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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| Topic 2: Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| Topic 3: Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| Topic 4: Data Preparation | 17% | - Data Cleaning and Transformation
|
| Topic 5: MLOps | 19% | - Deployment and Monitoring
|
| Topic 6: Machine Learning | 15% | - Model Development and Optimization
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
Question 1
In the context of cloud computing, what are the key benefits of using GPUs for data science tasks?
(Select two)
A. Faster parallel processing for large datasets
B. Lower cost of cloud infrastructure
C. Efficient handling of matrix operations in machine learning models
D. Better for memory-intensive workloads
E. Lower energy consumption compared to CPUs
Question 2
Which tools or technologies from NVIDIA are essential for implementing an efficient MLOps pipeline in production environments? (Select two)
A. NVIDIA TensorRT for efficient model inference
B. NVIDIA NGC for storing and sharing machine learning datasets
C. NVIDIA DLA (Deep Learning Accelerator) for model deployment
D. NVIDIA Triton Inference Server for managing deployment and serving models
E. NVIDIA CUDA for model training in cloud environments
Question 3
A data scientist is analyzing sales data for an e-commerce company that experiences strong seasonal trends (e.g., increased sales during holiday seasons). The goal is to accurately forecast future sales using GPU-accelerated data science techniques.
Which approach would be the most effective?
A. Apply k-Means clustering to identify seasonal patterns and extrapolate future values.
B. Use a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and optimize it using NVIDIA RAPIDS.
C. Train a Decision Tree model using cuML to classify future sales trends.
D. Compute a rolling average and manually adjust for seasonality based on previous peak sales months.
Question 4
In Python, when working with large datasets using pandas, which of the following methods are best for improving performance and efficiency when applying operations on DataFrames? (Select two)
A. Using vectorized operations (e.g., element-wise arithmetic)
B. Using apply() function over DataFrame rows
C. Using map() function to apply a function element-wise
D. Using for loops to apply operations row by row
E. Using iterrows() for iterating through DataFrame rows
Question 5
You are working on a large-scale machine learning project that requires preprocessing terabytes of structured and semi-structured data. You need a distributed data processing framework that can leverage NVIDIA GPUs efficiently to accelerate computations.
Which of the following approaches would best achieve this goal?
A. Using TensorFlow's Dataset API to load and preprocess the data on GPUs
B. Using Dask with RAPIDS cuDF and cuML for distributed GPU-accelerated processing
C. Using plain NumPy with CUDA extensions to manually parallelize computations across multiple GPUs
D. Using Apache Spark with PySpark for CPU-based distributed data processing
Solutions:
| Question 1 Answer: A,C | Question 2 Answer: A,D | Question 3 Answer: B | Question 4 Answer: A,C | Question 5 Answer: B |
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