Exam Name: NVIDIA-Certified-Professional Accelerated Data Science
Certification Provider: NVIDIA
Corresponding Certification: NVIDIA-Certified Professional
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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| GPU and Cloud Computing | 16% | - Resource management and scaling strategies - GPU architecture and acceleration principles - Cloud GPU environments and deployment - CRISP-DM and data science methodology |
| Data Preparation | 17% | - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization - Data validation and quality assurance - Workflow monitoring and bottleneck identification |
| Data Manipulation and Software Literacy | 19% | - Data processing libraries selection and usage - Dependency management and containerization - GPU-accelerated ETL workflows - Performance profiling and optimization tools |
| Machine Learning | 15% | - Model evaluation and validation - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms - Distributed training strategies |
| Data Analysis | 14% | - Time-series analysis and anomaly detection - Data visualization and graph analytics - Distributed and parallel data processing - Exploratory Data Analysis (EDA) |
| MLOps | 19% | - Pipeline automation and orchestration - End-to-end workflow management - Monitoring, logging and maintenance - Model deployment and serving |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are tasked with acquiring a dataset for training a machine learning model in healthcare, predicting patient readmission rates. Before using the dataset, you must assess its quality.
Which of the following is the most important factor to evaluate before acquisition?
- A. The number of missing values and inconsistencies in key columns
- B. The file format (CSV, JSON, or XML) of the dataset
- C. Whether the dataset is stored on a cloud server or a local machine
- D. The programming language used to preprocess the dataset
Correct Answer: A 🗳️
You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?
- A. Convert the dataset to a NumPy array and manually replace missing values with the mean
- B. Drop all rows containing missing values using Pandas before transferring data to the GPU
- C. Use fillna() with a fixed value on the GPU using cuDF
- D. Apply a deep learning-based imputation model before moving data to the GPU
Correct Answer: C 🗳️
You are building a real-time recommendation system that processes high-frequency transactional data from millions of users.
The system must:
- Ingest and preprocess data efficiently
- Perform similarity computations for user-item recommendations
- Scale to handle rapid incoming transactions
Which of the following NVIDIA technologies is the best choice for this use case?
- A. NVIDIA Triton Inference Server
- B. CUDA Kernels with Custom C++ Code
- C. NVIDIA NVTabular
- D. RAPIDS cuGraph
Correct Answer: C 🗳️
A data scientist is training a deep learning model on an NVIDIA GPU but is encountering out-of- memory (OOM) errors.
To optimize GPU memory usage while maintaining efficient training performance, which of the following strategies should they prioritize?
- A. Increasing batch size without adjusting the optimizer settings
- B. Storing all training data in GPU memory at once
- C. Using mixed precision training with automatic loss scaling
- D. Using single-precision (FP32) calculations for better accuracy
Correct Answer: C 🗳️
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 Clara Imaging for storing structured and unstructured datasets efficiently.
- B. NVIDIA Magnum IO for high-performance I/O and dataset storage optimization.
- C. NVIDIA Nsight Systems for managing dataset storage and retrieval performance.
- D. NVIDIA Morpheus for accelerating dataset indexing and retrieval in AI pipelines.
Correct Answer: B 🗳️
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