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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| 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 |
| Data Preparation | 17% | - Data validation and quality assurance - Feature engineering and data type optimization - Workflow monitoring and bottleneck identification - Data cleaning, preprocessing and transformation |
| Data Analysis | 14% | - Exploratory Data Analysis (EDA) - Time-series analysis and anomaly detection - Data visualization and graph analytics - Distributed and parallel data processing |
| Machine Learning | 15% | - GPU-accelerated ML frameworks and algorithms - Distributed training strategies - Model evaluation and validation - Model training and hyperparameter tuning |
| GPU and Cloud Computing | 16% | - GPU architecture and acceleration principles - Resource management and scaling strategies - CRISP-DM and data science methodology - Cloud GPU environments and deployment |
| MLOps | 19% | - End-to-end workflow management - Monitoring, logging and maintenance - Model deployment and serving - Pipeline automation and orchestration |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
1. You are working on a large-scale machine learning workload that involves training a deep learning model using multiple GPUs. You want to leverage Dask to implement data parallelism efficiently using NVIDIA GPUs.
Which of the following approaches best achieves data parallelism in this context?
A) Use Dask DataFrame to parallelize deep learning model training across multiple GPUs
B) Leverage Dask-CUDA to automatically assign computations to available GPUs using the worker pool
C) Use Dask with CuPy to distribute NumPy-based computations across multiple GPUs
D) Run a single large Dask task on the CPU and use Dask-MPI for multi-GPU execution
2. Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?
A) NVIDIA A100
B) NVIDIA Jetson
C) TensorRT
D) NVIDIA Tesla
3. You are working on a time-series forecasting project using NVIDIA RAPIDS and GPU-accelerated machine learning. The dataset consists of 10 years of daily stock price data. Your goal is to implement a model that efficiently handles large-scale time-series data while leveraging GPU acceleration for optimal performance.
Which approach best utilizes NVIDIA technologies for efficient forecasting?
A) Use PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.
B) Use cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.
C) Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.
D) Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
4. You are working with a large dataset containing missing values, and you need to clean and preprocess the data efficiently.
Which of the following methods provides the best performance when handling missing values in a GPU- accelerated EDA workflow using RAPIDS?
A) Drop all missing values using df.dropna() in Pandas before using RAPIDS.
B) Convert the dataset to a cuDF DataFrame and use cudf.DataFrame.fillna() to fill missing values.
C) Ignore missing values since GPU acceleration can handle incomplete data without performance degradation.
D) Use pandas.DataFrame.fillna() on the dataset before converting it to a cuDF DataFrame.
5. You are working on a predictive maintenance model for industrial equipment. The dataset includes various sensor readings, categorical metadata, and timestamped events.
Which data type is the best choice for a feature representing the operating status of a machine, which has three possible states: "Idle", "Running", and "Error"?
A) Integer (0, 1, 2) to represent each state numerically.
B) Floating-point representation (e.g., 0.1 for Idle, 0.5 for Running, 0.9 for Error).
C) Categorical encoding (one-hot encoding or ordinal encoding).
D) Text (storing states as raw strings like "Idle", "Running", "Error").
Solutions:
| Question # 1 Answer: B | Question # 2 Answer: A | Question # 3 Answer: B | Question # 4 Answer: B | Question # 5 Answer: C |
