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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q51-Q56):

NEW QUESTION # 51
Which of the following hardware components is most critical for accelerating the training of deep learning models?

A. Solid State Drive (SSD)
B. Central Processing Unit (CPU)
C. Random Access Memory (RAM)
D. Graphics Processing Unit (GPU)

Answer: D

NEW QUESTION # 52
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 cloud-based, non-GPU infrastructure for data processing
D. Using a single-node GPU-based solution for data processing.

Answer: B

NEW QUESTION # 53
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 cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.
B. Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
C. Use PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.
D. Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.

Answer: A

NEW QUESTION # 54
You are tasked with comparing the performance of different GPU-accelerated frameworks for a deep learning model. The frameworks you are considering are TensorFlow, PyTorch, and CUDA. To evaluate the performance, you decide to implement a benchmark that measures GPU efficiency, memory usage, and speed.
Which of the following actions should you take to design an effective benchmark? (Select two)

A. Use a batch size that is optimal for each framework's memory management.
B. Use CPU-based implementations of the same frameworks for comparison.
C. Measure GPU utilization and memory usage, but ignore the network and disk I/O.
D. Benchmark only the training phase of the deep learning model.
E. Use a common dataset for all frameworks to ensure comparability.

Answer: A,E

NEW QUESTION # 55
A financial institution is using cuGraph to analyze transaction data and detect potential fraudulent activity. The institution wants to identify users who have a high likelihood of being involved in suspicious activities based on the structure of their transactions.
Which of the following cuGraph algorithms would be the best choice for this task?

A. Weakly Connected Components
B. Breadth-First Search (BFS)
C. Betweenness Centrality
D. Spectral Clustering

Answer: C

NEW QUESTION # 56
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