Exam NCP-ADS Topics | NCP-ADS Latest Dumps Pdf

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q291-Q296):

NEW QUESTION # 291
You are building a predictive model for retail sales forecasting and need a dataset that includes historical sales transactions, customer demographics, and external economic indicators (e.g., inflation rate, unemployment rate).
Which of the following datasets would be the most appropriate for your model?

A. A public dataset of annual GDP per country
B. A dataset with global temperature trends over the past decade
C. A dataset containing transaction history and customer profiles from a retail company
D. A dataset of product reviews and customer sentiments from an e-commerce website

Answer: C

NEW QUESTION # 292
You are building a deep learning model using TensorFlow with cuDNN acceleration on an NVIDIA GPU. Your dataset contains continuous numerical features with vastly different ranges.
What is the best way to standardize the data efficiently to improve model convergence?

A. Apply batch normalization layers in the neural network to handle feature scaling dynamically during training.
B. Use cupy.linalg.norm() to normalize each feature vector individually to unit length.
C. Use cuml.StandardScaler() from RAPIDS to normalize the dataset before feeding it into the model.
D. Manually compute the feature mean and variance on the CPU and apply the transformation before training.

Answer: C

NEW QUESTION # 293
A data scientist is training a deep learning model on an NVIDIA GPU and wants to profile the model to identify performance bottlenecks. The scientist chooses to use NVIDIA DLProf.
Which of the following steps is the most effective way to profile the model using DLProf?

A. Modify the training script to manually insert timing functions for each layer and compare execution times.
B. Use nvprof instead of DLProf since it provides more detailed profiling for deep learning workloads.
C. Run the model using dlprof --mode profile to collect performance metrics and generate a report.
D. Rely on general CPU profiling tools like perf and gprof to analyze GPU performance.

Answer: C

NEW QUESTION # 294
A data scientist is analyzing large-scale sensor readings from an industrial IoT system and wants to visualize high-frequency time-series data efficiently.
Which approach using NVIDIA technologies would be the most effective for interactive visualization of this dataset?

A. Use NVIDIA OptiX to ray-trace the time-series dataset for visualization.
B. Process the data using RAPIDS cuDF, then convert it to CSV and visualize it with Excel charts.
C. Apply TensorRT to compress the time-series data and use PyTorch's native visualization functions.
D. Use RAPIDS cuDF with Datashader to render large time-series data on the GPU efficiently.

Answer: D

NEW QUESTION # 295
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 PyTorch with CPU acceleration to train a convolutional neural network (CNN) for forecasting.
B. Use Dask with pandas for data preprocessing, then train a TensorFlow LSTM model on the CPU.
C. Use cuDF to load and preprocess the data, then apply FB Prophet for forecasting.
D. Use cuDF for data preprocessing and train an XGBoost model with GPU acceleration for forecasting.

Answer: D

NEW QUESTION # 296
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