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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| MLOps | 19% | - Model deployment and serving - Monitoring, logging and maintenance - End-to-end workflow management - Pipeline automation and orchestration |
| Data Analysis | 14% | - Distributed and parallel data processing - Data visualization and graph analytics - Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA) |
| Data Preparation | 17% | - Data validation and quality assurance - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization |
| Machine Learning | 15% | - Model training and hyperparameter tuning - Distributed training strategies - GPU-accelerated ML frameworks and algorithms - Model evaluation and validation |
| Data Manipulation and Software Literacy | 19% | - Performance profiling and optimization tools - GPU-accelerated ETL workflows - Dependency management and containerization - Data processing libraries selection and usage |
| GPU and Cloud Computing | 16% | - GPU architecture and acceleration principles - CRISP-DM and data science methodology - Resource management and scaling strategies - Cloud GPU environments and deployment |
Questions and Answers About NVIDIA-Certified-Professional Accelerated Data Science
NVIDIA Deep Learning Institute: Visit official page
NVIDIA Certification Portal: register for NCP-ADS
The package includes 303 practice questions for the NCP-ADS exam, all with verified answers. The same content is available in three formats, so you can switch freely.
The NCP-ADS exam contains 60–70 questions to complete within 120 minutes minutes. Rehearsing under the same limit on the simulator builds calm, automatic pacing.
The PDF reads on phones, tablets, and computers, and it prints cleanly for candidates who like marking notes on paper. The PC file is an interactive simulator that makes rehearsal engaging. The online file is the updated edition of the PC version: intelligent functions, real-test simulation, and no limit on installation devices. All three carry the same questions and answers.
Recommended: 2–3 years hands-on experience in accelerated data science; strong knowledge of machine learning, GPU computing, and Python; experience with RAPIDS, CUDA, and related tools
- Data Analysis (14%)
- MLOps (19%)
- Machine Learning (15%)
Plan your hours according to each topic's weight.
NCP-ADS requires Pass/Fail only, no specific score published to pass, and the official exam fee is $200 USD. Fixing both numbers early keeps your practice target and your budget on the same page.
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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
A data science team wants to leverage GPU acceleration for detecting anomalies in a massive IoT sensor dataset that is continuously streaming.
Which of the following NVIDIA-supported methods would be the best for handling real-time anomaly detection in a high-throughput environment?
- A. Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.
- B. Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.
- C. Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.
- D. Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.
Correct Answer: A 🗳️
You have developed a deep learning model using TensorFlow and trained it on an NVIDIA A100 GPU. The model is deployed in production and serves real-time inference requests. However, the inference latency is high, and you need to optimize performance without retraining the model.
Which of the following approaches is the most effective for optimizing inference performance using NVIDIA technologies?
- A. Convert the model to ONNX format and use TensorRT for inference optimization.
- B. Enable mixed precision training and retrain the model to improve inference speed.
- C. Implement data augmentation techniques to improve inference efficiency.
- D. Reduce the batch size to decrease computational overhead and improve latency.
Correct Answer: A 🗳️
A data scientist is using RAPIDS cuML to build a predictive model on a large dataset containing numerical and categorical features.
To optimize feature engineering for accelerated GPU processing, which of the following is the best approach?
- A. Apply one-hot encoding on high-cardinality categorical features directly in cuDF without any transformation.
- B. Convert all numerical features to float64 to maximize precision for feature transformation.
- C. Convert all categorical variables to string format for efficient processing in cuDF.
- D. Normalize numerical features using min-max scaling implemented with cuDF, leveraging GPU acceleration.
Correct Answer: D 🗳️
You are working on a dataset containing missing values, duplicate records, and inconsistent data types.
The dataset size is 15GB and you need to efficiently perform data cleansing operations such as:
- Handling missing values
- Dropping duplicates
- Converting data types
Which of the following approaches would be the most efficient way to perform these operations on an NVIDIA GPU?
- A. Load the dataset using cuDF, then use cuDF's built-in .dropna(), .drop_duplicates(), and .astype() methods
- B. Use Vaex to perform data cleansing, as it is optimized for large-scale datasets
- C. Use pandas to load the dataset and perform the operations using standard pandas functions
- D. Convert the dataset into a NumPy array and process it using CuPy before converting it back to a DataFrame
Correct Answer: A 🗳️
You are building an MLOps pipeline for a predictive model that uses tabular data with both categorical and numerical features.
To ensure efficient data processing and optimal model training on an NVIDIA GPU, which of the following data types would be most suitable for a categorical feature representing different product categories?
- A. Int64
- B. String
- C. Float64
- D. Int32
Correct Answer: D 🗳️
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