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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. Batching and memory-efficient training methods 2. Hyperparameter tuning techniques 3. Feature engineering for ML models - 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
MLOps	19%	- 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 - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility
Data Analysis	14%	- Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - 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. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis
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. Groupby, apply, and aggregation operations 2. cuDF vs pandas API mapping and usage 3. Data integration, joining, merging, and filtering - Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
GPU and Cloud Computing	16%	- GPU architecture and fundamentals 1. GPU architecture fundamentals for data science 2. CPU vs GPU workloads and memory transfer optimization - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud - Performance optimization 1. Mixed precision and bottleneck analysis 2. Single and multi-GPU performance optimization 3. Memory profiling with DLProf - GPU resource management 1. Efficient GPU resource allocation and scheduling
Data Preparation	17%	- Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues - Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading

>> Lab NCP-ADS Questions <<

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q268-Q273):

NEW QUESTION # 268
You are tasked with implementing data caching to reduce shuffle in an accelerated machine learning pipeline using NVIDIA technologies. You need to cache intermediate results after a shuffle operation in a distributed setting.
Which of the following is the best approach to minimize shuffle overhead and maximize performance?

A. Cache the data in GPU memory using RAPIDS cuDF for faster access, and leverage GPU-based partitioning to reduce shuffle size.
B. Use DALI to perform preprocessing and cache the output before the shuffle operation, thereby eliminating the need for shuffle.
C. Use RAPIDS cuDF to cache the shuffled data on disk and then re-load it from disk during subsequent stages.
D. Implement Spark's default disk caching to store shuffle results, allowing the GPU to access disk data directly.

Answer: C

NEW QUESTION # 269
Which of the following scenarios are most appropriate for using GPU acceleration when working with large-scale datasets in machine learning? (Select two)

A. Training a large-scale natural language processing (NLP) model on text data with billions of words.
B. Running a large ensemble of simple models (e.g., random forests) on a dataset of 10 million rows.
C. Performing exploratory data analysis (EDA) on a dataset of 100,000 rows with 10 features.
D. Using decision trees for a classification task with a dataset of 1 million rows and 20 features.
E. Running a deep learning model for image classification with millions of labeled images.

Answer: A,E

NEW QUESTION # 270
You are setting up a deep learning model for training on a multi-GPU cluster. You want to maximize training efficiency while maintaining model convergence.
Which of the following strategies is most effective in ensuring efficient multi-GPU training?

A. Use Data Parallelism, where each GPU gets a different portion of the dataset, but gradients are averaged across all GPUs.
B. Train each GPU independently on a different dataset to reduce the communication bottleneck.
C. Use Model Parallelism, where different layers of the model are placed on different GPUs to reduce communication overhead.
D. Reduce batch size to ensure each GPU receives only a small portion of the training data.

Answer: A

NEW QUESTION # 271
You are analyzing a large financial dataset containing stock market tick-by-tick data stored in a cuDF DataFrame. Since the dataset contains billions of data points, you need to aggregate it at the minute level before visualizing price trends efficiently.
Which of the following is the best approach for aggregating and visualizing this time-series data using NVIDIA technologies?

A. Load the data into a relational database (e.g., PostgreSQL), run an SQL query for aggregation, and visualize using Seaborn
B. Convert cuDF to Pandas, aggregate using .resample() in Pandas, and visualize using Matplotlib
C. Use cuML's TSNE function to reduce dimensionality before visualizing with Bokeh
D. Use cuDF's .groupby() function to aggregate at the minute level, then visualize using hvPlot

Answer: D

NEW QUESTION # 272
You are building a fraud detection system and have a dataset that includes a feature representing transaction amounts. The values range from a few cents to several thousand dollars.
What is the most appropriate data type for this feature?

A. Floating-point (with precision to store cents or fractions of a cent).
B. Integer (rounding all transaction amounts to whole dollars).
C. Boolean (True for transactions above $100, False otherwise).
D. Categorical (grouping transactions into bins such as "low", "medium", "high").

Answer: A

NEW QUESTION # 273
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