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Certificazione AWS Certified Data Analytics – Specialty

Corsi e Certificazioni Amazon AWS - Amazon Web Service - AWS Certification - Formazione AWS - Cloud Practtioner - Solution Architect - DevOps Engineer - Developer - SysOps Administrator - Aws Machine Learning - AWS Security - AWS Database - AWS Data Analytics - AWS Specialty

Certificazione AWS Certified Data Analytics – Specialty

Panoramica | Svolgimento e Durata | Prerequisiti
Argomenti D’esame   |  Corsi di Preparazione

Panoramica   Svolgimento e Durata
Prerequisiti
Argomenti D’esame    Corsi di Preparazione

PANORAMICA

Certificazione AWS Certified Data Analytics – Specialty

Esame AWS Certified Data Analytics – Specialty;

 

The AWS Certified Data Analytics – Specialty (DAS-C01) exam is intended for individuals who perform a data analytics role. The exam validates a candidate’s comprehensive understanding of how to use AWS services to design, build, secure, and maintain analytics solutions that provide insight from data.

The exam also validates a candidate’s ability to complete the following tasks:

  • Define AWS data analytics services and understand how they integrate with each other
  • Explain how AWS data analytics services fit in the data lifecycle of collection, storage, processing,
    and visualization

Per conseguire la Certificazione AWS Certified Data Analytics – Specialty è necessario sostenere con successo il seguente esame:
AWS DAS-C01;

Corsi propedeutici alla certificazione

Corsi di Preparazione:

  • Building Data Lakes on AWS
  • Data Warehousing on AWS
  • Building Batch Data Analytics Solutions on AWS
  • Building Data Analytics Solutions Using Amazon Redshift
  • Exam Readiness: AWS Certified Data Analytics – Specialty

Conttaci ora per ricevere tutti i dettagli e per richiedere, senza alcun impegno, di parlare direttamente con uno dei nostri Docenti CLICCA QUI.
Oppure chiamaci subito al nostro numero verde  800-177596.

 SVOLGIMENTO E DURATA

Esame AWS Certified Data Analytics – Specialty Durata 180 minuti circa 65 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

Si consiglia la frequentazione dei seguenti corsi:

  • Building Data Lakes on AWS
  • Data Warehousing on AWS
  • Building Batch Data Analytics Solutions on AWS
  • Building Data Analytics Solutions Using Amazon Redshift
  • Exam Readiness: AWS Certified Data Analytics – Specialty

ARGOMENTI D’ESAME

Esame AWS Certified Data Analytics – Specialty – DAS-C01

Domain 1: Collection

  • Determine the operational characteristics of the collection system
  • Evaluate that the data loss is within tolerance limits in the event of failures
  • Evaluate costs associated with data acquisition, transfer, and provisioning from various sources into the collection system (e.g., networking, bandwidth, ETL/data migration costs)
  • Assess the failure scenarios that the collection system may undergo, and take remediation actions based on impact
  • Determine data persistence at various points of data capture
  • Identify the latency characteristics of the collection system
  • Select a collection system that handles the frequency, volume, and the source of data
  • Describe and characterize the volume and flow characteristics of incoming data (streaming, transactional, batch)
  • Match flow characteristics of data to potential solutions
  • Assess the tradeoffs between various ingestion services taking into account scalability, cost, fault tolerance, latency, etc.
  • Explain the throughput capability of a variety of different types of data collection and identify bottlenecks
  • Choose a collection solution that satisfies connectivity constraints of the source data system
  • Select a collection system that addresses the key properties of data, such as order, format, and compression
  • Describe how to capture data changes at the source
  • Discuss data structure and format, compression applied, and encryption requirements
  • Distinguish the impact of out-of-order delivery of data, duplicate delivery of data, and the tradeoffs between at-most-once, exactly-once, and at-least-once processing
  • Describe how to transform and filter data during the collection process

 

