NCP-ADS Exam Questions - NVIDIA-Certified-Professional Accelerated Data Science Exam Tests & NCP-ADS Test Guide

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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 - End-to-end workflow management - Model deployment and serving
Topic 2: Data Manipulation and Software Literacy	19%	- GPU-accelerated ETL workflows - Performance profiling and optimization tools - Data processing libraries selection and usage - Dependency management and containerization
Topic 3: Machine Learning	15%	- GPU-accelerated ML frameworks and algorithms - Distributed training strategies - Model training and hyperparameter tuning - Model evaluation and validation
Topic 4: 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
Topic 5: Data Analysis	14%	- Exploratory Data Analysis (EDA) - Distributed and parallel data processing - Data visualization and graph analytics - Time-series analysis and anomaly detection
Topic 6: Data Preparation	17%	- Feature engineering and data type optimization - Data cleaning, preprocessing and transformation - Data validation and quality assurance - Workflow monitoring and bottleneck identification

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q46-Q51):

NEW QUESTION # 46
You are working on a medium-sized dataset (~500,000 rows, 20 columns) and need to perform fast exploratory data analysis (EDA) with filtering, aggregations, and transformations.
Which of the following Python libraries would be the most efficient choice for this task?

A. Vaex
B. Pandas
C. PySpark
D. Dask

Answer: B

NEW QUESTION # 47
You are using NVIDIA DLProf to analyze the performance of a deep learning model deployed on an A100 GPU. The report indicates that compute-bound operations are dominating execution time, and kernel execution efficiency is below 50%.
What is the best action to take based on this insight?

A. Increase the batch size to fully utilize available GPU memory and reduce per-sample processing overhead.
B. Enable mixed precision training to improve computational efficiency.
C. Reduce the number of layers in the model to decrease computation time.
D. Use DLProf's Tensor Core Analysis to check if the model is leveraging Tensor Cores effectively.

Answer: D

NEW QUESTION # 48
Which of the following is the main advantage of using TensorRT for inference in an accelerated data science pipeline?

A. TensorRT automatically builds training models from raw data without requiring pre-trained models.
B. TensorRT is mainly used for data visualization and not for model inference.
C. TensorRT optimizes deep learning models to run efficiently on NVIDIA GPUs by reducing precision while maintaining accuracy.
D. TensorRT is only compatible with image classification models and does not support other model types.

Answer: C

NEW QUESTION # 49
What is the primary advantage of using NVIDIA Triton Inference Server for deploying and monitoring machine learning models in production?

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

Answer: B

NEW QUESTION # 50
You need to benchmark GPU-accelerated data science frameworks across both cloud-based and on-premise GPU setups.
Which of the following is the most effective strategy for ensuring consistent and reliable benchmarking results?

A. Using mixed cloud and on-premise GPU instances to test various hardware types and configurations.
B. Running benchmarks in isolated virtual machines to simulate real-world multi-tenant cloud environments.
C. Running each benchmark multiple times and averaging the results to account for variance in execution time.
D. Prioritizing synthetic benchmarks over real-world workloads to get a more controlled performance measurement.

Answer: C

NEW QUESTION # 51
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