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best practice Tests for IBM Big Data Engineer Certification 2021

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Description

About IBM Big Data Engineer

This certification is intended for IBM Big Data Engineers. The Big Data Engineer works directly with the Data Architect and hands-on Developers to convert the architect's Big Data vision and blueprint into a Big Data reality. The Data Engineer possesses a deep level of technical knowledge and experience across a wide array of products and technologies.


Prerequisite for the exam

• Understand the data layer and particular areas of potential challenge/risk in the data layer

• Ability to translate functional requirements into technical specifications.

• Ability to take overall solution/logical architecture and provide physical architecture.

• Understand Cluster Management

• Understand Network Requirements

• Understand Important interfaces

• Understand Data Modeling

• Ability to identify/support non-functional requirements for the solution

• Understand Latency

• Understand Scalability

• Understand High Availability

• Understand Data Replication and Synchronization

• Understand Disaster Recovery

• Understand Overall performance (Query Performance, Workload Management, Database Tuning)

• Propose recommended and/or best practices regarding the movement, manipulation, and storage of data in a big data solution (including, but not limited to:

• Understand Data ingestion technical options

• Understand Data storage options and ramifications (for example , understand the additional requirements and challenges introduced by data in the cloud)

• Understand Data querying techniques & availability to support analytics

• Understand Data lineage and data governance

• Understand Data variety (social, machine data) and data volume

• Understand/Implement and provide guidance around data security to support implementation, including but not limited to:

  • Understand LDAP Security

  • Understand User Roles/Security

  • Understand Data Monitoring

  • Understand Personally Identifiable Information (PII) Data Security considerations


Course Outline

1. Data Loading


• Load unstructured data into InfoSphere BigInsights

• Import streaming data into Hadoop using InfoSphere Streams

• Create a BigSheets workbook

• Import data into Hadoop and create Big SQL table definitions

• Import data to HBase

• Import data to Hive

• Use Data Click to load from relational sources into InfoSphere BigInsights with a self-service process

• Extract data from a relational source using Sqoop

• Load log data into Hadoop using Flume

• Insert data via IBM General Parallel File System (GPFS) Posix file system API

• Load data with Hadoop command line utility


2. Data Security


• Keep data secure within PCI standards

• Uses masking (e.g. Optim, Big SQL), and redaction to protect sensitive data


3. Architecture and Integration


• Implement MapReduce

• Evaluate use cases for selecting Hive, Big SQL, or HBase

• Create and/or query a Solr index

• Evaluate use cases for selecting potential file formats (e.g. JSON, CSV, Parquet, Sequence, etc..)

• Utilize Apache Hue for search visualization


4. Performance and Scalability


• Use Resilient Distributed Dataset (RDD) to improve MapReduce performance

• Choose file formats to optimize performance of Big SQL, JAQL, etc.

• Make specific performance tuning decisions for Hive and HBase

• Analyze performance considerations when using Apache Spark


5. Data Preparation, Transformation, and Export


• Use Jaql query methods to transform data in InfoSphere BigInsights

• Capture and prep social data for analytics

• Integrating SPSS model scoring in InfoSphere Streams

• Implement entity resolution within a Big Data platform (e.g. Big Match)

• Utilize Pig for data transformation and data manipulation

• Use Big SQL to transform data in InfoSphere BigInsights

• Export processing results out of Hadoop (e.g. DataClick, DataStage, etc.)

• Utilize consistent regions in InfoSphere Streams to ensure at least once processing

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