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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation	17%	- Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - Data cleaning and quality handling 1. Handling missing values and data quality issues 2. Data governance and compliance - Data loading and preprocessing 1. NVIDIA DALI for high-performance data loading 2. Handling class imbalance and generating synthetic data - GPU-accelerated ETL workflows 1. Efficient processing and storage with Parquet 2. RAPIDS-based ETL pipelines
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. Data integration, joining, merging, and filtering 3. cuDF vs pandas API mapping and usage - Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)
MLOps	19%	- Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - Model monitoring and management 1. Managing model artifacts and configurations for reproducibility 2. Monitoring production models for drift and performance degradation - Containerization and environment management 1. Docker for reproducible GPU-accelerated workflows 2. Conda environment management - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies
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. Hyperparameter tuning techniques 3. Feature engineering for ML models - Model training with GPU acceleration 1. Selection of appropriate algorithms for GPU execution 2. Multi-GPU training strategies 3. Training models using cuML and GPU-accelerated XGBoost
Data Analysis	14%	- Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting - 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 - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets
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 - 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 - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q114-Q119):

NEW QUESTION # 114
A data science team wants to leverage GPU acceleration for detecting anomalies in a massive IoT sensor dataset that is continuously streaming.
Which of the following NVIDIA-supported methods would be the best for handling real-time anomaly detection in a high-throughput environment?

A. Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.
B. Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.
C. Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.
D. Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.

Answer: C

NEW QUESTION # 115
A machine learning engineer is working on a multi-GPU workload using Dask and RAPIDS to process a massive dataset efficiently. However, they notice that GPU utilization is not optimal, and data transfer between GPUs is slowing down computation.
What is the best approach to minimize data transfer overhead and maximize parallel efficiency?

A. Use NVLink to enable high-bandwidth inter-GPU communication and dask_cuda.LocalCUDACluster
B. Use Dask's single-threaded scheduler to avoid multi-threading overhead
C. Transfer data between GPUs using NumPy's .copy() function
D. Load the dataset into a Pandas DataFrame and distribute computations across GPUs manually

Answer: A

NEW QUESTION # 116
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. NumPy, because it is optimized for numerical computing and offers better performance for handling tabular data.
B. cuPy, because it provides GPU-accelerated array operations, making it the best option for processing tabular data.
C. Pandas, as it is widely used and supports all common DataFrame operations, even for very large datasets.
D. cuDF, as it provides GPU-accelerated DataFrame operations similar to Pandas, allowing for efficient processing of large datasets.

Answer: D

NEW QUESTION # 117
You are tasked with designing a benchmark to compare the performance of different GPU- accelerated machine learning frameworks, such as TensorFlow, PyTorch, and RAPIDS.
Which of the following factors is the most critical to ensure a fair and meaningful comparison?

A. Using different model architectures per framework to test a variety of scenarios and ensure a broad evaluation.
B. Allowing each framework to run with its default settings, as tuning hyperparameters would bias the results.
C. Using the latest software versions of each framework, regardless of compatibility with the GPU hardware.
D. Ensuring all frameworks use the same dataset, batch size, and GPU hardware during benchmarking.

Answer: D

NEW QUESTION # 118
A financial institution is training a fraud detection model on a GPU-powered NVIDIA RAPIDS cuML pipeline. Their dataset includes customer ages, transaction amounts, merchant names, and transaction timestamps.
To optimize GPU memory usage while preserving accuracy, how should they store these features?

A. Convert all numeric data to float64 for maximum precision, as rounding errors in lower precision could impact the model's accuracy.
B. Convert timestamps into UNIX epoch integers instead of using datetime64 for more efficient GPU computations.
C. Store transaction amounts as float32, customer ages as int8, merchant names as categorical, and timestamps as datetime64.
D. Keep customer ages in float16 format to reduce memory consumption, as integer types are less efficient in GPU operations.

Answer: C

NEW QUESTION # 119
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