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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: MLOps	19%	- Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware - Model deployment and serving 1. Production deployment strategies 2. Model saving, loading, and prediction generation
Topic 2: Data Manipulation and Software Literacy	19%	- Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency - 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. cuDF vs pandas API mapping and usage 2. Data integration, joining, merging, and filtering 3. Groupby, apply, and aggregation operations
Topic 3: GPU and Cloud Computing	16%	- Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - Performance optimization 1. Mixed precision and bottleneck analysis 2. Memory profiling with DLProf 3. Single and multi-GPU performance optimization - 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
Topic 4: Data Preparation	17%	- 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 - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling
Topic 5: Data Analysis	14%	- Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets
Topic 6: Machine Learning	15%	- Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Feature engineering for ML models 3. Hyperparameter tuning techniques - Model training with GPU acceleration 1. Selection of appropriate algorithms for GPU execution 2. Training models using cuML and GPU-accelerated XGBoost 3. Multi-GPU training strategies

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q263-Q268):

NEW QUESTION # 263
When comparing the required memory with the available memory on a GPU for an MLOps deployment using NVIDIA technologies, which of the following is the best method to optimize memory usage while training large models?

A. Use mixed-precision training to reduce memory requirements by using half-precision floating-point numbers.
B. Use a higher number of GPUs to distribute the model and memory load across the GPUs.
C. Increase the input data size to fully utilize available memory and improve training performance.
D. Decrease the number of training iterations to reduce memory consumption.

Answer: A

NEW QUESTION # 264
You are working with a GPU-based cloud environment and need to optimize the memory usage for a dataset that contains a column item_id representing unique product IDs. The item_id values are large integers, and there are over 10 million distinct product IDs.
Which of the following is the most memory-efficient data type choice for this column?

A. df['item_id'] = df['item_id'].astype('int32')
B. df['item_id'] = df['item_id'].astype('float64')
C. df['item_id'] = df['item_id'].astype('int64')
D. df['item_id'] = df['item_id'].astype('string')

Answer: C

NEW QUESTION # 265
You are preparing a dataset for training a machine learning model using NVIDIA RAPIDS cuML. The dataset contains a feature representing timestamps in nanoseconds.
To optimize GPU performance while ensuring precision, which data type should you choose?

A. int32 - Uses less memory and can store high-precision timestamps efficiently.
B. datetime64[ns] - Optimizes storage and computation for timestamp data in RAPIDS.
C. bool - Provides a highly efficient way to store timestamps as binary values.
D. object - Allows flexibility in storing timestamps as strings for easier parsing.

Answer: B

NEW QUESTION # 266
When scaling a distributed data processing framework using NVIDIA GPU technology for big data processing, which of the following factors is most critical to optimize performance?

A. Using more CPU cores to handle computation-heavy tasks.
B. Limiting the number of GPU nodes used in the cluster to avoid complexity.
C. Ensuring the proper configuration of GPU resources across all nodes in the distributed system.
D. Maximizing the amount of data transferred between GPUs for faster processing.

Answer: C

NEW QUESTION # 267
You are using NVIDIA DLProf to analyze the performance of a deep learning model deployed on an A100 GPU. The report indicates that compute-bound operations are dominating execution time, and kernel execution efficiency is below 50%.
What is the best action to take based on this insight?

A. Enable mixed precision training to improve computational efficiency.
B. Use DLProf's Tensor Core Analysis to check if the model is leveraging Tensor Cores effectively.
C. Reduce the number of layers in the model to decrease computation time.
D. Increase the batch size to fully utilize available GPU memory and reduce per-sample processing overhead.

Answer: B

NEW QUESTION # 268
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