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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation	17%	- Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - 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. 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
GPU and Cloud Computing	16%	- Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - 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 - GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science
Data Analysis	14%	- Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - 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 - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis
Machine Learning	15%	- Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - 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 - Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Batching and memory-efficient training methods 3. Feature engineering for ML models
Data Manipulation and Software Literacy	19%	- 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 - 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
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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q213-Q218):

NEW QUESTION # 213
You are tasked with cleansing a dataset containing numerical data that has significant outliers.
You're using pandas to identify and appropriately handle these outliers before applying CuDF for accelerated downstream analysis.
Which method effectively manages the numerical outliers while preserving the dataset's integrity for subsequent accelerated analytics?

A. Fill outlier values with mean using pandas.Series.fillna().
B. Replace outliers with zero using pandas.Series.replace().
C. Clip outliers using pandas.Series.clip() based on percentile thresholds (e.g., 5th and 95th percentile).
D. Remove outliers completely using pandas.DataFrame.drop().

Answer: C

NEW QUESTION # 214
A data scientist is working on a customer segmentation model using NVIDIA RAPIDS on GPUs. The dataset contains millions of customer records with features such as transaction history, age, location, and frequency of visits.
To optimize feature engineering using NVIDIA technologies, what is the best approach?

A. Convert all categorical variables into string representations to preserve their original format for later analysis.
B. Store all numerical features in float64 format to prevent rounding errors during transformations.
C. Use cuDF to perform feature transformations like normalization and one-hot encoding directly on the GPU.
D. Perform feature engineering on CPUs using pandas before transferring data to the GPU for training.

Answer: C

NEW QUESTION # 215
A machine learning team is handling large-scale datasets that need to be efficiently stored and accessed within an NVIDIA RAPIDS workflow.
Which of the following storage formats and techniques provides the best performance for GPU-based data science pipelines?

A. Use CSV files for storage, as they are widely compatible and can be read quickly using cuDF's read_csv() method.
B. Save data in JSON format to ensure hierarchical relationships and flexibility before loading it into cuDF.
C. Store data in SQLite databases and query it into pandas before converting it to cuDF.
D. Store data in the Apache Parquet format and load it directly into cuDF using cuDF's read_parquet() method.

Answer: D

NEW QUESTION # 216
You are working with a large time-series dataset consisting of millions of records and want to efficiently visualize trends over time using NVIDIA technologies. The dataset is stored as a cuDF DataFrame, and you need to generate an interactive line plot with minimal performance overhead.
Which of the following is the best approach to achieve this goal?

A. Use the Bokeh library to plot the time-series data from a cuDF DataFrame directly
B. Use the hvPlot library with RAPIDS cuDF to directly render the time-series data interactively
C. Load the data into a Spark DataFrame and visualize using Apache Zeppelin
D. Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib

Answer: B

NEW QUESTION # 217
You are setting up a GPU-accelerated data science environment on a cloud-based instance that utilizes NVIDIA GPUs.
To ensure compatibility between CUDA, RAPIDS, and Python libraries, which of the following is the most effective approach for managing dependencies and avoiding version conflicts?

A. Use a virtual machine with all dependencies pre-installed to avoid any software conflicts
B. Use Conda environments with version-pinned dependencies to create an isolated environment for RAPIDS and CUDA
C. Install RAPIDS, PyTorch, TensorFlow, and other libraries in the same Conda base environment for simplified access
D. Manually install required libraries in a system-wide Python environment using pip install without version specifications

Answer: B

NEW QUESTION # 218
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