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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
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. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) - GPU-accelerated data manipulation using cuDF 1. cuDF vs pandas API mapping and usage 2. Groupby, apply, and aggregation operations 3. Data integration, joining, merging, and filtering
Data Analysis	14%	- 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 - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis
MLOps	19%	- Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - 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 - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility
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. Hyperparameter tuning techniques 3. Feature engineering for ML models - Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts
Data Preparation	17%	- GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - 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 - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables
GPU and Cloud Computing	16%	- 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 - Performance optimization 1. Single and multi-GPU performance optimization 2. Memory profiling with DLProf 3. Mixed precision and bottleneck analysis - GPU architecture and fundamentals 1. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q160-Q165):

NEW QUESTION # 160
You are developing an end-to-end data pipeline that processes terabytes of image metadata using NVIDIA technologies. You need a software stack that efficiently integrates GPU-accelerated data processing, machine learning, and visualization.
Which of the following tool combinations is best suited for this task?

A. Pandas for data manipulation, XGBoost for machine learning, and Matplotlib for visualization.
B. cuDF for data manipulation, cuML for machine learning, and Plotly for visualization.
C. Excel for data analysis, scikit-learn for machine learning, and Seaborn for visualization.
D. Hadoop for data storage, NumPy for computations, and TensorFlow for visualization.

Answer: B

NEW QUESTION # 161
You are tasked with profiling a deep learning model using NVIDIA's DLProf to identify performance bottlenecks and optimize resource utilization.
Which of the following statements correctly describes the capabilities of DLProf?

A. DLProf requires significant modifications to the source code to collect profiling data.
B. DLProf is primarily designed for debugging model accuracy rather than performance analysis.
C. DLProf can generate detailed reports that highlight kernel-level execution times and GPU utilization trends.
D. DLProf only works with TensorFlow models and does not support PyTorch-based workloads.

Answer: C

NEW QUESTION # 162
A data engineer is preparing a dataset for training a deep learning model. The dataset contains numerical features with missing values, outliers, and inconsistent units.
Which of the following strategies is the most appropriate for ensuring a standardized and clean dataset?

A. Use the median to fill missing values, convert all numerical values into categorical bins, and apply Min-Max scaling.
B. Remove all rows with missing values and outliers to ensure only clean data is used.
C. Standardize the dataset using the mean and standard deviation, but keep missing values and outliers unchanged to avoid data manipulation.
D. Replace missing values with the mean, apply z-score normalization, and clip extreme outliers based on a threshold (e.g., 3 standard deviations).

Answer: D

NEW QUESTION # 163
A data scientist is working with datasets ranging from hundreds of megabytes to several terabytes and needs to select the most efficient NVIDIA-accelerated data processing library for optimal memory management and performance.
Which approach is best for selecting the appropriate library for different dataset sizes?

A. Always use Dask regardless of dataset size since Dask automatically scales from small to large datasets.
B. Use Pandas for all dataset sizes since Pandas has built-in multi-threading optimizations.
C. Use cuDF for small datasets and Dask-cuDF for larger datasets that do not fit in a single GPU's memory.
D. Use RAPIDS cuML for handling all data processing tasks, regardless of dataset size.

Answer: C

NEW QUESTION # 164
In the CRISP-DM process, which NVIDIA technology is commonly used during the "Data Preparation" phase to handle large datasets efficiently?

A. NVIDIA Deep Learning AI
B. NVIDIA CUDA Toolkit
C. NVIDIA RAPIDS
D. NVIDIA Jetson

Answer: C

NEW QUESTION # 165
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