Domain 2: Storage and Data Management

  • Determine the operational characteristics of the storage solution for analytics
  • Determine the appropriate storage service(s) on the basis of cost vs. performance
  • Understand the durability, reliability, and latency characteristics of the storage solution based on requirements
  • Determine the requirements of a system for strong vs. eventual consistency of the storage system
  • Determine the appropriate storage solution to address data freshness requirements
  • Determine data access and retrieval patterns
  • Determine the appropriate storage solution based on update patterns (e.g., bulk, transactional, micro batching)
  • Determine the appropriate storage solution based on access patterns (e.g., sequential vs. random access, continuous usage vs.ad hoc)
  • Determine the appropriate storage solution to address change characteristics of data (appendonly changes vs. updates)
  • Determine the appropriate storage solution for long-term storage vs. transient storage
  • Determine the appropriate storage solution for structured vs. semi-structured data
  • Determine the appropriate storage solution to address query latency requirements
  • Select appropriate data layout, schema, structure, and format
  • Determine appropriate mechanisms to address schema evolution requirements
  • Select the storage format for the task
  • Select the compression/encoding strategies for the chosen storage format
  • Select the data sorting and distribution strategies and the storage layout for efficient data access
  • Explain the cost and performance implications of different data distributions, layouts, and formats (e.g., size and number of files)
  • Implement data formatting and partitioning schemes for data-optimized analysis
  • Define data lifecycle based on usage patterns and business requirements
  • Determine the strategy to address data lifecycle requirements
  • Apply the lifecycle and data retention policies to different storage solutions
  • Determine the appropriate system for cataloging data and managing metadata
  • Evaluate mechanisms for discovery of new and updated data sources
  • Evaluate mechanisms for creating and updating data catalogs and metadata
  • Explain mechanisms for searching and retrieving data catalogs and metadata
  • Explain mechanisms for tagging and classifying data

 

Domain 3: Processing

  • Determine appropriate data processing solution requirements
  • Understand data preparation and usage requirements
  • Understand different types of data sources and targets
  • Evaluate performance and orchestration needs
  • Evaluate appropriate services for cost, scalability, and availability
  • Design a solution for transforming and preparing data for analysis
  • Apply appropriate ETL/ELT techniques for batch and real-time workloads
  • Implement failover, scaling, and replication mechanisms
  • Implement techniques to address concurrency needs
  • Implement techniques to improve cost-optimization efficiencies
  • Apply orchestration workflows
  • Aggregate and enrich data for downstream consumption
  • Automate and operationalize data processing solutions
  • Implement automated techniques for repeatable workflows
  • Apply methods to identify and recover from processing failures
  • Deploy logging and monitoring solutions to enable auditing and traceability

 

Domain 4: Analysis and Visualization

  • Determine the operational characteristics of the analysis and visualization solution
  • Determine costs associated with analysis and visualization
  • Determine scalability associated with analysis
  • Determine failover recovery and fault tolerance within the RPO/RTO
  • Determine the availability characteristics of an analysis tool
  • Evaluate dynamic, interactive, and static presentations of data
  • Translate performance requirements to an appropriate visualization approach (pre-compute and consume static data vs. consume dynamic data)
  • Select the appropriate data analysis solution for a given scenario
  • Evaluate and compare analysis solutions
  • Select the right type of analysis based on the customer use case (streaming, interactive, collaborative, operational)
  • Select the appropriate data visualization solution for a given scenario
  • Evaluate output capabilities for a given analysis solution (metrics, KPIs, tabular, API)
  • Choose the appropriate method for data delivery (e.g., web, mobile, email, collaborative notebooks)
  • Choose and define the appropriate data refresh schedule
  • Choose appropriate tools for different data freshness requirements (e.g., Amazon Elasticsearch
  • Service vs. Amazon QuickSight vs. Amazon EMR notebooks)
  • Understand the capabilities of visualization tools for interactive use cases (e.g., drill down, drill through and pivot)
  • Implement the appropriate data access mechanism (e.g., in memory vs. direct access)
  • Implement an integrated solution from multiple heterogeneous data sources

 

Domain 5: Security

  • Select appropriate authentication and authorization mechanisms
  • Implement appropriate authentication methods (e.g., federated access, SSO, IAM)
  • Implement appropriate authorization methods (e.g., policies, ACL, table/column level permissions)
  • Implement appropriate access control mechanisms (e.g., security groups, role-based control)
  • Apply data protection and encryption techniques
  • Determine data encryption and masking needs
  • Apply different encryption approaches (server-side encryption, client-side encryption, AWS
  • KMS, AWS CloudHSM)
  • Implement at-rest and in-transit encryption mechanisms
  • Implement data obfuscation and masking techniques
  • Apply basic principles of key rotation and secrets management
  • Apply data governance and compliance controls
  • Determine data governance and compliance requirements
  • Understand and configure access and audit logging across data analytics services
  • Implement appropriate controls to meet compliance requirements

 CORSI DI PREPARAZIONE

  • Building Data Lakes on AWS
  • Data Warehousing on AWS
  • Building Batch Data Analytics Solutions on AWS
  • Building Data Analytics Solutions Using Amazon Redshift
  • Exam Readiness: AWS Certified Data Analytics – Specialty
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