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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - GPU resource management 1. Efficient GPU resource allocation and scheduling - Performance optimization 1. Mixed precision and bottleneck analysis 2. Single and multi-GPU performance optimization 3. Memory profiling with DLProf
Topic 2: Data Manipulation and Software Literacy	19%	- GPU-accelerated data manipulation using cuDF 1. Data integration, joining, merging, and filtering 2. Groupby, apply, and aggregation operations 3. cuDF vs pandas API mapping and usage - 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
Topic 3: 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. 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
Topic 4: Data Preparation	17%	- Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - Data loading and preprocessing 1. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data - Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet
Topic 5: MLOps	19%	- Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - 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 6: 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. 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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q93-Q98):

NEW QUESTION # 93
You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.
Which approach best utilizes NVIDIA technologies for efficient forecasting?

A. Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.
B. Use PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.
C. Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
D. Use cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.

Answer: D

NEW QUESTION # 94
You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

A. Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence
B. Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format
C. Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold
D. Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations

Answer: C

NEW QUESTION # 95
Which of the following best describes the functionality of DLProf in deep learning model profiling?

A. DLProf tracks GPU utilization, memory bandwidth, and kernel execution time to identify performance bottlenecks.
B. DLProf uses statistical methods to predict future model performance based on historical data.
C. DLProf can only profile the CPU side of a deep learning model's performance.
D. DLProf is used for visualizing model predictions, similar to TensorBoard.

Answer: A

NEW QUESTION # 96
When scaling a distributed data processing framework using NVIDIA GPU technology for big data processing, which of the following factors is most critical to optimize performance?

A. Using more CPU cores to handle computation-heavy tasks.
B. Limiting the number of GPU nodes used in the cluster to avoid complexity.
C. Ensuring the proper configuration of GPU resources across all nodes in the distributed system.
D. Maximizing the amount of data transferred between GPUs for faster processing.

Answer: C

NEW QUESTION # 97
A machine learning team needs to process terabytes of image metadata stored in a distributed storage system. They want to leverage GPU acceleration to speed up preprocessing and transformation while ensuring efficient parallel access.
Which of the following approaches best aligns with NVIDIA's accelerated data science ecosystem?

A. Process metadata using PySpark on a CPU cluster before sending it to a GPU.
B. Store metadata in a traditional SQL database and query it using Pandas.
C. Use SQLite for metadata storage and process it with RAPIDS cuDF.
D. Use Dask-cuDF with Parquet files stored in an object storage system like S3.

Answer: D

NEW QUESTION # 98
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