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Test Number : C2090-312
Test Name : IBM DB2 11 DBA for z/OS
Vendor Name : IBM
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C2090-312 test Format | C2090-312 Course Contents | C2090-312 Course Outline | C2090-312 test Syllabus | C2090-312 test Objectives

Exam Title : IBM Certified Database Administrator - DB2 11 DBA for z/OS
Exam ID : C2090-312
Exam Duration : 90 mins
Questions in test : 67
Passing Score : 59%
Official Training : DB2 11 for zOS Implementation Workshop
Exam Center : Pearson VUE
Real Questions : IBM DB2 DBA for z/OS Real Questions
VCE VCE exam : IBM C2090-312 Certification VCE Practice Test

Database Design and Implementation
- Design tables and views (columns, data type considerations for large objects, XML, column sequences, user-defined data types, temp tables, clone tables, temporal tables, MQTs, new archive transparency, etc.)
- Explain the different performance implications of identity column, row ID, and sequence column definitions (applications, utilities), hash access
- Design indexes (key structures, type of index, index page structure, index column order, index space, clustering, compression, index on expression, include column)
- Design table spaces (choose a DB2 page size, clustering) and determine space attributes
- Perform partitioning (table partitioning, index partitioning, DPSI, universal table space)
- Normalize data (E-R model, process model) and translate data model into physical model (denormalize tables)
- Implement user-defined integrity rules (referential integrity, user-defined functions & data types, check constraints, triggers)
- Use the appropriate method to alter DB2 objects (table, column, drop column, alter limit key, index, table space, database, online schema)
- Understand impacts of different encoding schemes 24%
Operation and Recovery
- Knowledge of commands for normal operational conditions (START, STOP, DISPLAY)
- Knowledge of commands and utility control statements for use in abnormal conditions (RECOVER, RESTART)
- Load and unload data into and from the created tables
- Reorganize objects when necessary (reorg avoidance, automatic mapping table, new reorg features)
- Monitor the object by collecting statistics (run stats, improved in-line statistics, real time stats, autonomic stats, and statistics related stored procedures)
- Monitor and manage threads and utilities (distributed, local, MODIFY DDF)
- Identify and respond to advisory/restrictive statuses on objects
- Identify and perform problem determination (traces and other utilities, plans and packages)
- Perform health checks (check utilities, offline utilities, catalog queries)
- Identify and perform actions that are needed to protect databases from planned and unplanned outages (tables spaces; indexes; full pack; hardware; Flash copies; full, incremental, reference update; copy-to-copy, non-data objects; catalog) and recovery scenarios (off-site recovery, data sharing, table spaces, indexes, roll forward, roll back, current point in time, prior point in time, system point in time copy and restore, catalog and directory, offline utilities (DSN1), new Extended RBA and LRSN) 22%
Security and Auditing
- Understanding privileges and authorities
- Protect access to DB2 and its objects
- Audit DB2 activity and resources and identify primary audit techniques
- Identify and respond appropriately to symptoms from trace output or error messages that signify security problems 6%
Performance
- Plan for performance monitoring by setting up and running monitoring procedures (continuous, detailed, periodic, exception)
- Analyze performance (manage and tune CPU requirements, memory, I/O, locks, response time, index and table compression)
- Analyze and respond to RUNSTATS statistics analysis (real-time, batch, catalog queries, reports, histograms)
- Determine when and how to perform REBIND (APCOMPARE and APREUSE)
- Describe DB2 interaction with WLM (distributed, stored procedures, user-defined functions, RRS)
- Interpret traces (statistics, accounting, performance) & explain the performance impact of different DB2 traces
- Identify and respond to critical performance metrics (excessive I/O wait times, lock-latch waits and CPU waits; deadlocks, timeouts, RID failures)
- Review and tune SQL (access paths, EXPLAIN tables, awareness of query transformation and predicate processing, use of Virtual Indexes)
- Dynamic SQL Performance (DSN_STATEMENT_CACHE_TABLE, parameter markers, literal replacement, REOPT)
- Design features for performance (hash row access, inline LOBs)
- Knowledge of controlling access paths (SYSSTATSFEEDBACK table, SYSQUERY) 22%
Installation and Migration / Upgrade
- Knowledge and understanding of the critical ZPARMs (database-, object- and application-oriented, application compatibility - no DDF)
- Identify and explain Datasharing components and commands
- Knowledge of pre-migration checklists
- Knowledge of catalog and directory (new tables, change tables, new objects) 7%
Additional Database Functionality
- Knowledge of SQL constructs (temporal, archive, table functions, built-in scalar functions, recursive, common table expresssions)
- Knowledge of SQL/PL (Array data type, new array data type functions, functions and procedures)
- Knowledge of SQL/XML (results database, XML functions, cross loader with XML, xpath expressions, FLWOR, pattern matching and regular expressions)
- Knowledge of Stored Procedures (native, external, autonomous, ziip considerations)
- Knowledge of User-defined functions (scalar functions, table functions, SQL/external functions)
- Knowledge of global variables (in stored procedures, in SQL/PL, distributed considerations) 10%
Distributed Access
- Implementing distributed data access (communications database)
- Knowledge of ZPARMs (for DDF)
- Knowledge of DDF setup (DB2 Connect, Client, Drivers, profile tables, RLMT)
- Understanding and implementing distributed data access (perf settings for DDF access) 7%

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IBM Question Bank

knowledgeable panel embraces DataOps as key initiative for accumulating, analyzing essential suggestions | C2090-312 dumps questions and test Questions

It’s unusual for surveys to record that 100% of respondents were all following the identical course. Yet when 300 businesses were lately requested by way of 451 analysis LLC about converting records into enterprise perception or DataOps, all 300 had been both currently planning or actively pursuing DataOps initiatives.

