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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Data Preparation	17%	- Data validation and quality assurance - Data cleaning, preprocessing and transformation - Workflow monitoring and bottleneck identification - Feature engineering and data type optimization
GPU and Cloud Computing	16%	- CRISP-DM and data science methodology - GPU architecture and acceleration principles - Resource management and scaling strategies - Cloud GPU environments and deployment
MLOps	19%	- Monitoring, logging and maintenance - Pipeline automation and orchestration - Model deployment and serving - End-to-end workflow management
Data Analysis	14%	- Time-series analysis and anomaly detection - Exploratory Data Analysis (EDA) - Distributed and parallel data processing - Data visualization and graph analytics
Data Manipulation and Software Literacy	19%	- GPU-accelerated ETL workflows - Performance profiling and optimization tools - Dependency management and containerization - Data processing libraries selection and usage
Machine Learning	15%	- Model evaluation and validation - Distributed training strategies - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q81-Q86):

NEW QUESTION # 81
You are training a deep learning model for image classification and want to optimize its hyperparameters, including learning rate, batch size, and number of layers.
Which of the following techniques is the most effective for efficiently searching through a high- dimensional hyperparameter space?

A. Random Search
B. Grid Search
C. Gradient Descent
D. Bayesian Optimization

Answer: D

NEW QUESTION # 82
You are working on an MLOps pipeline that involves loading a large dataset for training a deep learning model on an NVIDIA GPU. Before training, you need to ensure that the dataset fits within the available GPU memory.
Which of the following commands in Python using the pandas and numpy libraries can correctly determine the memory size of a dataset?

A. np.array(df).nbytes
B. df.info(memory_usage='deep')
C. df.memory_usage(deep=True).sum()
D. sys.getsizeof(df)

Answer: C

NEW QUESTION # 83
A data scientist is working with a large dataset containing millions of records and aims to accelerate the data preprocessing workflow using NVIDIA technologies.
Which of the following approaches is the most effective for optimizing data preprocessing performance using GPUs?

A. Running data transformations in a multi-threaded CPU environment
B. Using Dask to distribute processing across multiple CPU cores
C. Using NumPy for array computations and parallelizing with Python's multiprocessing
D. Using RAPIDS cuDF to replace pandas operations

Answer: D

NEW QUESTION # 84
You are deploying an NVIDIA GPU-accelerated machine learning model in a Docker container and want to ensure that your application can leverage the GPU efficiently.
What is the best way to manage CUDA dependencies and avoid compatibility issues inside your Docker container?

A. Use a base Ubuntu image and install TensorFlow, PyTorch, and CUDA using pip install inside the container.
B. Manually install CUDA and cuDNN inside the container by downloading them from NVIDIA's website and setting environment variables.
C. Disable GPU acceleration in Docker and force computations on the CPU to avoid CUDA compatibility issues.
D. Use NVIDIA's official Docker images from NVIDIA GPU Cloud (NGC), which come with pre-installed CUDA and AI frameworks.

Answer: D

NEW QUESTION # 85
You are tasked with profiling a deep learning model using NVIDIA's DLProf to identify performance bottlenecks and optimize resource utilization.
Which of the following statements correctly describes the capabilities of DLProf?

A. DLProf is primarily designed for debugging model accuracy rather than performance analysis.
B. DLProf requires significant modifications to the source code to collect profiling data.
C. DLProf can generate detailed reports that highlight kernel-level execution times and GPU utilization trends.
D. DLProf only works with TensorFlow models and does not support PyTorch-based workloads.

Answer: C

NEW QUESTION # 86
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