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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Preparation	17%	- Data loading and preprocessing 1. Handling class imbalance and generating synthetic data 2. NVIDIA DALI for high-performance data loading - Feature engineering 1. Dimensionality reduction and data sampling 2. Feature engineering for numerical and categorical variables - GPU-accelerated ETL workflows 1. RAPIDS-based ETL pipelines 2. Efficient processing and storage with Parquet - Data cleaning and quality handling 1. Data governance and compliance 2. Handling missing values and data quality issues
Topic 2: Data Analysis	14%	- Time-series analysis 1. Anomaly detection in time-series datasets 2. Time-series data handling and forecasting - Graph analytics 1. Creating and analyzing graph data using cuGraph 2. Node importance evaluation and network relationship visualization - Exploratory data analysis 1. Descriptive statistics and summary analysis 2. Performing EDA on GPU-accelerated datasets - Visualization 1. Visualizing data using Plotly and Matplotlib 2. Selecting appropriate plots for different analysis goals
Topic 3: 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. Benchmarking workflows and selecting optimal hardware 2. MLflow, Weights & Biases, and custom tracking tools
Topic 4: GPU and Cloud Computing	16%	- 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 - Performance optimization 1. Memory profiling with DLProf 2. Mixed precision and bottleneck analysis 3. Single and multi-GPU performance optimization - Cloud GPU environments 1. Containerized workflow deployment on cloud 2. Cloud-based GPU instance configuration
Topic 5: Data Manipulation and Software Literacy	19%	- Distributed computing with Dask 1. Dask-cuDF for parallel data processing 2. Scaling data operations across multiple GPUs - GPU-accelerated data manipulation using cuDF 1. Data integration, joining, merging, and filtering 2. cuDF vs pandas API mapping and usage 3. Groupby, apply, and aggregation operations - Software literacy and development tools 1. RAPIDS ecosystem (cuDF, cuML, cuGraph, cuPy) 2. Python, NumPy, pandas, Jupyter proficiency
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. Training models using cuML and GPU-accelerated XGBoost 2. Selection of appropriate algorithms for GPU execution 3. Multi-GPU training strategies - Feature engineering and hyperparameter tuning 1. Batching and memory-efficient training methods 2. Feature engineering for ML models 3. Hyperparameter tuning techniques

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q226-Q231):

NEW QUESTION # 226
You are tasked with optimizing an ETL pipeline that processes petabytes of data daily. Your organization is already using Apache Spark for distributed data processing but is experiencing performance bottlenecks. You need a solution that improves execution speed without requiring extensive code modifications.
Which of the following solutions best meets your needs?

A. Switching to Dask and RAPIDS to fully utilize GPU acceleration
B. Using the NVIDIA Spark RAPIDS Accelerator to run Spark workloads on GPUs
C. Rewriting the entire ETL pipeline in CUDA to maximize GPU efficiency
D. Using Apache Arrow to optimize in-memory columnar processing within Spark

Answer: B

NEW QUESTION # 227
You are working on a data science project that involves processing large-scale financial transaction data. You want to optimize data manipulation operations using NVIDIA's RAPIDS cuDF.
Which of the following approaches best leverages NVIDIA technologies for efficient data manipulation?

A. Convert the dataset into a SQLite database and execute SQL queries to perform data transformations.
B. Use cuDF DataFrames for data manipulation and rely on GPU-accelerated functions like .groupby(),
.merge(), and .applymap().
C. Load the dataset into a Pandas DataFrame and use multi-threading to speed up operations.
D. Preprocess the data using Apache Spark's CPU-based DataFrame API before transferring it to a GPU for machine learning.

Answer: B

NEW QUESTION # 228
You are building a large-scale AI training pipeline that requires efficient storage and retrieval of structured and unstructured datasets across multiple GPUs.
Which of the following is the best NVIDIA technology to organize and manage datasets at scale?

A. NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.
B. NVIDIA Nsight Systems for managing dataset storage and retrieval performance.
C. NVIDIA Clara Imaging for storing structured and unstructured datasets efficiently.
D. NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.

Answer: A

NEW QUESTION # 229
A data scientist is preprocessing a dataset containing multiple categorical features using NVIDIA RAPIDS to accelerate feature engineering.
The dataset contains:
A low-cardinality categorical feature (Product Type) with 10 unique values.
A high-cardinality categorical feature (User ID) with 100,000 unique values.
A numerical feature (Price) that requires transformation.
Which of the following feature engineering approaches will be the most efficient for GPU acceleration?

A. Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.
B. Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.
C. Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision.
D. Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.
E. Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.
F. Frequency encoding for User ID is an efficient alternative to one-hot encoding, as it replaces each category with its frequency in the dataset, reducing dimensionality while preserving useful information.

Answer: E

NEW QUESTION # 230
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. Ensuring the ETL pipeline uses only batch processing for data ingestion.
C. Using a single storage location for both raw and transformed data to simplify the workflow.
D. Relying on traditional single-threaded processing for the extraction phase to reduce complexity.
E. Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.

Answer: A,E

NEW QUESTION # 231
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