
Certificazione AWS Certified Data Analytics – Specialty
PANORAMICA

Esame AWS Certified Data Analytics – Specialty;
L’esame di certificazione AWS Certified Data Analytics – Specialty (DAS-C01) è pensato per valutare le competenze avanzate dei candidati nella progettazione, implementazione e gestione di soluzioni di analisi dei dati su AWS. L’esame copre tematiche come la raccolta, il processamento e l’analisi di grandi quantità di dati, utilizzando servizi AWS come Kinesis, S3, Redshift e EMR.
L’obiettivo principale è assicurare che i candidati dimostrino una solida conoscenza delle best practice e delle soluzioni avanzate AWS per l’analisi dei dati. Durante l’esame, i candidati affronteranno argomenti quali l’integrazione di servizi AWS e di terze parti per l’analisi dei dati, l’ottimizzazione delle prestazioni e l’implementazione di soluzioni di sicurezza.
Per conseguire la Certificazione AWS Certified Data Analytics – Specialty è necessario sostenere con successo il seguente esame:
AWS DAS-C01;

Corsi di Preparazione:
– 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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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:
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