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NVIDIA NCP-ADS Exam Overview:
| Certification Vendor: | NVIDIA |
| Exam Name: | NVIDIA-Certified Professional: Accelerated Data Science |
| Exam Number: | NCP-ADS |
| Exam Price: | 1580 CNY (~$200 USD) |
| Real Exam Qty: | 60-70 |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple-choice, Scenario-based multiple-choice |
| Available Languages: | Chinese, English |
| Related Certifications: | NVIDIA-Certified Associate: Accelerated Data Science (NCA-ADS) |
| Exam Duration: | 120 minutes |
| Recommended Training: | Accelerating End-to-End Data Science Workflows (DLI) Fundamentals of Accelerated Data Science |
| Exam Registration: | NVIDIA Training & Certification Portal NVIDIA Certification Support |
| Sample Questions: | NVIDIA NCP-ADS Sample Questions |
| Exam Way: | Proctored exam (online or authorized test center depending on region) |
| Pre Condition: | 2–3 years of experience in accelerated data science, machine learning, and GPU computing |
| Official Syllabus URL: | https://www.nvidia.cn/training/certification/accelerated-data-science-professional/ |
NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 15% | - Model Development and Optimization
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| 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
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. A data scientist is working with a 50 TB dataset consisting of structured logs from IoT devices. The data needs to be cleaned, transformed, and aggregated before training a machine learning model.
Which of the following frameworks would be the most efficient choice for distributed data processing?
A) Apache Spark with RAPIDS Accelerator
B) Pandas
C) SQLite
D) Python multiprocessing module
2. You are monitoring a GPU-accelerated ETL pipeline using RAPIDS cuDF and Dask-cuDF. You suspect that a bottleneck is causing the pipeline to slow down.
Which of the following methods is the most effective way to diagnose performance bottlenecks in your data processing pipeline?
A) Monitor CPU usage in the system to detect high CPU load that might indicate a bottleneck
B) Use print() statements in the code to manually track execution times of different operation
C) Use NVIDIA Nsight Systems to profile GPU utilization and identify potential kernel execution inefficiencies
D) Increase the batch size of data loading without checking GPU memory usage
3. 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 Triton Inference Server to train the model efficiently on a single GPU.
B) Use NVIDIA Magnum IO to optimize machine learning model parameters on the GPU.
C) Use NVIDIA RAPIDS cuML for GPU-accelerated machine learning model training.
D) Use NVIDIA Modulus to accelerate machine learning training and feature selection.
4. Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)
A) Using Apache Spark for distributed data processing
B) Using Dask for parallel processing of large datasets
C) Using SQLite as a local database for large-scale data analysis
D) Using SQLAlchemy to interact with databases for large data processing
E) Using Excel to manipulate large datasets
5. What is the primary advantage of using NVIDIA Triton Inference Server for deploying and monitoring machine learning models in production?
A) It automatically tunes hyperparameters for all models.
B) It is designed solely for edge devices and not for data centers.
C) It provides GPU optimization to handle high-throughput inference workloads.
D) It only supports TensorFlow models for inference.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: A,B | Question # 5 Answer: C |
NVIDIA NCP-ADS Exam Overview:
| Certification Vendor: | NVIDIA |
| Exam Name: | NVIDIA-Certified Professional: Accelerated Data Science |
| Exam Number: | NCP-ADS |
| Exam Price: | 1580 CNY (~$200 USD) |
| Real Exam Qty: | 60-70 |
| Certificate Validity Period: | 2 years |
| Exam Format: | Multiple-choice, Scenario-based multiple-choice |
| Available Languages: | Chinese, English |
| Related Certifications: | NVIDIA-Certified Associate: Accelerated Data Science (NCA-ADS) |
| Exam Duration: | 120 minutes |
| Recommended Training: | Accelerating End-to-End Data Science Workflows (DLI) Fundamentals of Accelerated Data Science |
| Exam Registration: | NVIDIA Training & Certification Portal NVIDIA Certification Support |
| Sample Questions: | NVIDIA NCP-ADS Sample Questions |
| Exam Way: | Proctored exam (online or authorized test center depending on region) |
| Pre Condition: | 2–3 years of experience in accelerated data science, machine learning, and GPU computing |
| Official Syllabus URL: | https://www.nvidia.cn/training/certification/accelerated-data-science-professional/ |
NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Machine Learning | 15% | - Model Development and Optimization
|
| Data Preparation | 17% | - Data Cleaning and Transformation
|
| 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
|
| Data Analysis | 14% | - Exploratory Data Analysis (EDA)
|
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. A data scientist is working with a 50 TB dataset consisting of structured logs from IoT devices. The data needs to be cleaned, transformed, and aggregated before training a machine learning model.
Which of the following frameworks would be the most efficient choice for distributed data processing?
A) Apache Spark with RAPIDS Accelerator
B) Pandas
C) SQLite
D) Python multiprocessing module
2. You are monitoring a GPU-accelerated ETL pipeline using RAPIDS cuDF and Dask-cuDF. You suspect that a bottleneck is causing the pipeline to slow down.
Which of the following methods is the most effective way to diagnose performance bottlenecks in your data processing pipeline?
A) Monitor CPU usage in the system to detect high CPU load that might indicate a bottleneck
B) Use print() statements in the code to manually track execution times of different operation
C) Use NVIDIA Nsight Systems to profile GPU utilization and identify potential kernel execution inefficiencies
D) Increase the batch size of data loading without checking GPU memory usage
3. 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 Triton Inference Server to train the model efficiently on a single GPU.
B) Use NVIDIA Magnum IO to optimize machine learning model parameters on the GPU.
C) Use NVIDIA RAPIDS cuML for GPU-accelerated machine learning model training.
D) Use NVIDIA Modulus to accelerate machine learning training and feature selection.
4. Which of the following tools or techniques are essential for effectively working with large-scale data in a distributed environment? (Select two)
A) Using Apache Spark for distributed data processing
B) Using Dask for parallel processing of large datasets
C) Using SQLite as a local database for large-scale data analysis
D) Using SQLAlchemy to interact with databases for large data processing
E) Using Excel to manipulate large datasets
5. What is the primary advantage of using NVIDIA Triton Inference Server for deploying and monitoring machine learning models in production?
A) It automatically tunes hyperparameters for all models.
B) It is designed solely for edge devices and not for data centers.
C) It provides GPU optimization to handle high-throughput inference workloads.
D) It only supports TensorFlow models for inference.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: C | Question # 3 Answer: C | Question # 4 Answer: A,B | Question # 5 Answer: C |
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