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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Analysis	14%	- Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals
Topic 2: Data Manipulation and Software Literacy	19%	- GPU-accelerated data manipulation using cuDF 1. Groupby, apply, and aggregation operations 2. cuDF vs pandas API mapping and usage 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. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
Topic 3: Data Preparation	17%	- 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 loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet
Topic 4: GPU and Cloud Computing	16%	- Performance optimization 1. Memory profiling with DLProf 2. Mixed precision and bottleneck analysis 3. Single and multi-GPU performance optimization - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - GPU architecture and fundamentals 1. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization - GPU resource management 1. Efficient GPU resource allocation and scheduling
Topic 5: MLOps	19%	- Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - 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. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools
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. Batching and memory-efficient training methods 2. Hyperparameter tuning techniques 3. Feature engineering for ML models

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

NEW QUESTION # 77
You are implementing a GPU-accelerated ETL pipeline that involves joining two large datasets:
Dataset A: A cuDF DataFrame with 10 million customer records.
Dataset B: A cuDF DataFrame with 100 million transaction records.
The goal is to efficiently perform a join operation to link customer details with transaction data, ensuring that the pipeline remains scalable and performant.
Which of the following is the best approach to optimize the join operation using NVIDIA RAPIDS?

A. Use cuDF's .merge() function and ensure both DataFrames have the correct index before joining
B. Perform the join operation entirely in a CPU-based Spark environment for better stability
C. Store the transaction data as a CSV file and perform joins using SQL queries before loading it into cuDF
D. Convert both DataFrames to Pandas before performing the join for compatibility

Answer: A

NEW QUESTION # 78
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.delayed to wrap PyTorch training functions and schedule them across multiple GPUs
C. Use Dask-CUDA workers with PyTorch's DistributedDataParallel (DDP) for training across multiple GPUs
D. Use Dask Bag to shard the dataset and train separate PyTorch models on each shard

Answer: C

NEW QUESTION # 79
A data engineering team is designing an ETL pipeline to process large-scale financial transaction data. They want to leverage NVIDIA-accelerated ETL tools to extract data from a data lake, transform it by filtering and aggregating key fields, and load it into a data warehouse.
Which of the following approaches provides the most efficient ETL processing using NVIDIA technologies?

A. Write a custom ETL script in pure Python to handle data extraction, transformation, and loading
B. Perform all transformations using Pandas DataFrames before loading the data into the GPU
C. Use RAPIDS cuDF to preprocess data in-memory and BlazingSQL to accelerate SQL-based transformations
D. Use Dask on CPUs for distributed ETL processing and later move results to a GPU-based database

Answer: C

NEW QUESTION # 80
Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?

A. Log Transformation
B. Min-Max Scaling
C. Robust Scaling
D. Z-score Standardization

Answer: C

NEW QUESTION # 81
You are performing data cleansing on a large dataset using CuDF. The dataset contains numerical values, some of which are outliers. You need to remove or adjust these outliers to make your model training more robust.
Which of the following approaches should you consider for handling outliers efficiently in CuDF? (Select two)

A. Using quantile() to calculate the interquartile range (IQR) and filter out outliers
B. Using applymap() to apply a custom function for handling outliers
C. Using dropna() to remove rows with outliers
D. Using clip() to set a maximum and minimum threshold for numerical values

Answer: A,D

NEW QUESTION # 82
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