Quiz NCP-ADS - NVIDIA-Certified-Professional Accelerated Data Science Marvelous Valid Test Prep

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NVIDIA NCP-ADS Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning	15%	- Model evaluation and validation - Model training and hyperparameter tuning - GPU-accelerated ML frameworks and algorithms - Distributed training strategies
Topic 2: GPU and Cloud Computing	16%	- GPU architecture and acceleration principles - Cloud GPU environments and deployment - Resource management and scaling strategies - CRISP-DM and data science methodology
Topic 3: Data Preparation	17%	- Data validation and quality assurance - Data cleaning, preprocessing and transformation - Feature engineering and data type optimization - Workflow monitoring and bottleneck identification
Topic 4: Data Analysis	14%	- Distributed and parallel data processing - Exploratory Data Analysis (EDA) - Time-series analysis and anomaly detection - Data visualization and graph analytics
Topic 5: Data Manipulation and Software Literacy	19%	- Data processing libraries selection and usage - Performance profiling and optimization tools - GPU-accelerated ETL workflows - Dependency management and containerization
Topic 6: MLOps	19%	- Pipeline automation and orchestration - Model deployment and serving - Monitoring, logging and maintenance - End-to-end workflow management

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q46-Q51):

NEW QUESTION # 46
You are deploying a deep learning model on an edge device with 8GB of available RAM. The model's estimated peak memory usage, including model weights, intermediate tensors, and batch data, is 9.5GB.
What is the best course of action to ensure successful deployment while maintaining performance?

A. Increase the device's swap space to compensate for insufficient RAM
B. Reduce the batch size during inference
C. Reduce the number of model parameters by removing layers from the architecture
D. Offload some computation to cloud-based processing

Answer: B

NEW QUESTION # 47
A company is deploying an MLOps pipeline for training and serving deep learning models. The data scientists want to leverage GPU acceleration at multiple stages of the pipeline to enhance efficiency.
Which of the following steps would benefit the most from GPU acceleration?

A. Training and inference workloads using deep learning models with TensorFlow or PyTorch.
B. Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.
C. Model monitoring by logging metadata and performance metrics in a database.
D. Storing and retrieving models from a centralized object storage system.

Answer: A

NEW QUESTION # 48
You are working with a data science project that requires GPU acceleration for machine learning tasks. Your team is facing challenges with software version conflicts between different dependencies when deploying the project on different systems.
Which of the following solutions should you consider to efficiently manage software dependencies and avoid conflicts? (Select two)

A. Install GPU drivers on the host machine and rely on the local system environment for dependency management.
B. Set up a virtual machine for each different dependency configuration to isolate environments.
C. Use Conda to create isolated environments for different versions of dependencies, ensuring version compatibility.
D. Manually install all dependencies directly on the host machine to avoid using dependency management tools.
E. Use Docker to containerize the project, ensuring that the dependencies and environment are consistent across different systems.

Answer: C,E

NEW QUESTION # 49
A data scientist is working with large-scale datasets in a RAPIDS AI pipeline and needs to efficiently process and organize the data while leveraging GPU acceleration.
Which of the following approaches best ensures optimized processing and memory management when using NVIDIA technologies?

A. Write intermediate data to disk as CSV files before reading them back into RAPIDS AI to avoid memory overflow.
B. Store data in a pandas DataFrame first and then convert it to cuDF only when GPU operations are needed.
C. Use cuDF to store and process data in GPU memory, leveraging its vectorized operations for transformations and aggregations.
D. Process data using Apache Spark on CPU before transferring results to GPU for model training.

Answer: C

NEW QUESTION # 50
Which of the following best describes the purpose of the NVIDIA TensorRT library?

A. Manages GPU resources for deep learning models
B. Optimizes and accelerates inference of trained models
C. Provides hardware abstraction for AI model development
D. Accelerates training of neural networks

Answer: B

NEW QUESTION # 51
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