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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 - 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 2: Machine Learning	15%	- Model training with GPU acceleration 1. Training models using cuML and GPU-accelerated XGBoost 2. Multi-GPU training strategies 3. Selection of appropriate algorithms for GPU execution - Deep learning frameworks integration 1. Using RAPIDS with TensorFlow and PyTorch 2. Overfitting vs underfitting concepts - Feature engineering and hyperparameter tuning 1. Hyperparameter tuning techniques 2. Feature engineering for ML models 3. Batching and memory-efficient training methods
Topic 3: Data Preparation	17%	- Feature engineering 1. Feature engineering for numerical and categorical variables 2. Dimensionality reduction and data sampling - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - 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
Topic 4: 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 - 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 - Performance optimization 1. Mixed precision and bottleneck analysis 2. Single and multi-GPU performance optimization 3. Memory profiling with DLProf
Topic 5: Data Analysis	14%	- Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting - 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
Topic 6: Data Manipulation and Software Literacy	19%	- 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 - Distributed computing with Dask 1. Scaling data operations across multiple GPUs 2. Dask-cuDF for parallel data processing - Software literacy and development tools 1. Python, NumPy, pandas, Jupyter proficiency 2. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy)

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q16-Q21):

NEW QUESTION # 16
You are training a machine learning model using scikit-learn-like API on a dataset with millions of samples and thousands of features. You need to optimize both training time and inference speed using NVIDIA technologies.
Which solution is the most appropriate?

A. Use NVIDIA Magnum IO to optimize machine learning model parameters on the GPU.
B. Use NVIDIA Triton Inference Server to train the model efficiently on a single GPU.
C. Use NVIDIA RAPIDS cuML for GPU-accelerated machine learning model training.
D. Use NVIDIA Modulus to accelerate machine learning training and feature selection.

Answer: C

NEW QUESTION # 17
You are processing a large dataset in a distributed computing environment using RAPIDS and Dask.
Your workflow involves frequent shuffling of data between partitions, leading to significant slowdowns.
Which of the following strategies is the best way to implement data caching to reduce shuffle overhead using NVIDIA technologies?

A. Use traditional disk-based caching by writing intermediate results to CSV files and reloading when needed.
B. Enable GPU-accelerated caching with RAPIDS cuDF and persist intermediate results in GPU memory.
C. Use a CPU-based caching solution like Memcached to store intermediate data before reloading into cuDF.
D. Disable caching altogether to force a recomputation of results, ensuring up-to-date data processing.

Answer: B

NEW QUESTION # 18
You are working on a large-scale machine learning project that requires preprocessing terabytes of structured and semi-structured data. You need a distributed data processing framework that can leverage NVIDIA GPUs efficiently to accelerate computations.
Which of the following approaches would best achieve this goal?

A. Using Dask with RAPIDS cuDF and cuML for distributed GPU-accelerated processing
B. Using Apache Spark with PySpark for CPU-based distributed data processing
C. Using plain NumPy with CUDA extensions to manually parallelize computations across multiple GPUs
D. Using TensorFlow's Dataset API to load and preprocess the data on GPUs

Answer: A

NEW QUESTION # 19
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. Apply cuDF's normalize() function to scale each feature between 0 and 1.
B. Use cuML's PCA to directly remove the need for standardization by reducing dimensionality.
C. Apply cuML's RobustScaler() to center the data using median and scale using the interquartile range.
D. Use cuML's StandardScaler() to transform the features to have zero mean and unit variance.

Answer: D

NEW QUESTION # 20
You are working on a machine learning problem that involves training a deep learning model on a dataset with billions of records. The dataset is stored in a distributed cloud storage system.
Given the need for acceleration, which is the most effective approach?

A. Load the entire dataset into RAM on a single powerful CPU-based machine before starting model training.
B. Reduce the dataset to a small representative sample to avoid the need for specialized acceleration.
C. Use GPU acceleration with libraries like RAPIDS AI or TensorFlow to leverage parallel processing.
D. Store the dataset in a relational database and query it sequentially using SQL before training the model.

Answer: C

NEW QUESTION # 21
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