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000-998 exam Dumps Source : WebSphere Message Broker V6.0, System Admin

Test Code : 000-998
Test Name : WebSphere Message Broker V6.0, System Admin
Vendor Name : IBM
Q&A : 102 Real Questions

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IBM WebSphere Message Broker V6.0,

Correlsense broadcasts SharePath for IBM WebSphere Message broking service, Improves service-stage administration, Error Identification and Bottleneck Isolation | killexams.com Real Questions and Pass4sure dumps

FRAMINGHAM, MA--(Marketwired - Jun 11, 2013) - Correlsense, a number one IT monitoring application company, today brought SharePath for IBM® WebSphere Message broker (WMB). This latest enhancement to SharePath 3.0, the enterprise's business-class APM solution for managing complex interconnected functions, is premiere for any company counting on IBM middleware for its transactional backbone.

As purposes develop into more and more linked, organizations are employing business provider Buses (ESBs) to tackle the communications between disparate functions and functions. using an ESB, despite the fact, introduces new management challenges. They are sometimes decoupled from the core software and execute company good judgment and code which adds complexity whereas increasing the time to detect and isolate efficiency issues. in contrast to different middleware management options that focal point on infrastructure monitoring, SharePath for IBM WebSphere Message broking service offers more granular visibility into the complete circulation of individual message flows into, inside and out of the ESB. This gives indisputable proof about where efficiency problems and errors ensue, no matter if within message broker, during which node, or in the backend capabilities or purposes.

SharePath follows every message circulation via its nodes and external capabilities, and logs all this specified tips in a scalable huge data repository, including parameters, values and exception mistakes of each execution. apart from wide constructed-in logging, auditing, and tracking of all messages, SharePath provides trending and alter analysis to tune performance over time.

SharePath for WebSphere Message broking service offers middleware owners, IT operations and construction assist teams the capacity to without delay remember dependencies and establish middleware bottlenecks. Most principally, SharePath for IBM WebSphere Message broking service:

  • allows IT operations groups to effectively locate "misplaced messages" by way of any search criteria through a simple full-textual content search.
  • gives the most correct bottleneck detection on the market today by way of monitoring and logging every single message stream across its execution, including usage of exterior features and supplies, and shows no matter if bottlenecks are inside WMB or outdoor of it.
  • instantly detects all message flows and their exterior dependencies to give an always up to date dependency mapping. No manual configuration is required when message flows are updated or new message flows are deployed.
  • "SharePath for IBM WebSphere Message broking service empowers organizations to profit control over a critical enterprise part," pointed out Nir Livni, VP of products. "groups the usage of SharePath now not must build wide, costly and error-susceptible logging and tracking mechanisms to understand what happened to a selected message and no matter if it succeeded or failed."

    SharePath for IBM WebSphere Message broker is purchasable immediately as a standalone providing or as a part of a complete end-to-conclusion APM solution according to SharePath 3.0. extra counsel can also be found at http://www.correlsense.com/options/company-want/sharepath-for-ibm-websphere-message-broking service/

    About Correlsense Correlsense develops software performance management and IT monitoring software. it's the APM made from choice for enterprise and IT operations managers that count on complex and significant business purposes. Correlsense paints a complete and dynamic photo of IT provider stages and efficiency for functions that span cellular, SaaS, cloud, data core and legacy mainframes. Correlsense became founded in 2005, is privately held and SharePath clients encompass one of the crucial world's greatest economic, telecom and retail corporations. For greater information, consult with www.correlsense.com.


    IBM App join XML records XML external Entity [CVE-2018-1801] | killexams.com Real Questions and Pass4sure dumps

    A vulnerability was found in IBM App connect, Integration Bus and WebSphere Message broking service (application Server application) and categorised as essential. This situation affects a part of the part XML records Handler. The manipulation with an unknown enter results in a privilege escalation vulnerability (XXE). the use of CWE to declare the difficulty ends up in CWE-611. Impacted is confidentiality, integrity, and availability.

    The weakness become launched 02/04/2019. it is possible to study the advisory at trade.xforce.ibmcloud.com. The identification of this vulnerability is CVE-2018-1801 when you consider that 12/13/2017. The attack can be initiated remotely. The technical details are unknown and an make the most is not publicly available. The pricing for an exploit might possibly be round USD $25k-$100k at the moment (estimation calculated on 02/05/2019).

    There is no information about viable countermeasures commonly used. It may well be advised to change the affected object with an choice product.

