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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q17-Q22):

NEW QUESTION # 17
Which of the following is the most efficient method for processing big data in a distributed environment using NVIDIA technologies?

A. Relying on traditional CPU-only processing frameworks for big data tasks.
B. Utilizing a distributed data processing framework like Apache Spark with GPU acceleration from the NVIDIA RAPIDS library.
C. Using a single-node GPU-based solution for data processing.
D. Using cloud-based, non-GPU infrastructure for data processing

Answer: B

NEW QUESTION # 18
You need to deploy a machine learning model on a GPU-equipped system. The GPU has 16GB of VRAM, and the model requires approximately 12GB of memory during inference. However, additional system processes and other applications consume 5GB of VRAM.
What would happen if you attempt to run inference without making any optimizations, and how should you resolve the issue?

A. The model will fail to run due to out-of-memory (OOM) errors, and using a smaller batch size can help reduce memory usage
B. The model will run without issues because 16GB of VRAM is sufficient for a 12GB model
C. Switching from a GPU to CPU inference will resolve memory issues without performance loss
D. The model will run successfully but with reduced performance due to memory fragmentation

Answer: A

NEW QUESTION # 19
When scaling a distributed data processing framework using NVIDIA GPU technology for big data processing, which of the following factors is most critical to optimize performance?

A. Using more CPU cores to handle computation-heavy tasks.
B. Ensuring the proper configuration of GPU resources across all nodes in the distributed system.
C. Limiting the number of GPU nodes used in the cluster to avoid complexity.
D. Maximizing the amount of data transferred between GPUs for faster processing.

Answer: B

NEW QUESTION # 20
You need to benchmark GPU-accelerated data science frameworks across both cloud-based and on-premise GPU setups.
Which of the following is the most effective strategy for ensuring consistent and reliable benchmarking results?

A. Prioritizing synthetic benchmarks over real-world workloads to get a more controlled performance measurement.
B. Running each benchmark multiple times and averaging the results to account for variance in execution time.
C. Running benchmarks in isolated virtual machines to simulate real-world multi-tenant cloud environments.
D. Using mixed cloud and on-premise GPU instances to test various hardware types and configurations.

Answer: B

NEW QUESTION # 21
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. Storing and retrieving models from a centralized object storage system.
C. Model monitoring by logging metadata and performance metrics in a database.
D. Running CI/CD workflows for code integration and deployment using a traditional CPU-based Jenkins setup.

Answer: A

NEW QUESTION # 22
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