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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
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. Production deployment strategies 2. Model saving, loading, and prediction generation
GPU and Cloud Computing	16%	- Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration - Performance optimization 1. Memory profiling with DLProf 2. Mixed precision and bottleneck analysis 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
Data Preparation	17%	- GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines - 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 - Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues
Machine Learning	15%	- Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Feature engineering for ML models 3. Hyperparameter tuning techniques - Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - 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
Data Analysis	14%	- Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets - Visualization 1. Selecting appropriate plots for different analysis goals 2. Visualizing data using Plotly and Matplotlib
Data Manipulation and Software Literacy	19%	- 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 - Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing - Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q66-Q71):

NEW QUESTION # 66
You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

A. Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.
B. Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
C. Ensuring the ETL pipeline uses only batch processing for data ingestion.
D. Using a single storage location for both raw and transformed data to simplify the workflow.
E. Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.

Answer: A,E

NEW QUESTION # 67
A data scientist is deploying a deep learning model to production using an NVIDIA GPU-powered inference server. They want to ensure low latency and high throughput while maintaining model accuracy.
Which of the following deployment strategies would be most effective?

A. Disabling mixed-precision inference to avoid accuracy loss
B. Deploying a model without batching to ensure real-time responses
C. Running inference on a CPU instead of a GPU to reduce power consumption
D. Using NVIDIA Triton Inference Server with model ensemble optimization

Answer: D

NEW QUESTION # 68
A machine learning engineer is training a convolutional neural network (CNN) on an NVIDIA GPU and needs to maximize throughput while avoiding OOM errors.
Which of the following techniques is the most effective way to balance memory efficiency and training speed?

A. Using a batch size of 1 to minimize memory usage
B. Loading all dataset samples into GPU memory at the start of training
C. Allocating a fixed batch size without monitoring memory usage
D. Using dynamic batch sizing based on available GPU memory

Answer: D

NEW QUESTION # 69
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 a higher number of GPUs to distribute the model and memory load across the GPUs.
B. Increase the input data size to fully utilize available memory and improve training performance.
C. Use mixed-precision training to reduce memory requirements by using half-precision floating-point numbers.
D. Decrease the number of training iterations to reduce memory consumption.

Answer: C

NEW QUESTION # 70
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. Ensuring the proper configuration of GPU resources across all nodes in the distributed system.
B. Using more CPU cores to handle computation-heavy tasks.
C. Maximizing the amount of data transferred between GPUs for faster processing.
D. Limiting the number of GPU nodes used in the cluster to avoid complexity.

Answer: A

NEW QUESTION # 71
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