Reliable NVIDIA NCP-ADS Exam Syllabus | New NCP-ADS Dumps Pdf

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q57-Q62):

NEW QUESTION # 57
You are consulting for a retail company that collects data from daily sales transactions, customer interactions, and inventory tracking across multiple locations. They are unsure whether their dataset qualifies as big data and which processing method would be most suitable.
Which of the following characteristics best indicate that the dataset requires big data processing and acceleration techniques?

A. The dataset exceeds the memory capacity of a single machine and requires distributed or GPU- accelerated processing
B. The dataset contains more than 1 million records, making it impossible to process using pandas
C. The dataset includes a mix of numerical and categorical variables, requiring additional preprocessing
D. The dataset is stored in multiple relational database tables, making querying inefficient

Answer: A

NEW QUESTION # 58
You are working with a large-scale financial dataset containing stock prices over the past 10 years.
Your goal is to forecast future prices using deep learning techniques optimized for GPU acceleration.
Which of the following approaches would be the most suitable for achieving accurate and efficient forecasting?

A. Use an LSTM (Long Short-Term Memory) network optimized with NVIDIA RAPIDS and CuDNN acceleration.
B. Apply a simple moving average (SMA) over historical stock prices and extrapolate future values.
C. Use a k-Nearest Neighbors (k-NN) algorithm to identify similar historical price patterns and predict future values.
D. Apply Principal Component Analysis (PCA) to extract dominant trends and use them for forecasting.

Answer: A

NEW QUESTION # 59
A manufacturing company wants to detect anomalies in a real-time IoT sensor dataset using NVIDIA technologies. The data arrives as a continuous stream, and the company needs to process it efficiently for immediate anomaly detection.
Which NVIDIA-powered approach is the most suitable for this use case?

A. Leverage cuGraph to model the sensor readings as a graph and detect anomalies using centrality measures
B. Deploy an Autoencoder-based deep learning model using NVIDIA TensorRT for real-time inference
C. Use RAPIDS cuDF with batch processing to analyze historical anomalies every few hours
D. Apply cuML's Isolation Forest on incoming streaming data for real-time anomaly detection

Answer: D

NEW QUESTION # 60
You are optimizing a data pipeline for a large-scale machine learning project using NVIDIA RAPIDS and Apache Spark. The pipeline performs many expensive shuffle operations.
Which of the following is the most effective method to reduce shuffle and improve performance using NVIDIA technologies?

A. Use RAPIDS cuDF to perform in-memory data processing on GPU before shuffling to avoid network communication.
B. Implement a custom shuffle partitioning scheme using NVIDIA DALI for more control over data partitioning during shuffle operations.
C. Use RAPIDS cuDF to repartition the data in the GPU memory after each shuffle.
D. Store data in HDFS before performing shuffle operations to reduce GPU memory overhead.

Answer: A

NEW QUESTION # 61
Which NVIDIA technology is specifically designed for accelerating deep learning workloads in the cloud?

A. NVIDIA Tesla
B. TensorRT
C. NVIDIA A100
D. NVIDIA Jetson

Answer: C

NEW QUESTION # 62
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