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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 - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation
Topic 2: Machine Learning	15%	- Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Feature engineering for ML models 3. Hyperparameter tuning techniques - Model training with GPU acceleration 1. Training models using cuML and GPU-accelerated XGBoost 2. Selection of appropriate algorithms for GPU execution 3. Multi-GPU training strategies - Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch
Topic 3: Data Manipulation and Software Literacy	19%	- 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 - GPU-accelerated data manipulation using cuDF 1. cuDF vs pandas API mapping and usage 2. Data integration, joining, merging, and filtering 3. Groupby, apply, and aggregation operations
Topic 4: GPU and Cloud Computing	16%	- Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - 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. Single and multi-GPU performance optimization 3. Mixed precision and bottleneck analysis - GPU resource management 1. Efficient GPU resource allocation and scheduling
Topic 5: Data Preparation	17%	- Data loading and preprocessing 1. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - 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
Topic 6: Data Analysis	14%	- Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization

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The NVIDIA-Certified-Professional Accelerated Data Science (NCP-ADS) certification is a valuable credential that every NVIDIA professional should earn it. The NVIDIA NCP-ADS certification exam offers a great opportunity for beginners and experienced professionals to demonstrate their expertise. With the NVIDIA-Certified-Professional Accelerated Data Science (NCP-ADS) certification exam everyone can upgrade their skills and knowledge. There are other several benefits that the NCP-ADS Exam holders can achieve after the success of the NVIDIA-Certified-Professional Accelerated Data Science (NCP-ADS) certification exam. However, you should keep in mind to pass the NVIDIA NCP-ADS certification exam is not an easy task. It is a challenging job.

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q77-Q82):

NEW QUESTION # 77
When comparing the required memory with the available memory on a GPU for an MLOps deployment using NVIDIA technologies, which of the following is the best method to optimize memory usage while training large models?

A. Increase the input data size to fully utilize available memory and improve training performance.
B. Use mixed-precision training to reduce memory requirements by using half-precision floating-point numbers.
C. Use a higher number of GPUs to distribute the model and memory load across the GPUs.
D. Decrease the number of training iterations to reduce memory consumption.

Answer: B

NEW QUESTION # 78
You are comparing the performance of GPU-accelerated deep learning models on two cloud platforms: AWS EC2 and Google Cloud Platform (GCP). You want to design a benchmark that evaluates GPU resource utilization, processing time, and cost-efficiency for training models with large datasets.
Which actions should you take to implement an effective benchmark? (Select two)

A. Limit the benchmarking tests to a single model architecture for each cloud platform.
B. Run each model on the same GPU type and instance size across both cloud platforms.
C. Measure both GPU utilization and network latency between the cloud platform and your data storage location.
D. Only consider the cost of GPU resources for each cloud provider without factoring in data storage and transfer costs.

Answer: B,C

NEW QUESTION # 79
You are working with a large dataset containing customer transactions and want to perform exploratory data analysis (EDA) efficiently. Given the dataset's size, you decide to use NVIDIA RAPIDS to accelerate the process.
Which of the following approaches is the most effective for conducting EDA using NVIDIA technologies?

A. Use TensorFlow and Keras to preprocess the data before performing EDA
B. Use RAPIDS cuDF to perform fast dataframe operations and visualize results with cuXfilter
C. Perform SQL queries on a CPU-based database for initial data analysis before GPU acceleration
D. Load the dataset into Pandas and use Matplotlib for visualization

Answer: B

NEW QUESTION # 80
You are analyzing a time-series dataset that represents temperature readings from an industrial sensor. Your goal is to detect anomalies that may indicate sensor failures or environmental changes.
Which of the following methods would be the most appropriate statistical technique for detecting anomalies in this time-series dataset?

A. Z-score (Standard Score) Analysis
B. One-Hot Encoding
C. Moving Average Smoothing
D. Principal Component Analysis (PCA)

Answer: A

NEW QUESTION # 81
You are tasked with profiling a PyTorch-based deep learning model to identify performance bottlenecks using NVIDIA DLProf. Your goal is to analyze kernel execution times and identify operations causing excessive memory consumption.
Which of the following steps is the MOST appropriate sequence for profiling using DLProf?

A. Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.
B. Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
C. Execute the training script under DLProf TensorBoard mode to visualize performance insights, then re-run the model with automatic mixed precision (AMP) to reduce memory usage.
D. Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.

Answer: B

NEW QUESTION # 82
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