    CPE CVSSv3 VulDB Meta Base rating: 6.3VulDB Meta Temp rating: 6.three

    VulDB Base rating: ≈6.3VulDB Temp score: ≈6.3VulDB Vector: 🔒VulDB Reliability: 🔍

    CVSSv2 VulDB Base rating: 🔒VulDB Temp rating: 🔒VulDB Reliability: 🔍Exploiting class: Privilege escalation / XXE (CWE-611)local: NoRemote: sure

    Availability: 🔒

    fee Prediction: 🔍current price Estimation: 🔒

    probability Intelligence possibility: 🔍Adversaries: 🔍Geopolitics: 🔍economic climate: 🔍Predictions: 🔍movements: 🔍 Countermeasures advised: no mitigation known0-Day Time: 🔒 Timeline 12/13/2017 CVE assigned02/04/2019 Advisory disclosed02/05/2019 VulDB entry created02/05/2019 VulDB closing update assetsAdvisory: alternate.xforce.ibmcloud.comConfirmation: 🔒

    CVE: CVE-2018-1801 (🔒)

    entryCreated: 02/05/2019Complete: 🔍

    Lien vers l'article supply


    Compuware declares APM business’s Broadest assist for IBM WebSphere Environments With dynaTrace four.1 | killexams.com Real Questions and Pass4sure dumps

    Compuware organisation (Nasdaq:CPWR), the technology performance enterprise, today introduced the release of Compuware dynaTrace 4.1, the trade’s first software efficiency administration (APM) solution to deliver full assist for IBM WebSphere Message broking service. dynaTrace four.1 also adds to its powerful person event management (UEM) capabilities, enhances visualization and integrates with Compuware Gomez real-person Monitoring – facts center.

    This new unlock furthers Compuware’s leadership in the APM industry and extends its capability to carry exceptional company agility, charge optimization and aggressive benefits to the marketplace.

    dynaTrace 4.1 offers the business’s broadest support of IBM WebSphere and brings unique, cutting-edge APM to IBM WebSphere environments together with: WebSphere Message broking service, software Servers and Portal Servers. dynaTrace superior end-to-end transaction tracing and UEM combine with sophisticated visibility to deliver unparalleled building, verify and operational insight into WebSphere infrastructures.“valued clientele across industries and worldwide count on the IBM WebSphere stack and WebSphere Message broking service to habits tens of millions important business transactions a day, and now they delight in extraordinary visibility into those transactions from the world’s leading APM solution,” observed John Van Siclen, commonplace manager of Compuware’s APM company unit. “The culmination is enormously more desirable time-to-cost and faster ROI for WebSphere deployments and updates. development, check and production expenses are all slashed by way of cutting back the time it takes to create, debug and finalize production releases.”

    “finding software efficiency problems inside complicated multi-tier internet purposes can be difficult, even for the most experienced,” observed Thomas Murphy, analysis director at Gartner. “modern purposes frequently make use of feature prosperous consumers running on pcs and/or devices related to features via message oriented middleware. whereas this provides a superb deal of flexibility for creating information trade between purposes it could actually also result in a great deal of complexity, making it complicated to establish and diagnose the basis explanation for application efficiency considerations, principally in precise-time.”dynaTrace four.1 provides considerable company value to anyone using the WebSphere stack or any half thereof, together with WebSphere Message broking service clients. With dynaTrace 4.1, WebSphere users can verify in precise time if messages are flowing as it should be or if messages have not been competently got through the meant recipient functions and no matter if Message broking service is properly configured to maximise service effectivity. additionally, with the aid of decreasing the amount of time spent separating and repairing issues, dynaTrace slashes imply-Time-To-restore (MTTR) for all points of WebSphere implementations.

    This latest version of dynaTrace additionally brings clients multiplied company cost via:• more desirable visibility into third-party web page content material with images and graphs to display performance facts regarding content material served through content material birth networks (CDNs) and advert-servers. This feature without difficulty auto-configures as part of the dynaTrace dashboard, which conveys the genuine efficiency impact of third-birthday party content on utility responsiveness and sooner or later, the user journey.• Browser diagnostics visualized gives actual screenshots of what conclusion-clients see and presents unparalleled details about browser-based software efficiency. dynaTrace now takes periodic screenshots of the browser window to show what occurs when and in what sequence the browser renders the locations of internet pages. This actual visibility from the consumer point of view identifies browser issues that can not be viewed from the statistics core, speeds the resolution of complications, and improves customer experience and delight. moreover, enhanced export capabilities and timer-based mostly efficiency numbers make Browser Diagnostics a repeatable part of the development procedure.• dynaTrace integration with Compuware Gomez real-user Monitoring – statistics middle gives true-time transaction reputation in function-specified dashboards across all application tiers. additionally, clients now gain deep diagnostic potential through a single click on, enabling unheard of root trigger analysis. This additionally complements current integration with the Gomez internet Load trying out and synthetic Monitoring solutions.


