Valid NVIDIA NCP-ADS exam pdf & NCP-ADS practice exam & NCP-ADS braindumps2go dumps

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q49-Q54):

NEW QUESTION # 49
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. Implement a GPU-accelerated spectral residual anomaly detection model using NVIDIA Merlin for rapid feature selection.
C. Use RAPIDS cuML's GPU-accelerated DBSCAN to cluster the sensor readings and classify outliers as anomalies.
D. Deploy a TensorRT-optimized Transformer model to process the time-series data in parallel for real- time anomaly detection.

Answer: A

NEW QUESTION # 50
You are using cuGraph to run the PageRank algorithm on a directed web graph. The dataset is large, and you want to ensure an accurate and efficient computation while optimizing GPU performance.
Which of the following configurations is the best approach for running PageRank in cuGraph?

A. Use the cugraph.pagerank() function with a damping factor of 0 and 10 iterations
B. Load the graph into NetworkX first, compute PageRank, and then convert the results back into cuGraph format
C. Run cugraph.pagerank() with a damping factor of 0.85 and set the max iterations to 100 with a convergence threshold
D. Convert the graph into an adjacency matrix and perform matrix multiplication iteratively for convergence

Answer: C

NEW QUESTION # 51
A data scientist is using NVIDIA RAPIDS cuDF to process a large dataset of customer transactions.
The dataset contains numerical, categorical, and timestamp-based features.
To optimize memory usage and performance on NVIDIA GPUs, which approach should they take when selecting data types?

A. Avoid downcasting integer columns, as lower-bit integer types (e.g., int8) are not supported in GPU- accelerated computations.
B. Store all numerical columns as float64 to preserve maximum precision, even if lower precision suffices.
C. Convert categorical variables into cuDF categorical data types and downcast numerical columns to the smallest possible precision without losing information.
D. Convert all timestamp features into object (string) format to maintain readability and ensure compatibility with GPU processing.

Answer: C

NEW QUESTION # 52
You are processing a multi-terabyte dataset in CuDF and want to optimize query performance and storage efficiency.
Which approach should you follow to ensure that the dataset remains efficiently partitioned and easily accessible?

A. Store the entire dataset in a single large file to minimize the number of files in the directory.
B. Split the dataset into multiple files based on a logical partitioning key, such as a date column.
C. Use pandas for large-scale processing instead of CuDF since pandas is more stable for big data processing.
D. Convert all numeric columns to float64 for higher precision, even if float32 is sufficient.

Answer: B

NEW QUESTION # 53
You have a massive time-series dataset containing millions of records per day, and you need to perform forecasting at scale.
Which of the following techniques best utilizes NVIDIA technologies to optimize time-series forecasting?

A. Run Facebook Prophet on a multi-core CPU environment to leverage parallel forecasting.
B. Use RAPIDS cuML's XGBoost-GPU implementation to forecast time-series patterns with improved training efficiency.
C. Use RAPIDS cuDF and Dask-cuDF to preprocess the data and train an LSTM model with PyTorch Lightning on multiple GPUs.
D. Convert time-series data to tensors and train a deep learning model using PyTorch with mixed precision on an NVIDIA GPU.

Answer: C

NEW QUESTION # 54
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