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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q278-Q283):

NEW QUESTION # 278
Which of the following hardware components is most critical for accelerating the training of deep learning models?

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

Answer: B

NEW QUESTION # 279
What is the primary advantage of using NVIDIA Triton Inference Server for deploying and monitoring machine learning models in production?

A. It only supports TensorFlow models for inference.
B. It is designed solely for edge devices and not for data centers.
C. It provides GPU optimization to handle high-throughput inference workloads.
D. It automatically tunes hyperparameters for all models.

Answer: C

NEW QUESTION # 280
You are working with a dataset consisting of 100 million records stored in a distributed system. The dataset includes numerical and categorical variables, requiring both exploratory data analysis (EDA) and machine learning model training. The processing time using traditional CPU-based methods is too slow.
Which of the following techniques would be the most effective acceleration method to handle this workload efficiently?

A. Store the dataset in a relational database and query it using SQL
B. Use RAPIDS cuDF for GPU-accelerated data processing
C. Reduce the dataset to a smaller sample size before processin
D. Scale up to a high-core-count CPU machine

Answer: B

NEW QUESTION # 281
You are monitoring a GPU-accelerated ETL pipeline using RAPIDS cuDF and Dask-cuDF. You suspect that a bottleneck is causing the pipeline to slow down.
Which of the following methods is the most effective way to diagnose performance bottlenecks in your data processing pipeline?

A. Increase the batch size of data loading without checking GPU memory usage
B. Monitor CPU usage in the system to detect high CPU load that might indicate a bottleneck
C. Use print() statements in the code to manually track execution times of different operation
D. Use NVIDIA Nsight Systems to profile GPU utilization and identify potential kernel execution inefficiencies

Answer: D

NEW QUESTION # 282
You are building a real-time recommendation system that processes high-frequency transactional data from millions of users.
The system must:
- Ingest and preprocess data efficiently
- Perform similarity computations for user-item recommendations
- Scale to handle rapid incoming transactions
Which of the following NVIDIA technologies is the best choice for this use case?

A. NVIDIA Triton Inference Server
B. NVIDIA NVTabular
C. CUDA Kernels with Custom C++ Code
D. RAPIDS cuGraph

Answer: B

NEW QUESTION # 283
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