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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
GPU and Cloud Computing	16%	- 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 - Cloud GPU environments 1. Cloud-based GPU instance configuration 2. Containerized workflow deployment on cloud
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. Overfitting vs underfitting concepts 2. Using RAPIDS with TensorFlow and PyTorch - 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
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. Production deployment strategies 2. Model saving, loading, and prediction generation - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - Experiment tracking 1. MLflow, Weights & Biases, and custom tracking tools 2. Benchmarking workflows and selecting optimal hardware
Data Preparation	17%	- Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - 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. 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
Data Analysis	14%	- Exploratory data analysis 1. Performing EDA on GPU-accelerated datasets 2. Descriptive statistics and summary analysis - Graph analytics 1. Node importance evaluation and network relationship visualization 2. Creating and analyzing graph data using cuGraph - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting
Data Manipulation and Software Literacy	19%	- Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency - 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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q288-Q293):

NEW QUESTION # 288
You are working with a dataset containing hundreds of millions of records, and you need to perform ETL operations such as filtering, joins, and aggregations. Given the dataset size, which NVIDIA- accelerated library should you use to achieve optimal performance?

A. cuDF, as it provides GPU-accelerated DataFrame operations similar to Pandas, allowing for efficient processing of large datasets.
B. NumPy, because it is optimized for numerical computing and offers better performance for handling tabular data.
C. Pandas, as it is widely used and supports all common DataFrame operations, even for very large datasets.
D. cuPy, because it provides GPU-accelerated array operations, making it the best option for processing tabular data.

Answer: A

NEW QUESTION # 289
You are processing a large dataset using NVIDIA Dask-cuDF to distribute GPU-accelerated computation across multiple nodes. Users report inconsistent execution times, with some jobs taking significantly longer than expected.
Which of the following actions would best help diagnose the performance bottleneck?

A. Limit GPU memory usage to force more frequent spilling to disk and observe performance differences.
B. Switch to using Pandas with Dask to compare execution speed differences.
C. Reduce the number of Dask workers to minimize parallel execution overhead.
D. Use Dask's dashboard and NVTX markers to analyze task execution times and GPU utilization.

Answer: D

NEW QUESTION # 290
A machine learning engineer is working with a 1 TB dataset stored in Apache Parquet format and wants to analyze the data for patterns before building a model. The engineer is considering various acceleration methods.
Which of the following approaches would be the best choice for efficient analysis?

A. Use a GPU-accelerated library such as RAPIDS cuDF to load and process the Parquet file efficiently.
B. Read the Parquet file line by line using Python's built-in file handling functions to save memory.
C. Load the dataset into a relational database and query it using simple SQL statements.
D. Convert the Parquet file to a Pandas DataFrame and perform analysis using Pandas functions.

Answer: A

NEW QUESTION # 291
You are working with a large dataset in a cloud environment for a deep learning model. The dataset consists of several features including numerical values, categorical data, and timestamps.
Which of the following choices would result in the most efficient use of GPU and cloud resources when determining the optimal data type for each feature? (Select three)

A. Use int8 for categorical features where there are fewer than 256 categories.
B. Use datetime64[ns] for timestamp features to ensure high precision.
C. Use int32 for all numerical features to save memory.
D. Use float64 for all numerical features to ensure maximum precision.
E. Use object data types for categorical features to avoid type conversion overhead.

Answer: A,B,C

NEW QUESTION # 292
A manufacturing company wants to detect anomalies in a real-time IoT sensor dataset using NVIDIA technologies. The data arrives as a continuous stream, and the company needs to process it efficiently for immediate anomaly detection.
Which NVIDIA-powered approach is the most suitable for this use case?

A. Deploy an Autoencoder-based deep learning model using NVIDIA TensorRT for real-time inference
B. Use RAPIDS cuDF with batch processing to analyze historical anomalies every few hours
C. Apply cuML's Isolation Forest on incoming streaming data for real-time anomaly detection
D. Leverage cuGraph to model the sensor readings as a graph and detect anomalies using centrality measures

Answer: C

NEW QUESTION # 293
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