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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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. Groupby, apply, and aggregation operations 2. Data integration, joining, merging, and filtering 3. cuDF vs pandas API mapping and usage
Topic 2: 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 3: MLOps	19%	- Experiment tracking 1. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools - Model monitoring and management 1. Monitoring production models for drift and performance degradation 2. Managing model artifacts and configurations for reproducibility - Model deployment and serving 1. Model saving, loading, and prediction generation 2. Production deployment strategies - Containerization and environment management 1. Conda environment management 2. Docker for reproducible GPU-accelerated workflows
Topic 4: Data Analysis	14%	- 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 - Time-series analysis 1. Time-series data handling and forecasting 2. Anomaly detection in time-series datasets
Topic 5: GPU and Cloud Computing	16%	- GPU architecture and fundamentals 1. CPU vs GPU workloads and memory transfer optimization 2. GPU architecture fundamentals for data science - GPU resource management 1. Efficient GPU resource allocation and scheduling - Performance optimization 1. Mixed precision and bottleneck analysis 2. Memory profiling with DLProf 3. Single and multi-GPU performance optimization - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration
Topic 6: Machine Learning	15%	- Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - 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 - Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Hyperparameter tuning techniques 3. Feature engineering for ML models

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q160-Q165):

NEW QUESTION # 160
Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)

A. Using Apache Spark for distributed data processing
B. Using SQLAlchemy to interact with databases for large data processing
C. Using Excel to manipulate large datasets
D. Using Dask for parallel processing of large datasets
E. Using SQLite as a local database for large-scale data analysis

Answer: A,D

NEW QUESTION # 161
A data scientist wants to compare the performance of two different GPU-accelerated data science frameworks, NVIDIA RAPIDS (cuDF, cuML) and TensorFlow, for a tabular data classification task.
Which of the following approaches would be the best practice for designing an unbiased and effective benchmark?

A. Ignore preprocessing and focus only on model training speed when comparing performance.
B. Use TensorFlow's built-in training time metrics without comparing equivalent RAPIDS-based operations.
C. Measure execution time and memory usage for each framework using NVIDIA Nsight Systems (nsys).
D. Run all benchmarks on a CPU to ensure fairness across frameworks.

Answer: C

NEW QUESTION # 162
You are working on a deep learning project that requires a large dataset of high-resolution satellite images for training a convolutional neural network (CNN). You want to leverage NVIDIA technologies to efficiently acquire and manage the dataset.
Which of the following approaches is the most suitable?

A. Use NVIDIA DALI (Data Loading Library) to stream and preprocess satellite image data efficiently for deep learning training.
B. Use NVIDIA Modulus to generate synthetic satellite images instead of acquiring real-world data.
C. Use NVIDIA DeepStream to acquire satellite images and store them in a structured dataset for machine learning.
D. Use NVIDIA RAPIDS cuDF to directly download and preprocess satellite images from an API in real time.

Answer: A

NEW QUESTION # 163
A data scientist is working on a dataset where the numerical features have different ranges, and they need to ensure uniformity across features before training a machine learning model.
Which of the following approaches, utilizing NVIDIA technologies, would best achieve this goal?

A. Use cuML's PCA to directly remove the need for standardization by reducing dimensionality.
B. Apply cuML's RobustScaler() to center the data using median and scale using the interquartile range.
C. Apply cuDF's normalize() function to scale each feature between 0 and 1.
D. Use cuML's StandardScaler() to transform the features to have zero mean and unit variance.

Answer: D

NEW QUESTION # 164
A company is processing large log files from a cloud application, accumulating over 5TB of data daily. The data processing pipeline must be GPU-accelerated to extract insights quickly.
Which of the following is the most effective approach to handle high-volume log processing using NVIDIA technologies?

A. Use cuDF with explicit memory management to load and process the entire dataset into a single GPU.
B. Store logs as Pandas DataFrames and use multiprocessing to parallelize operations across CPU cores.
C. Use RAPIDS cuML for performing log file processing, taking advantage of its optimized ML algorithms.
D. Leverage Dask-cuDF to distribute the dataset across multiple GPUs, ensuring efficient parallel processing.

Answer: D

NEW QUESTION # 165
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