of the following is the primary benefit of using NVIDIA RAPIDS in the context of data science workflows?

A. It enables real-time data streaming and analytics
B. It simplifies the creation of visualizations for large datasets
C. It speeds up the preprocessing and analysis of large datasets by utilizing GPUs
D. It allows for the implementation of reinforcement learning algorithms

Answer: C

NEW QUESTION # 96
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