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    WebSphere Message Broker V6.0, System Admin

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    MESSAGING: WHAT TO CHOOSE AND WHEN | killexams.com real questions and Pass4sure dumps

    XavientBlockedUnblockFollowFollowing

    Oct 18, 2016

    In the previous blog, we gave an overview of the different messaging protocols available to us (AMQP & JMS) and listed each one’s benefits and issues. In this blog, we intend throwing light on the different messaging solutions available in the market such as Kafka, RabbitMQ, Cloud Messaging solutions such as Amazon SQS and Google Pub Sub, Container built in messaging such as Oracle M)M in Oracle Weblogic and IBM MQ in WebSphere and which should be used for what situation

    Apache Kafka

    Apache Kafka is a message broker that was originally developed by LinkedIn before going open source under the Apache umbrella. Kafka’s main focus was speed and efficiency. It can maintain a message throughput rate of 100k+ msgs/sec which is significantly higher than most other alternatives. However, other basic features found in other brokers are eschewed in favor of efficiency. Features like message acknowledgments, routing rules and an admin UI are not available as standard. Even though Kafka is defined as ‘stable’ and ‘production-ready’, deploying Kafka in HA mode is tougher than it is with other providers. It is highly dependent on the Zookeeper module in order to maintain synchronization between multiple Kafka nodes and client apps.

    RabbitMQ

    RabbitMQ is an open source message broker software managed by Pivotal. It implements the Advanced Message Queuing Protocol (AMQP) which we described in detail in the earlier blog. Unlike Kafka, RabbitMQ comes with a built-in management dashboard for easy configuration and monitoring of message queues. It also supports requests for message acknowledgements and is easily deployed in HA mode with minimal effort. With a message rate of about 20k+ msgs/sec which is much less than Kafka, it’s sufficient enough for most use cases. Due to it’s platform-independent framework and ease of use, RabbitMQ is a very mature message broker offering with a lot of extended support from the community.

    Amazon SQS

    Simple Queue Service (SQS) is Amazon’s cloud-based message queuing system made available as part of Amazon Web Services (AWS). Unlike the other brokers mentioned here, there is barely any setup/deployment required for an application to use SQS. All you need is AWS credentials to be able to use it. Since it is a SaaS (Software-as-a-service), this makes it a much lower-cost option since there is no infrastructure cost. You only pay for what you use. SQS also has the notion of in-flight messages. This means that the message is pulled off the queue but never deleted until the ‘delete‘ command is sent for that message ID. So if you lose your worker mid-processing of the event, that event isn’t lost for good.

    One drawback of the SQS implementation is the need for polling of a message queue to determine if new messages have appeared. This is a bit of an issue being as you must now model your application to perform a polling cycle in order to determine if new messages are available. While the SQS costs are reasonable, Amazon places a limit on the message rate which can be increased on request and also charges extra based on the overall message rate.

    Google Cloud Pub/Sub

    Google’s Cloud Pub/Sub is a fully-managed real-time messaging service that allows you to send and receive messages between applications or server. It is developed specifically to faciliate messaging among applications deployed on the Google Compute Engine (GCE) cloud. It’s basic features are very similar to that of Amazon’s SQS in that there are no infrastructure costs and you pay for what you use. The major difference from SQS is that Google charges based only on volume and not transacation speed i.e. you will not be charged extra for a sudden increase in message rate.

    IBM MQ & Oracle WebLogic JMS

    Both IBM MQ and Oracle WebLogic JMS are very similar in features to standard JMS. You would choose one over the other based on what framework your app is using i.e. if your app is deployed on IBM Websphere, it will be much easier to integrate it with IBM MQ than any other provider.

    Similarly, it would be simpler to integrate an app running on Weblogic with the JMS server that it provides instead of an external provider.

    For in-detail comparisons , read more here — https://techblog.xavient.com/messaging-what-to-choose-and-when/

    Implementing Retries with a MDB or an MQ Batch Job? (WAS 7, MQ 6) | killexams.com real questions and Pass4sure dumps

    We need to listen for messages distributed via Websphere MQ to get informed when an employee joins or leaves IBM. And because the resources used in the processing (a database, a webservice) may be temporarily unavailable, we must be able to deal with such outages, which may range from minutes to hours, by repeatedly retrying the processing after some delay. And we must be also able to deal with “poison messages”, that means messages whose processing always fails either because their content is invalid or because their data isn’t consistent with the database.

