NVIDIA - NCP-ADS - Fantastic NVIDIA-Certified-Professional Accelerated Data Science Reliable Exam Pattern

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q16-Q21):

NEW QUESTION # 16
A data science team wants to leverage GPU acceleration for detecting anomalies in a massive IoT sensor dataset that is continuously streaming.
Which of the following NVIDIA-supported methods would be the best for handling real-time anomaly detection in a high-throughput environment?

A. Utilize NVIDIA Morpheus with deep learning-based anomaly detection to process high-throughput log data efficiently.
B. Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.
C. Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.
D. Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.

Answer: A

NEW QUESTION # 17
A data scientist is preparing a dataset containing numerical features with varying scales and distributions. Some features range from 0 to 1, while others have values ranging from -5000 to 5000. The dataset will be used in a machine learning model that relies on gradient-based optimization.
What is the best approach to standardizing the data to ensure uniformity across features?

A. Use min-max scaling to transform all features into the range [0,1]
B. Apply z-score normalization (standardization) to scale features to have a mean of 0 and a standard deviation of 1
C. Normalize the dataset by dividing each feature by its maximum absolute value
D. Keep the features as they are since machine learning models can handle varying scales naturally

Answer: B

NEW QUESTION # 18
A data scientist is working on a machine learning model for fraud detection. Due to the limited size of the dataset, they decide to generate synthetic data using NVIDIA RAPIDS AI and cuDF.
Which of the following approaches is the most efficient and effective for generating synthetic data while ensuring compatibility with RAPIDS AI workflows?

A. Use numpy and pandas to generate synthetic data, then convert the DataFrame to cuDF before using it in RAPIDS AI workflows.
B. Train a generative adversarial network (GAN) using PyTorch and then use the generated samples in RAPIDS AI without any additional processing.
C. Use the RAPIDS cuML library to directly generate synthetic tabular data with controlled statistical properties.
D. Use cuDF DataFrame operations to create new synthetic samples by applying random transformations (e.g., noise injection, permutation) to the existing dataset.

Answer: D

NEW QUESTION # 19
When scaling data parallelism using Dask with multiple Nvidia GPUs, what is the key consideration to avoid memory issues when distributing large datasets?

A. Allow Dask to allocate data chunks dynamically without partitioning the dataset first, letting the system handle memory distribution automatically.
B. Ensure that each GPU's memory usage is manually monitored and adjusted, as Dask does not manage memory allocation automatically across GPUs.
C. Split the dataset into smaller partitions that fit into each GPU's memory to prevent out-of-memory errors, and let Dask manage data distribution.
D. Use dask_gpu instead of dask_cuda to manage memory automatically across GPUs.

Answer: C

NEW QUESTION # 20
Which of the following steps is the first in the CRISP-DM (Cross-Industry Standard Process for Data Mining) process when using NVIDIA technologies?

A. Data Preparation
B. Model Building
C. Data Understanding
D. Business Understanding

Answer: D

NEW QUESTION # 21
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