Latest NCP-ADS Exam Question & Exam Dumps NCP-ADS Zip

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q27-Q32):

NEW QUESTION # 27
A machine learning team has deployed a fraud detection model in production, and they want to monitor its performance over time to detect model drift and performance degradation.
Which monitoring strategy is most effective for this use case?

A. Using a static dataset for monitoring instead of real-time production data
B. Disabling logging to reduce system overhead and avoid storage costs
C. Logging only inference latency without tracking model predictions
D. Implementing model performance tracking with drift detection metrics

Answer: D

NEW QUESTION # 28
A data scientist is training a deep learning model and wants to find the best learning rate to optimize convergence speed and generalization. The scientist tests different values: A very small learning rate (0.00001) results in slow convergence.
A very large learning rate (10) causes the model loss to fluctuate wildly and not converge.
Which of the following strategies is the most effective way to optimize the learning rate dynamically during training?

A. Use a fixed learning rate chosen through trial and error
B. Use learning rate warm-up followed by decay
C. Decrease the learning rate to zero at the end of training (learning rate scheduling)
D. Use the same learning rate for all layers in a deep neural network

Answer: B

NEW QUESTION # 29
You are setting up a GPU-accelerated data science environment that includes NVIDIA RAPIDS, PyTorch, TensorFlow, and other libraries for machine learning and data processing.
Given that these frameworks have different dependencies and version requirements, what is the best approach to avoid software conflicts while ensuring reproducibility across multiple environments?

A. Install all packages globally using pip on the system-wide Python installation to ensure consistency.
B. Manually download and compile each library from source to guarantee compatibility across all versions.
C. Use a single Docker container with the latest versions of all dependencies installed system-wide.
D. Use Conda to create isolated virtual environments for each project and install dependencies via conda-forge or NVIDIA channels.

Answer: D

NEW QUESTION # 30
You are working on an AI-driven customer behavior prediction project.
According to the CRISP-DM (Cross Industry Standard Process for Data Mining) methodology, what is the most critical task to complete during the Data Understanding phase?

A. Deploying the model into a production environment for real-time inference.
B. Acquiring and exploring the dataset to assess quality, completeness, and potential biases.
C. Selecting the most appropriate machine learning algorithm for the prediction task.
D. Identifying and preparing feature engineering strategies to improve model accuracy.

Answer: B

NEW QUESTION # 31
You are tasked with designing a benchmark to compare the performance of different GPU- accelerated machine learning frameworks, such as TensorFlow, PyTorch, and RAPIDS.
Which of the following factors is the most critical to ensure a fair and meaningful comparison?

A. Using the latest software versions of each framework, regardless of compatibility with the GPU hardware.
B. Ensuring all frameworks use the same dataset, batch size, and GPU hardware during benchmarking.
C. Using different model architectures per framework to test a variety of scenarios and ensure a broad evaluation.
D. Allowing each framework to run with its default settings, as tuning hyperparameters would bias the results.

Answer: B

NEW QUESTION # 32
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