    The question is whether this would be better implemented as a Message-Driven Bean (MDB) or a batch job regularly checking its queue given that we have Websphere Application Server 7 (and thus Java EE 5) and Websphere MQ 6, which both have some important changes compared to the previous versions. It turns out that it depends – both approaches have some advantages and disadvantages and so it’s a question of the likelihood of particular problems and business requirements and priorities.

    Setting the scene MDB vs. a batch job: the decision factors

    Whether we should select the MDB or the batch job approach depends on a number of factors, some of them are:

  • Requirements
  • Quantity: What quantity of messages do we need to handle?
  • Error likelihood: What’s the probability that a resource will be temporarily unavailable or that a message will contain data that can’t be processed correctly and how soon such a problem needs to be resolved? I.e. can we wait to another day or shall we get going as soon as the resource is up again? This will tell us how often we need to retry and whether a manual handling of an issue is sufficient.
  • Support for error handling/retrial logic
  • Ease of use/development/maintenance
  • Performance: We need to handle all the incoming messages and to have minimal negative impact on the  performance of the target system
  • Speed of processing of the incoming messages (immediate vs. once or few times per day)
  • Integration with our operations monitoring (logging, our Operations Dashboard webapp)
  • The problems to deal with

    There are three types of problems that can occur:

  • A failure to communicate with MQ, for instance because a network connection got interrupted
  • Inability to process a message due to a resource (a DB or a WS) being temporary unavailable
  • A poison (invalid) message (wrong data type, unexpected content) leading to an exception during its processing
  • The two approaches Approach 1: A Message-Driven Bean (MDB)

    A MDB is hosted by an application server that does a lot of work on behalf of the bean (such as transaction and concurrency management) thus simplifying its development and configuration. It may be as simple as writing

    @javax.ejb.MessageDrivenpublic class SimpleMessageBean implements javax.jms.MessageListener { public void onMessage(javax.jms.Message inMessage) { final javax.jms.TextMessage msg = (javax.jms.TextMessage) inMessage; final String msgBody = msg.getText(); // handle the msgBody ... }}

    and configuring the ActivationSpecification for the MDB in JNDI via the app. server’s administration UI.

    The question is, of course, how well it can handle poison messages and retrial when resources are temporarily unavailable.

    MDB error handling and configuration in Websphere 7

    Let’s have a look how Websphere deals with various kinds of errors related to MDBs and how we do configure a MDB in this application server, especially with regard to error handling.

    MDB error handling in Websphere

    What happens when an error occurs?

    Normally an Application Server starts an MQ transaction before it invokes a MDB and it either commits it when the MDB finishes or rolls it back when it throws an exception. If the transaction succeeds then the message is removed from the MDB’s queue otherwise it will be returned there and processed again in the future. This is the default behavior corresponding to configuring container-managed transactions (tx) with the type of ‘required’. Notice that also DB operations can participate in this transaction and thus be committed/rolled back as well, which might be useful.

  • In the case of an MQ communication/connection failure, WAS logs the exception and retries the connection later based on its configuration. It’s also possible to set an ExceptionListener that would be called with the exception as a parameter in such a case.
  • In the case of an exception during message processing (or due to a manual setRollbackOnly invocation) the current transaction is rolled back, the message is put back to the queue and the MDB is restarted. When the queue is re-checked, the message is found again and passed to another MDB. If the cause of the problem persists, this will fail again – and so on.
  • We have two ways how to deal with a failed message:

  • Remove the message from the queue either by discarding it or by moving it to the queue’s “back-out queue”. This is appropriate when the message itself is the problem (e.g. contains data inconsistent with the DB …).
  • Stop processing messages from the queue (pause the Activation Specification) and restart it later when the problem is resolved. This is appropriate when a necessary resource is temporarily unavailable.
  • Messaging provider and JMS resources configuration related to error handling

    We will use JCA 1.5 Activation Specification (and not Listener Ports that are deprecated since WAS 7) with Websphere MQ as the provider, which limits our configuration options to those described below.

