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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Manipulation and Software Literacy | 19% | - ETL and Data Processing Workflows
|
| GPU and Cloud Computing | 16% | - GPU Optimization and Infrastructure
|
| MLOps | 19% | - Deployment and Monitoring
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| Machine Learning | 15% | - Model Development and Optimization
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
Question 1
A data engineer is designing an Extract, Transform, Load (ETL) pipeline for a retail analytics platform that processes millions of customer transactions per day. The primary objective is to accelerate data ingestion, transformation, and storage while ensuring efficient scalability.
Which of the following approaches would be the most effective for optimizing this ETL workflow using NVIDIA-accelerated ETL tools?
A. Use NVIDIA RAPIDS cuDF for data transformations and Dask-cuDF for parallelized processing across multiple GPUs.
B. Use Apache Spark with CPU-based processing instead of leveraging GPU acceleration.
C. Implement ETL processes using only SQL-based transformations within a relational database system.
D. Perform all transformations using Pandas DataFrames and then use multiprocessing to parallelize the workload on CPUs.
Question 2
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. Compute a rolling average and manually adjust for seasonality based on previous peak sales months.
C. Use a Seasonal Autoregressive Integrated Moving Average (SARIMA) model and optimize it using NVIDIA RAPIDS.
D. Train a Decision Tree model using cuML to classify future sales trends.
Question 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('float32')
B. df['revenue'] = df['revenue'].astype('uint32')
C. df['revenue'] = df['revenue'].astype('int64')
D. df['revenue'] = df['revenue'].astype('float64')
Question 4
Which of the following is the most efficient method for processing big data in a distributed environment using NVIDIA technologies?
A. Using cloud-based, non-GPU infrastructure for data processing
B. Using a single-node GPU-based solution for data processing.
C. Relying on traditional CPU-only processing frameworks for big data tasks.
D. Utilizing a distributed data processing framework like Apache Spark with GPU acceleration from the NVIDIA RAPIDS library.
Question 5
You are tasked with optimizing the performance of a large-scale data science project that involves deep learning models on a cloud infrastructure. Your organization is using GPUs for model training.
Which of the following strategies would be the most effective in optimizing GPU performance for data science tasks? (Select two)
A. Optimize GPU performance by limiting the number of threads running on each GPU.
B. Use larger batch sizes to make better use of GPU memory during model training.
C. Utilize multi-GPU training to parallelize the workload, reducing training time.
D. Overclock the GPU to achieve higher computational speeds and improve training times.
E. Use a single cloud instance with the largest GPU available to ensure maximum performance.
Solutions:
| Question 1 Answer: A | Question 2 Answer: C | Question 3 Answer: D | Question 4 Answer: D | Question 5 Answer: B,C |
