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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q33-Q38):

NEW QUESTION # 33
A machine learning engineer runs NVIDIA DLProf to analyze the performance of a deep learning model and receives a report indicating high GPU idle time.
What is the most likely cause of this issue?

A. The GPU is not powerful enough to process the deep learning model efficiently.
B. The batch size is too large, leading to excessive GPU memory utilization and slow processing.
C. The CUDA cores are overheating, leading to automatic throttling of computations.
D. The model is experiencing data loading bottlenecks, causing the GPU to wait for input batches.

Answer: D

NEW QUESTION # 34
Which of the following is the most efficient way to implement data parallelism using Dask for multi- GPU scaling on an Nvidia platform?

A. Use Dask with the dask_cuda package to distribute computation across GPUs, ensuring that each GPU is responsible for a portion of the data, while using distributed.Client to connect the GPU workers.
B. Use Dask with the dask_gpu package to assign data chunks across GPUs manually, without utilizing the distributed.Client.
C. Use Dask on a single GPU machine with no GPU-specific optimization, treating the system as if it were CPU-only.
D. Use Dask with a single GPU, distributing data across multiple workers within the GPU without considering GPU memory limitations.

Answer: A

NEW QUESTION # 35
A research team is analyzing a large transportation network and wants to identify groups of locations that are closely connected based on travel routes.
Which NVIDIA cuGraph function would be the most effective for this task?

A. Use cuGraph's pagerank() to rank the most connected locations.
B. Use cuGraph's shortest_path() to compute all shortest paths and derive clusters manually.
C. Apply cuGraph's bfs() (Breadth-First Search) to find clusters of locations.
D. Use cuGraph's louvain() function to detect communities within the graph.

Answer: D

NEW QUESTION # 36
Which of the following best describes the purpose of the NVIDIA TensorRT library?

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

Answer: A

NEW QUESTION # 37
You are working with a large dataset in NVIDIA RAPIDS cuDF and notice that the data processing pipeline is taking longer than expected.
Which of the following tools or techniques can help you identify and analyze bottlenecks in the pipeline?

A. Use cudf.DataFrame.astype(str) to convert numerical values to strings for easier debugging.
B. Enable pandas compatibility mode in cudf to improve performance.
C. Use nvtx.annotate() to label sections of code and analyze performance in Nsight Systems.
D. Use cuml.preprocessing.StandardScaler() to normalize the dataset before processing.

Answer: C

NEW QUESTION # 38
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