  • Disabling (temporarily) the whole queue, more exactly shutting down the MDB
  • “Number of sequential delivery failures before suspending endpoint” – on an MQ Activation Specification
  • “Stop endpoint if message delivery fails” – if true, message delivery suspended when the Number of sequential delivery failures… exceeded
  • Removing problematic messages (this is done by Websphere MQ itself, not WAS)
  • “Backout threshold” (BOTHRESH; a property of a queue, configured via MQ) specifies the maximum number of times a message can be put onto a queue before it is moved to the specified backout requeue queue.  Default: 0 or unset?! => never re-delivered
  • “Backout requeue queue” (BOQNAME; a property of a queue, configured via MQ) – the queue where to put the failed messages; default: SYSTEM.DEAD.LETTER.QUEUE
  • WARNING: This seems to apply only to a queue, not to a topic. But underneath topics use (likely dynamic) queues anyway so it should be possible somehow.
  • Other related settings
  • Interesting resources:

    Design of the MDB approach Error handling design

    The main problem with the MDB approach is that it doesn’t support retrying an operation after a delay (either for a single failing message or for the whole queue, if a resource is temporarily unavailable). There are some workarounds, but not very nice.

  • For a single message I couldn’t find a way to implement retrials after some, preferably increasing, delay; the only thing we can do is to retry it few times with the default Websphere’s delay, which seems to be 5s, and if it still doesn’t succeed then move it into a special queue that would be processed manually while perhaps also sending an email notification.
  • If there is some global problem, such as an unavailable resource, indicated by several consecutive failures of one or more messages (depending on the content of the queue), we could let WAS stop the MDB and re-enable it later either automatically after a delay or manually when the problem gets fixed.
  • MDB design
  • When resource unavailability is detected, let WAS stop the MDB automatically via the setting “Number of sequential delivery failures before suspending endpoint”. It will need to be re-enabled either manually or automatically.
  • Manual re-activation of the MDB: We have to somehow detect that the MDB was disabled (the only option is perhaps by watching the log), find out why it failed, and re-enable it via the Websphere Administration Console.
  • Automated re-activation: Implement a scheduled stateless EJB, which re-enables the MDB after few tens of minutes – preferably do this few times with increasing delay, if still failing, give up and notify an admin.
  • When there is a poison message, move it to the backout queue and notify somebody to handle it (e.g. via email)
  • If the queue contains only one message there is no generic way how to find out whether the problem is within the message or in some resource, the MDB would need to find this out and communicate it. If there are multiple messages and only one fails, we know it’s a poison message and it could be automatically removed by means of the “Backout threshold”. (Beware of the interaction between the message’s redelivery count/backout threashold and the “Number of sequential delivery failures…” – the letter is reset when either a message processing succeeds or when the MDB is stopped/restarted.)
  • (Perhaps we could use JMS selectors on JMS header properties (e.g. JMSRedeliveredto, JMSXDeliveryCount) but the possibilities are rather limited because both the selector query and the properties are static (e.g. something like MyRetryTime >= now() isn’t possible). Note: MQ V7 has a major rewrite of the selectors support and their handling was moved from the Java client directly to the queue manager.)
  • MDB approach evaluation Key MDB issues
  • Permissions to perform a WAS administration operation required.
  • Difficult to distinguish a poison message from a resource outage without additional logic when the queue contains only one element.
  • But see the batch job design below, which also requires to be able to distinguish these two types of problems.
  • Inefficient determination of MDB’s status for the delay reactivation logic: either polling its status regularly or watching the log with many unrelated messages that can’t be filtered out.
  • Key MDB advantages and disadvantages
  • Advantages
  • The data is processed and reaches the destination system soon after it is published
  • Key characteristics are configurable via UI (Number of sequential delivery failures, Backout threshold, MQ connection data, MQ security/SSL, processing concurrency, DataSource configuration/injection, …). Actually this is also a disadvantage due to needing an admin, see below
  • Logging configurable at the runtime (Java logging, levels can be set in WAS console)
  • Disadvantages
  • Any configuration requires a WAS administrator and thus lot of time due to the IBM bureaucracy and ceremony
  • Difficult to collect and communicate statistics for our Operations Dashboard because (a) there are frequent fine-grained changes instead of 1/day batch changes and (b) there is no support for the Job logging framework of ours (a possible but laborious solution is to gather statistics in an instance variable and log them in regular intervals into the job tables using JDBC and some code extracted from the job framework)
  • Necessary to somehow configure the reactivation EJB (the reactivation delay, number of attempts before giving up)
  • MDB design questions:

  • Do we need automated reactivation of a disabled MDB? Perhaps not if: (1) a resource outage happens rarely and/or (2) the administration team spots this automatically and can handle it automatically without any bureaucracy and consumption of our resources.
  • MDB resources

    Essential docs

    Other (not all docs available for our version, namely MQ v6 and WAs v7)

    Approach 2: A batch job checking MQ regularly

    A batch job is a command-line application that is regularly, for example once a day, run by cron and actively scans its incoming queue/topic for new messages and processes them all at once. All the JMS communication and management and configuration must be implemented from scratch. (Though utilities such as Spring JMS template may simplify it.)

