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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Machine Learning	15%	- Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Batching and memory-efficient training methods 3. Feature engineering for ML models - 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
GPU and Cloud Computing	16%	- 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 - GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science - Performance optimization 1. Single and multi-GPU performance optimization 2. Mixed precision and bottleneck analysis 3. Memory profiling with DLProf
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 - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools
Data Manipulation and Software Literacy	19%	- 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. Data integration, joining, merging, and filtering 2. Groupby, apply, and aggregation operations 3. cuDF vs pandas API mapping and usage - Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency
Data Preparation	17%	- Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - 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. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues
Data Analysis	14%	- Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q280-Q285):

NEW QUESTION # 280
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. Principal Component Analysis (PCA)
B. One-Hot Encoding
C. Moving Average Smoothing
D. Z-score (Standard Score) Analysis

Answer: D

NEW QUESTION # 281
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. Convert the cuDF DataFrame to a Pandas DataFrame and plot using Matplotlib
D. Load the data into a Spark DataFrame and visualize using Apache Zeppelin

Answer: B

NEW QUESTION # 282
You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A. Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.
B. Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
C. Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
D. Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.

Answer: B

NEW QUESTION # 283
You are training a large-scale random forest model on a dataset with millions of rows and hundreds of features. The training time is significantly high when using traditional CPU-based machine learning frameworks.
Which NVIDIA technology should you use to accelerate training while maintaining compatibility with common ML frameworks like scikit-learn?

A. NVIDIA Triton Inference Server to distribute random forest model training across multiple GPUs.
B. NVIDIA TensorRT to accelerate random forest model training by optimizing tree-based algorithms.
C. NVIDIA RAPIDS cuML to accelerate random forest training using GPU-optimized implementations.
D. NVIDIA DeepStream to preprocess tabular data and optimize random forest model execution.

Answer: C

NEW QUESTION # 284
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. 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.
B. Run dlprof --mode=default --output_path=profile_results on the training script, analyze the generated report, and optimize memory-intensive operations.
C. Use nvidia-smi to capture GPU utilization metrics, then manually correlate high utilization periods with the training script to determine bottlenecks.
D. Profile the model using torch.profiler, then compare the results against the DLProf report to analyze GPU-specific kernel optimizations.

Answer: B

NEW QUESTION # 285
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