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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| MLOps | 19% | - Pipeline automation and orchestration - End-to-end workflow management - Monitoring, logging and maintenance - Model deployment and serving |
| Data Manipulation and Software Literacy | 19% | - Data processing libraries selection and usage - GPU-accelerated ETL workflows - Performance profiling and optimization tools - Dependency management and containerization |
| Data Preparation | 17% | - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization - Data validation and quality assurance |
| Machine Learning | 15% | - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms - Model evaluation and validation - Distributed training strategies |
| GPU and Cloud Computing | 16% | - CRISP-DM and data science methodology - GPU architecture and acceleration principles - Resource management and scaling strategies - Cloud GPU environments and deployment |
| Data Analysis | 14% | - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA) - Data visualization and graph analytics - Distributed and parallel data processing |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. 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) Load the dataset into a Pandas DataFrame and use multi-threading to speed up operations.
B) Preprocess the data using Apache Spark's CPU-based DataFrame API before transferring it to a GPU for machine learning.
C) Use cuDF DataFrames for data manipulation and rely on GPU-accelerated functions like .groupby(),
.merge(), and .applymap().
D) Convert the dataset into a SQLite database and execute SQL queries to perform data transformations.
2. You are a data scientist working on a large-scale deep learning project. Your team needs to train a deep neural network (DNN) on terabytes of image data using NVIDIA GPUs in the cloud.
Which of the following approaches will maximize GPU utilization and optimize training time in an NVIDIA- accelerated cloud environment?
A) Use Jupyter Notebooks on a local machine with minimal GPU resources and transfer model checkpoints manually to the cloud for training.
B) Select low-cost, consumer-grade GPUs available in the cloud marketplace to reduce costs.
C) Use NVIDIA NGC containers with TensorFlow or PyTorch and enable NCCL for multi-GPU communication.
D) Avoid using mixed precision training and rely solely on full-precision (FP32) computations for numerical stability.
3. Which of the following data normalization techniques is most appropriate when the dataset contains outliers, and you want to minimize the influence of those outliers on the model performance?
A) Robust Scaling
B) Z-score Standardization
C) Log Transformation
D) Min-Max Scaling
4. You are working on a data science project where you need to process a large dataset containing
500 million records. You want to determine whether GPU acceleration would significantly improve performance.
Which of the following factors best indicates that you should use an accelerated computing solution like RAPIDS?
A) The dataset is heavily structured but mainly requires text-based analysis using regex-based search and manipulation.
B) The dataset has high-dimensional sparse features and requires complex operations such as nearest neighbor search and clustering.
C) The dataset consists of simple arithmetic operations on a few columns and can be processed using vectorized NumPy operations.
D) The dataset is a structured table with less than 100,000 records and can be handled efficiently with a Pandas DataFrame.
5. Which of the following best describes the purpose of the NVIDIA TensorRT library?
A) Optimizes and accelerates inference of trained models
B) Manages GPU resources for deep learning models
C) Accelerates training of neural networks
D) Provides hardware abstraction for AI model development
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: C | Question # 3 Answer: A | Question # 4 Answer: B | Question # 5 Answer: A |
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