    Job error handling and configuration Error handling

    The problems and ways to deal with errors are the same as when running in an application server, only we have to do everything ourselves. That means we need to manually start a transaction, catch exception and commit/roll back and configure the topic/queue for poison messages.

    We would need to implement a problem cause detection logic to distinguish whether there is a temporary resource outage or whether there is a problem with the message itself (either incorrect data type or data inconsistent with the target DB ). There is no other good way to deal with these distinct kinds of problems without really knowing which of them it is.

    We would deal with the problems as follows:

  • For a resource outage, retry few times with an increasing delay, then quit and postpone the processing till the next scheduled execution
  • For a poison message, move it to the backout queue and notify an administrator
  • JMS configuration in a batch job

    We have two ways to configure the JMS resources (a Topic and a (Topic)ConnectionFactory) and their options related to error handling:

  • Use MQ provider-specific APIs to create the objects and configure them. See this JMS + MQ API example.
  • Configure the provider-specific resources in JNDI and use only the standardized JNDI and JMS APIs. This is very easy with Sun’s filesystem-based JNDI provider (fscontext.jar and providerutil.jar) and vendor-supplied tools for generating the JNDI .bindings file for existing JMS resources . In the case of MQ you can do it in the MQ Explorer GUI or with the command-line utility JMSAdmin (a JMSAdmin example, another one).
  • You can create the JNDI configuration via the MQ Explorer wizards – after having added a new JNDI “context” using fscontext and a local directory – either by first creating the JMS resource and then letting the wizard generate an MQ resources for it and adjusting it as needed:

    Or by creating the JMS resource from an existing MQ resource:

    Provided that the FsContext configuration file .bindings produced by JMSAdmin/MQ Explorer is in the folder /etc/mqJndiconfig, we would connect to the MQ as follows:

    final Hashtable<String, String> env = new Hashtable<String, String>();env.put(Context.INITIAL_CONTEXT_FACTORY, "com.sun.jndi.fscontext.RefFSContextFactory");env.put(Context.PROVIDER_URL, "file:/etc/mqJndiconfig");final InitialContext context = new InitialContext(env);ConnectionFactory qcf = (javax.jms.ConnectionFactory) context.lookup("MyConnectionFactory"); // Note: I set the channel property MCAUSER so it actually isn't necessary to supply credentials below:final Connection connection = qcf.createConnection("anna", "password ignored");// Client ID is necessary for a durable subscr.// We could alternatively set in on the ConnectionFactory - the CLIENTID propertyconnection.setClientID("MyCoolApplication");final Session session = connection.createSession(true, -1); // the param 2 is ignored for durable subscr.final Topic destination = (Topic) context.lookup(JNDI_PREFIX + TOPIC);final MessageConsumer receiver = subscribe(session, destination);try {connection.start();} catch (javax.jms.JMSException e) {throw new RuntimeException("Failed to start the JMS connection", e);}

    We would then read the messages via:

    while ((msg= receiver.receiveNoWait()) != null) {handleMessage(msg);}

    The dependencies include jms.jar, fscontext.jar and providerutil.jar. You can find them either in the WMQ installation or on the web.

    You may want to have a look at Writing a simple publish/subscribe application connecting through WebSphere MQ in WMQ help, which discusses some of the code above in more detail.

