Certificazione Azure Data Scientist Associate
PANORAMICA
PANORAMICA
Esame DP-100 Azure Data Scientist Associate;
L’innovativa certificazione Azure Data Scientist Associate fa parte della area di competenza Microsoft sul DATA MANAGEMENT & ANALYTICS. Chi consegue questo titolo certifica la propria competenza nella posizione professionale di Data Scientist e Data Analyst, dimostrando la capacità di utilizzare i potenti strumenti Azure Machine Learning (AML) Service, applicando le tecnologie di Machine Learning per analizzare e processare i Big Data.
Per conseguire la Certificazione Azure Data Scientist Associate è necessario sostenere con successo il seguente esame:
Esame DP-100 Azure Data Scientist Associate;
CORSI PROPEDEUTICI
Affrontare un esame di certificazione richiede una preparazione specifica.
Per questa certificazione consigliamo:
SVOLGIMENTO E DURATA
SVOLGIMENTO E DURATAEsame DP-100 Durata 120 minuti 40-60 quesiti;
Negli esami sono presenti quesiti formulati in lingua inglese in forme differenti: Risposta Multipla; completamento di testo, collegamenti concettuali Drag and Drop; vere e proprie simulazioni laboratoriali.
PREREQUISITI
Conoscenza Basilare piattaforma Cloud Microsoft Azure.
ARGOMENTI D’ESAME
ARGOMENTI D’ESAMEEsame DP-100 Azure Data Scientist Associate;
- Define and prepare the development environment
- Select development environment
- assess the deployment environment constraints
- analyze and recommend tools that meet system requirements
- select the development environment
- Set up development environment
- create an Azure data science environment
- configure data science work environments
- Quantify the business problem
- define technical success metrics
- quantify risks
- Prepare data for modeling
- Transform data into usable datasets
- develop data structures
- design a data sampling strategy
- design the data preparation flow
- Perform Exploratory Data Analysis (EDA)
- review visual analytics data to discover patterns and determine next steps
- identify anomalies, outliers, and other data inconsistencies
- create descriptive statistics for a dataset
- Cleanse and transform data
- resolve anomalies, outliers, and other data inconsistencies
- standardize data formats
- set the granularity for data
- Perform feature engineering
- Perform feature extraction
- perform feature extraction algorithms on numerical data
- perform feature extraction algorithms on non-numerical data
- scale features
- Perform feature selection
- define the optimality criteria
- apply feature selection algorithms
- Develop models
- Select an algorithmic approach
- determine appropriate performance metrics
- implement appropriate algorithms
- consider data preparation steps that are specific to the selected algorithms
- Split datasets
- determine ideal split based on the nature of the data
- determine number of splits
- determine relative size of splits
- ensure splits are balanced
- Identify data imbalances
- resample a dataset to impose balance
- adjust performance metric to resolve imbalances
- implement penalization
- Train the model
- select early stopping criteria
- tune hyper-parameters
- Evaluate model performance
- score models against evaluation metrics
- implement cross-validation
- identify and address overfitting
- identify root cause of performance results
CORSI PROPEDEUTICI
CONTATTACI
