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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
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| Machine Learning | 15% | - Model Development and Optimization
|
| MLOps | 19% | - Deployment and Monitoring
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You have a multi-GPU cluster running RAPIDS with Dask to process a large dataset stored in Apache Parquet format. During execution, you notice some GPUs are underutilized, while others are overloaded, leading to uneven processing times.
What is the most effective way to balance the workload across GPUs?
- A. Split the dataset into smaller chunks manually and assign them to GPUs
- B. Switch to a CPU-based framework like Spark to distribute the load evenly
- C. Use Dask's adaptive scaling to dynamically adjust the number of GPU workers
- D. Increase the number of worker threads per GPU manually
Correct Answer: C 🗳️
Which of the following best describes the role of MLOps in the context of NVIDIA technologies for deploying machine learning models in production? (Select two)
- A. MLOps helps manage the lifecycle of machine learning models, ensuring efficient collaboration and model governance
- B. MLOps replaces the need for data preprocessing during training and deployment
- C. MLOps ensures that models trained on GPUs can only run on GPUs during deployment
- D. MLOps frameworks support version control and automation, ensuring reproducibility and scalability of ML workflows
Correct Answer: A,D 🗳️
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. Perform all transformations using Pandas DataFrames and then use multiprocessing to parallelize the workload on CPUs.
- B. Use Apache Spark with CPU-based processing instead of leveraging GPU acceleration.
- C. Use NVIDIA RAPIDS cuDF for data transformations and Dask-cuDF for parallelized processing across multiple GPUs.
- D. Implement ETL processes using only SQL-based transformations within a relational database system.
Correct Answer: C 🗳️
Which of the following techniques are best suited for efficiently processing and organizing large datasets using NVIDIA technologies? (Select two)
- A. Using cuDF for GPU-accelerated DataFrame operations
- B. Using TensorFlow with custom data pipelines for data loading
- C. Leveraging Dask for distributed GPU-based parallel computing
- D. Storing data directly on an HDD and processing with GPUs
Correct Answer: A,C 🗳️
You are analyzing a time-series dataset that represents temperature readings from an industrial sensor. Your goal is to detect anomalies that may indicate sensor failures or environmental changes.
Which of the following methods would be the most appropriate statistical technique for detecting anomalies in this time-series dataset?
- A. Principal Component Analysis (PCA)
- B. Moving Average Smoothing
- C. One-Hot Encoding
- D. Z-score (Standard Score) Analysis
Correct Answer: D 🗳️
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