    Job design
  • Set a reasonable backout threshold and a suitable backout queue on the queue used by the Topic so that problematic messages are removed automatically after few failed attempts
  • Some monitoring of the backout queue would be necessary. If the MQ infrastructure doesn’t provide it then we can implement another MQ/JMS reader that would send an email when there are some new messages in the queue.
  • Regarding the type of the topic queue:
  • Either we can use the shared JMS queue (SYSTEM.JMS.D.SUBSCRIBER.QUEUE) for the topic
  • Or we can use a non-shared queue unique for our application, which would be actually better and more aligned with IBM standards. It’s achieved by setting a broker durable subscription queue pattern in the form “SYSTEM.JMS.D..*” (notice the trailing *) on the JMS ConnectionFactory when defining it in JNDI or in the Java code. Upon the first connection a permanent dynamic queue is generated for the client. We can then set the backout options on it (or the administrators may define a model queue for these dynamic queues with this setting already applied).
  • Read and process each message in a new MQ transaction so that if the processing fails it will be put back into the queue (and its delivery count will be increased, thus making it perhaps eligible for the backout queue)
  • Batch job approach evaluation
  • Advantages
  • Simple implementation offlexibledelayed retrials – upon a resource outage, end the job and try again during the next scheduled run or, perhaps, retry first after a manual delay (Thread.sleep(); beware connection timeouts).
  • Simple integration into our monitoring/logging framework incl. the Operations Dashboard.
  • Disadvantages
  • More coding to set up/clean the resources and handle errors, which is not trivial, and thus also more bug prone.
  • Concurrent processing of the messages would be considerably more difficult to implement correctly if possible at all (if MQ JMS does implement the necessary optional methods). We would need to use the advanced methods targeted at app. server vendors – there is a non-MQ example of a multithreaded (non-durable subscription) consumer. Hopefully it could be modified for a durable one using Connection.createDurableConnectionConsumer with the same simple implementation of ServerSessionPool.
  • Impl. details: The “pool” would always create a new custom ServerSession implementation. instance, whose getSession() would simply create a new transacted TopicSession, set its MessageListener, and run the session in a new Thread when start() called. Notice that (Topic)Session implements Runnable, whose run() invokes the provided listener sequentially for available messages. The listener would process a message and call commit/rollback on its owning session (see this transactional listener example).
  • Important: Handling of failed messages would need to be considered carefully as returning a message to the queue would lead to its immediate re-processing and likely rejection by one of the other threads, exceeding its backout treshold in a few seconds and thus subverting the delayed retrial processing. On the other hand, as mentioned above, we should anyway be able to distinguish resource outage, in which case we would stop processing immediately, and a problematic message, which would anyway end up in the backout queue so this is perhaps not a real issue.
  • Note: When using MessageListeners, it’s important to set an ExceptionListener on the connection because some errors can only be communicated this way.
  • Summary and conclusion

    The version and fixpack level of WMQ/WAS is very important.

    Both the approaches have some pros and cons and it depends on the requirements and their priority which one would be more suitable.

    MDB: It’s more difficult to implement delayed retrial if it is required – it may be implemented via a scheduled EJB automatically re-enabling the MDB stopped by WAS after a number of failures (one issue is that we’d need WAS admin rights for the EJB to do that; another is performance due to the need to either monitor logs or check the MDB’s status regularly). On the other hand, concurrent processing is available out of the box and also implementing a bean notifying about problematic messages in the backout queue is simpler thanks to the injection of the mail API resources. This solution may thus require some JMX and Java EE (scheduled EJB) magic and there may be unexpected problems with that.

    JOB: Concurrency: it’s more difficult to implement processing of the messages in parallel, there is even a slight chance that it’s impossible. Also  more coding is required and thus there will be more bugs. On the other hand we can implement delayed retrials as we want. Thus if concurrent processing isn’t critical while the automatic delayed retrial may be then this would be the best approach.

     

    From http://theholyjava.wordpress.com/2010/09/13/implementing-retrial-with-a-mdb-or-an-mq-batch-job-was-7-mq-6/


    Review: RSA ClearTrust 5.5 secure federated identity management system | killexams.com real questions and Pass4sure dumps

    Secure (SSO) single sign-on to multiple websites is the ultimate draw for emerging Web services. Security managers want to leverage architecture to save costs and increase user productivity. That means extending digital identities across applications that reside on multiple platforms and domains inside and outside the enterprise. RSA Security's ClearTrust 5.5 is designed to deliver SSO through federated identity, user management support and dynamic transactional authorization for multipartner Web services.

    ClearTrust 5.5 provides SAML-based identity and authentication via a Federated Identity Management (FIM) module. Its API allows developers to bundle authentication and authorization requests from legacy and new applications. The release also features user management enhancements and automated workflow to authorize account creation, group assignment, profile updates and password resets. It includes transactional Smart Rules, which enable dynamic look up in databases and LDAP directories to support authorization decisions.

    While ClearTrust's federated identity is a no-brainer for developing real-time SSO authorization for Web services, security managers will be distracted by having to toggle across the application's three interfaces.

    Simple SSOClearTrust 5.5's FIM module provides access to multiple Web sites with a single logon -- meaning a Web service user can authenticate once and benefit from a distributed SSO to other Web sites that have a trusted relationship. FIM enables SAML 1.1-compliant identity management by generating and processing SAML assertions, which provide XML-based authentication and authorization.

    FIM makes it easy to manage SAML Asserting and Relying Parties. The Asserting Party is the one who requests access to a resource on a Relying Party's site. The Relying Party accepts SAML assertions, which are statements that declare that the identity of a user has been authenticated. Trust relations, established by defining the Asserting Party and Relying Party, enable one identity to be mapped to multiple identities on other sites to eliminate the constant toggling of names and passwords. ClearTrust maintains a local store of relationship data (user names and aliases).

