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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: GPU and Cloud Computing	16%	- 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. 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
Topic 2: Data Preparation	17%	- Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - 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
Topic 3: MLOps	19%	- Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies
Topic 4: 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. cuDF vs pandas API mapping and usage 3. Groupby, apply, and aggregation operations - Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
Topic 5: Machine Learning	15%	- Feature engineering and hyperparameter tuning 1. Feature engineering for ML models 2. Hyperparameter tuning techniques 3. Batching and memory-efficient training methods - 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 - Deep learning frameworks integration 1. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch
Topic 6: Data Analysis	14%	- Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - 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 - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting

NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:

Question 1

Which of the following techniques are commonly used to identify and acquire datasets for machine learning and data science projects? (Select three)

A. Data augmentation
B. Web scraping
C. Manual data entry
D. Crowdsourcing
E. Public APIs

Question 2

You are implementing a Dask-based solution for distributed data parallelism across a multi-GPU system.
Which configuration steps would ensure effective use of GPUs for parallel computation? (Select two)

A. Use dask_cuda's LocalCUDACluster with proper GPU memory management to handle multiple GPUs
B. Use Dask's Cluster class with the distributed scheduler and specify CPU cores only for GPU workloads
C. Use dask_cuda's LocalCUDACluster and let Dask automatically allocate GPUs without any configuration
D. Create a LocalCUDACluster and manually specify the GPUs you want to use for each Dask worker
E. Use dask_cudf to convert DataFrame computations into GPU-accelerated operations using cuDF

Question 3

You are working on a financial dataset that tracks stock prices over time, and you need to detect anomalies such as sudden spikes or drops using NVIDIA technologies.
Which of the following approaches would be the most effective for anomaly detection in a time-series dataset using NVIDIA's RAPIDS AI and TensorRT?

A. Use RAPIDS cuML's Isolation Forest for anomaly detection and deploy it with NVIDIA Triton Inference Server.
B. Use traditional ARIMA modeling with RAPIDS cuML to classify anomalies based on residual analysis.
C. Apply a traditional rule-based thresholding method using pandas and NumPy for detecting sudden spikes in stock prices.
D. Perform anomaly detection by applying DBSCAN clustering with RAPIDS cuML without any feature engineering.

Question 4

You are developing an AI model for medical imaging that requires acquiring a large dataset of MRI scans from multiple sources.
Which NVIDIA technology would best assist in acquiring, standardizing, and efficiently handling the dataset?

A. Use NVIDIA Nsight Systems to collect MRI data and visualize potential performance bottlenecks.
B. Use NVIDIA Clara Imaging to standardize, curate, and preprocess MRI datasets for AI model training.
C. Use NVIDIA AI Enterprise to directly acquire and store MRI images from multiple hospitals.
D. Use NVIDIA Morpheus to analyze MRI data for cybersecurity threats before storing it.

Question 5

A data scientist is analyzing a large dataset of financial transactions containing millions of records.
To efficiently perform exploratory data analysis (EDA) using RAPIDS cuDF, which approach provides the most optimized performance while ensuring comprehensive insights?

A. Downsample the dataset and analyze a subset using Pandas for efficiency.
B. Use RAPIDS cuDF functions like .describe() and .value_counts() to perform statistical summaries directly on the GPU.
C. Convert the dataset to a Pandas DataFrame for easier visualization and use .describe() to summarize statistics.
D. Perform all analysis on the CPU to avoid potential GPU memory limitations.

Solutions:

Question 1
Answer: B,D,E

Question 2
Answer: A,E

Question 3
Answer: A

Question 4
Answer: B

Question 5
Answer: B