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% | - GPU Optimization and Infrastructure
|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| MLOps | 19% | - Deployment and Monitoring
|
| Machine Learning | 15% | - Model Development and Optimization
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
Which of the following can DLProf specifically help identify when profiling a deep learning model on Nvidia GPUs?
- A. Training dataset bias.
- B. GPU utilization and memory usage.
- C. Hyperparameter tuning results.
- D. Number of model parameters.
Correct Answer: B 🗳️
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. Apply one-hot encoding to both Product Type and User ID, and scale Price using float64 precision.
- B. Using float32 for Price is optimal for GPU-based ML models, balancing precision and computational efficiency.
- C. Convert Product Type to integers using label encoding, use frequency encoding for User ID, and normalize Price using float32.
- D. Store both Product Type and User ID as string data types in cuDF to maintain raw categorical information.
- E. Convert both Product Type and User ID to int64 and use standardization (mean normalization) on Price.
- 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.
Correct Answer: C 🗳️
You are selecting a dataset for GPU-accelerated data science using NVIDIA RAPIDS.
Which dataset characteristic is most suitable for taking advantage of GPU acceleration?
- A. A dataset stored as an SQLite database file
- B. A dataset consisting of unstructured text stored as plain .txt files
- C. A small dataset with fewer than 1,000 rows stored in an Excel file
- D. A large, structured dataset stored in a Parquet format
Correct Answer: D 🗳️
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 RAPIDS cuML for GPU-accelerated machine learning model training.
- B. Use NVIDIA Modulus to accelerate machine learning training and feature selection.
- C. Use NVIDIA Triton Inference Server to train the model efficiently on a single GPU.
- D. Use NVIDIA Magnum IO to optimize machine learning model parameters on the GPU.
Correct Answer: A 🗳️
Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?
- A. Model Building
- B. Data Preparation
- C. Data Understanding
- D. Business Understanding
Correct Answer: D 🗳️
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