Latest NCP-ADS Exam Vce, Valid Braindumps NCP-ADS Free

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NVIDIA NCP-ADS Exam Syllabus Topics:

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

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NVIDIA-Certified-Professional Accelerated Data Science Sample Questions (Q154-Q159):

NEW QUESTION # 154
You are designing an accelerated ETL pipeline to process large-scale datasets in a data science workflow.
Which of the following are key considerations when selecting the right tools and methods for implementing this pipeline? (Select two)

A. Leveraging parallel processing and distributed computing frameworks like Apache Spark to speed up the transformation phase.
B. Using a single storage location for both raw and transformed data to simplify the workflow.
C. Using GPU-accelerated libraries such as RAPIDS for data transformation to enhance processing speed.
D. Ensuring the ETL pipeline uses only batch processing for data ingestion.
E. Relying on traditional single-threaded processing for the extraction phase to reduce complexity.

Answer: A,C

NEW QUESTION # 155
You are tasked with acquiring a dataset for training a machine learning model in healthcare, predicting patient readmission rates. Before using the dataset, you must assess its quality.
Which of the following is the most important factor to evaluate before acquisition?

A. Whether the dataset is stored on a cloud server or a local machine
B. The number of missing values and inconsistencies in key columns
C. The programming language used to preprocess the dataset
D. The file format (CSV, JSON, or XML) of the dataset

Answer: B

NEW QUESTION # 156
You are analyzing a time-series dataset that represents temperature readings from an industrial sensor. Your goal is to detect anomalies that may indicate sensor failures or environmental changes.
Which of the following methods would be the most appropriate statistical technique for detecting anomalies in this time-series dataset?

A. Z-score (Standard Score) Analysis
B. Principal Component Analysis (PCA)
C. One-Hot Encoding
D. Moving Average Smoothing

Answer: A

NEW QUESTION # 157
Which of the following methods are commonly used to handle missing data in data analysis? (Select two)

A. Ignoring missing data entirely
B. Removing rows with missing data
C. Imputing with the mean or median of the feature
D. Using the mode to impute missing values
E. Using a fixed constant value for imputation

Answer: B,C

NEW QUESTION # 158
You are a data scientist working on a large-scale deep learning project. Your team needs to train a deep neural network (DNN) on terabytes of image data using NVIDIA GPUs in the cloud.
Which of the following approaches will maximize GPU utilization and optimize training time in an NVIDIA- accelerated cloud environment?

A. Use NVIDIA NGC containers with TensorFlow or PyTorch and enable NCCL for multi-GPU communication.
B. Select low-cost, consumer-grade GPUs available in the cloud marketplace to reduce costs.
C. Avoid using mixed precision training and rely solely on full-precision (FP32) computations for numerical stability.
D. Use Jupyter Notebooks on a local machine with minimal GPU resources and transfer model checkpoints manually to the cloud for training.

Answer: A

NEW QUESTION # 159
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