The results are extra affirmation that DataOps is a exact priority within the enterprise and the need for extra agile and automatic statistics administration has certainly not been superior.

“There’s redundant, disparate data in all places,” observed Victoria Stasiewicz (pictured, middle), records engineer at the Northwestern Mutual life coverage Co. “I should inform you the place the data is, inform you what’s trusted, and that means that you could instantly access the suggestions and bring back solutions to company questions it truly is one reply no longer many. That’s the greatest challenge.”

Stasiewicz spoke with Dave Vellante, host of theCUBE, SiliconANGLE Media’s livestreaming studio, during the IBM DataOps in motion adventure. She become joined by using Caitlin Halferty (left), director of AI Accelerator and client success at IBM Corp., and Steven Lueck (correct), senior vice chairman and director of information administration at associated bank. They discussed IBM’s synthetic intelligence-driven DataOps initiatives, a altering corporate culture attitude round accessing information, the significance of pace to perception, and overcoming inertia to obtain business scale. (* Disclosure beneath.)

cost of infusing AI

One company that has developed a DataOps apply and built-in its principles into product choices is IBM. The enterprise’s international Chief statistics office is empowered to construct a depended on facts supply and synthetic-intelligence framework during the corporation.

“We’ve been on this event for roughly four years now,” Halferty pointed out. “on the time they wrote their facts method, it become in regards to the cognitive business. They promptly have pivoted to look the genuine opportunity and value of infusing AI across all of their fundamental workflows.”

IBM pursued its DataOps approach by means of essentially serving as its own guinea pig, constructing equipment for metadata curation while gathering remarks from personnel. This event led IBM to recognize that once statistics managers have been pulling in structured and unstructured records, there became a superb deal of labor worried to catalog it safely.

“Our records stewards were spending a lot of time manually tagging and creating company metadata about that information,” Halferty spoke of. “We identified that it was a real pain element and costing us a lot of money and effective components. So, they began to automate the metadata and they pushed that means out into Cloud Pak for facts.”

Cloud Pak for records has emerged over the last two years as a platform designed to modernize how organizations assemble and analyze guidance. The enterprise these days up to date the tool with new DataOps capabilities for automatic planning, budgeting and forecasting.

“Cloud Pak for statistics is truly a one-stop shop,” Stasiewicz stated. “That’s enabling short, seamless entry for a business consumer versus having them go into one of the previous models that IBM had rolled out.”

Operationalizing with pace and scale

entry to the appropriate equipment is critical, but information administration leaders are also discovering that DataOps is set developing a brand new cultural approach as smartly. linked bank discovered that as soon as the statistics has been virtualized or saved in an information lake, users may start to get their fingers around key information for constructing applications without embarking on a search mission to find it.

“That’s been the largest way of life shift for us, having that availability of statistics to supply insights as adversarial to having the company or application homeowners ask for that data,” Lueck defined. “What we’re discovering is that through offering the platform and the foundational layers, we’re getting the use cases to conform. Let’s start to discover. That’s a data science mentality and culture.”

yet another key effect of DataOps is velocity. For analysts to be informed it could be months before crucial datasets are available is not conducive to the notion of an agile enterprise.

Operationalizing information into a framework where it can be abruptly accessed in a usable structure highlights a key driver at the back of the DataOps movement as revealed in one survey: pace of innovation.

“long past are the times when scientists are inquiring for connections to all of these distinct data sources,” Stasiewicz referred to. “they can go into a catalog without needing to request access to the rest, and inside five minutes they could see the buildings. That’s DataOps. It’s allowing us to pace up all of that advice, taking stuff that took months all the way down to weeks and down to hours.”

it would be hard to argue that speed to insight isn't whatever preferred by company nowadays. This can also smartly clarify why surveys are showing across-the-board pastime in DataOps initiatives. The most effective factor that may be holding organizations returned is inertia, an inability to get moving down the DataOps path. For this, practitioners reminiscent of Lueck have standard suggestions.

“everything should be focused on scale,” Lueck pointed out. “start to display that development and price, the use situations will come. Don’t try to over believe it or over plan it. Get started.”

here’s the finished video interview, part of SiliconANGLE’s and theCUBE’s coverage of the IBM DataOps in motion adventure. (* Disclosure: TheCUBE is a paid media accomplice for the IBM DataOps in action adventure. Neither IBM, the sponsor for theCUBE’s adventure coverage, nor different sponsors have editorial control over content on theCUBE or SiliconANGLE.)

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