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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Analysis | 14% | - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA) - Data visualization and graph analytics - Distributed and parallel data processing |
| Topic 2: Data Manipulation and Software Literacy | 19% | - Data processing libraries selection and usage - Dependency management and containerization - Performance profiling and optimization tools - GPU-accelerated ETL workflows |
| Topic 3: Machine Learning | 15% | - Distributed training strategies - Model evaluation and validation - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms |
| Topic 4: Data Preparation | 17% | - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Data validation and quality assurance - Feature engineering and data type optimization |
| Topic 5: MLOps | 19% | - Monitoring, logging and maintenance - End-to-end workflow management - Pipeline automation and orchestration - Model deployment and serving |
| Topic 6: GPU and Cloud Computing | 16% | - Cloud GPU environments and deployment - GPU architecture and acceleration principles - CRISP-DM and data science methodology - Resource management and scaling strategies |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. A data scientist is working with a 50 TB dataset consisting of structured logs from IoT devices. The data needs to be cleaned, transformed, and aggregated before training a machine learning model.
Which of the following frameworks would be the most efficient choice for distributed data processing?
A) SQLite
B) Pandas
C) Apache Spark with RAPIDS Accelerator
D) Python multiprocessing module
2. Which of the following best describes the purpose of the NVIDIA TensorRT library?
A) Provides hardware abstraction for AI model development
B) Manages GPU resources for deep learning models
C) Accelerates training of neural networks
D) Optimizes and accelerates inference of trained models
3. You are working with a large dataset in a GPU-accelerated environment, and one of the columns, revenue, contains numeric values representing the annual revenue for companies. The revenue values are in the billions of dollars.
Which of the following is the most memory-efficient data type for the revenue column in a cuDF DataFrame?
A) df['revenue'] = df['revenue'].astype('uint32')
B) df['revenue'] = df['revenue'].astype('float32')
C) df['revenue'] = df['revenue'].astype('int64')
D) df['revenue'] = df['revenue'].astype('float64')
4. A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?
A) Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
B) Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
C) Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.
D) Train a generative adversarial network (GAN) using PyTorch and then use the generated samples in RAPIDS AI without any additional processing.
5. You have trained a machine learning model using cuML as part of the Modeling phase in the CRISP- DM framework. Now, you need to assess how well the model performs before moving forward with deployment.
Which of the following steps aligns best with the Evaluation phase of CRISP-DM using NVIDIA technologies?
A) Deploy the model to an edge device using TensorRT for real-time inference.
B) Define the problem statement and collect relevant datasets before training the model.
C) Optimize the data pipeline using cudf.DataFrame.merge() to improve data loading speed.
D) Compute model accuracy, precision, and recall using cuml.metrics.accuracy_score() and cuml.metrics.classification_report().
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: C | Question # 5 Answer: D |
