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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Data Preparation | 17% | - Feature engineering
|
| Topic 2: GPU and Cloud Computing | 16% | - Cloud GPU environments
|
| Topic 3: MLOps | 19% | - Experiment tracking
|
| Topic 4: Data Manipulation and Software Literacy | 19% | - Software literacy and development tools
|
| Topic 5: Machine Learning | 15% | - Feature engineering and hyperparameter tuning
|
| Topic 6: Data Analysis | 14% | - Time-series analysis
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
Question 1
You are working with a large dataset containing numeric and categorical features, which will be processed using NVIDIA RAPIDS cuDF for accelerated analytics.
To optimize performance while minimizing memory usage, which data type is the most appropriate for storing a categorical variable with a small number of unique values?
A. int64 - Provides high precision and avoids potential overflow.
B. float32 - Reduces memory consumption compared to float64 while maintaining precision.
C. category - Optimizes storage and computation for categorical data in cuDF.
D. bool - Minimizes memory usage and supports efficient operations for categorical data.
Question 2
You are working with a dataset in a cloud-based GPU environment that contains a column country representing the country of origin for customers. The column contains only 10 unique country values, but the dataset has millions of rows.
Which of the following is the most memory-efficient approach to handle the country column in a cuDF DataFrame?
A. df['country'] = df['country'].astype('int32')
B. df['country'] = df['country'].astype('object')
C. df['country'] = df['country'].astype('string')
D. df['country'] = df['country'].astype('category')
Question 3
Which of the following actions can you perform using DLProf to analyze a deep learning model's performance?
A. Modify the training dataset during model execution
B. Increase batch size to improve accuracy
C. Automatically adjust the learning rate based on the model's convergence
D. Visualize GPU memory utilization over time
Question 4
A data scientist is working on a dataset where the numerical features have different ranges, and they need to ensure uniformity across features before training a machine learning model.
Which of the following approaches, utilizing NVIDIA technologies, would best achieve this goal?
A. Use cuML's StandardScaler() to transform the features to have zero mean and unit variance.
B. Apply cuDF's normalize() function to scale each feature between 0 and 1.
C. Use cuML's PCA to directly remove the need for standardization by reducing dimensionality.
D. Apply cuML's RobustScaler() to center the data using median and scale using the interquartile range.
Question 5
What is the primary advantage of using NVIDIA Triton Inference Server for deploying and monitoring machine learning models in production?
A. It is designed solely for edge devices and not for data centers.
B. It automatically tunes hyperparameters for all models.
C. It only supports TensorFlow models for inference.
D. It provides GPU optimization to handle high-throughput inference workloads.
Solutions:
| Question 1 Answer: C | Question 2 Answer: D | Question 3 Answer: D | Question 4 Answer: A | Question 5 Answer: D |
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