    We configured an associated Relying Party for our Web service, a remote user identity and the authentication methods allowed, and mapped our local identity to a different remote identity to fully test the SSO federated identity capabilities.

    We set the Asserting Party issuer and source identity, Asserting Party policy and trusted Relying Parties. The issuer and source identity -- the equivalent of a user ID for a Web service consumer -- is a 40-character randomly generated string that's similar to a shared key in a site-to-site security relationship. ClearTrust binds this identity string to a SOAP transport service URL, which acts as a broker for requests to a Web service. We used the FIM interface to configure length assertion lifetime policy, which defines how long the session is allowed, and custom authentication and Relying Party verification methods, such as digital certificate or basic authentication.

    We authenticated to the Web service hosted on our system via the local client domain. An encrypted cookie identified us as a trusted authenticated party, and our request was directed via an SSL-secured connection to the remote Web service domain associated with our Relying Party. The SAML assertion essentially mapped our local identity to the remote identity. The remote Web service, which was configured to display the entire SAML assertion upon successful authentication and authorization, accepted our assertion, and the comprehensive display verified the receipt and accuracy in processing the SAML assertion.

    Along with the standard SAML fields, ClearTrust allows for the enumeration of additional attributes, which can be used to pass information to other Web services. For example, we were able to pass metadata in the form of name/value pairs for an affinity program to the remote domain. It accepted our identity and authentication assertions, but relied on remote site authorization to control what we were able to access.

    Streamlining Web Services SecurityRSA includes its ClearTrust API in version 5.5 as a "One Call" Web service for enhanced integration and to extend its reach to enterprise applications. For example, developers can bundle authentication and authorization requests from legacy and new applications such as Microsoft's .NET into a single Web service API call. ClearTrust can respond to the Web services calls with a single response that answers the packaged requests. This reduces the number of conversations between systems and improves performance considerably. For example, providing the API as a standards-based Web service allows enterprises to integrate Microsoft's BizTalk and Sun Microsystems' J2EE to use the same authentication and authorization scheme.

    ClearTrust employs a Web Services Description Language interface, which uses an XML-based language to describe various Web services calls, their related functions and how to access them.

    We installed our Web archive file, which is used for deploying Web applications, on a BEA WebLogic server to test the ClearTrust API, and made several test calls to the ClearTrust Web service API requesting authentication and authorization. Our simple Web service client sent the packaged request seeking authorization to three URLs (/secure, /test and /accounts). The ClearTrust Web service API bundled its answer, and following policy, returned three "allow" responses.

    ClearTrust compares the user ID, authentication secret and URL requested with the local ClearTrust security policy to issue allowance decisions. We removed our user's entitlements to the three URLs to test ClearTrust's ability to deny access, and resent our packaged request. The Web service API responded by bundling three "deny" responses.

    Transactional AuthorizationsClearTrust has extended its standard Smart Rules in 5.5 to enable a dynamic lookup of authorization decision data stored in databases, LDAP directories, Web services and XML files.

    We created an XML file with a user ID for dynamically checking a value, and a file with a field called "credit card approval" and an assigned value of 0,1 to toggle between. We set the rule to deny access if the value for the user was 0, and allow access to a resource if the value was 1. The ACL resource was defined as our Web server directory containing a collection of analyst reports that were for paying subscribers only. Our transactional rule performed a credit card validation lookup, and issued file access pending the charge acceptance.

    The transactional smart rule denied us when we tried to access the directory with the value set to 0, and with the value set to 1 allowed our access to see pages within the protected directory. ClearTrust's pairing of dynamic data and business logic for driving security decisions is an important functionality. In the case where a user's access to downloadable software is based on available credit, ClearTrust could check the user's credit to dynamically deny or authorize access.

    Empowered UsersClearTrust 5.5 has eased the sting of managing end users with its Advanced User Management (AUM) module. With AUM's built-in workflow, security managers can preapprove a series of actions for authorized users, including account creation, group assignment, profile update and password reset requests.

    We completed the username, password, e-mail address, full name and zip code to authorize account creation. Three questions were selected from a pull-down menu to authorize future unassisted password resets. Questions are configurable, but we applied the RSA templates for "favorite teacher," "first pet's name" and "name of your elementary school." A receipt was issued, and we were notified by e-mail when the account was approved.

    The AUM module provides a list of approval tasks, and we clicked to endorse the account. An e-mail message was issued to our end user that the account activation had been approved. We tested the password reset by entering our user name in the text field and providing our three previously validated responses to the psychographic questions. Our verified responses allowed us to replace our forgotten password.

    A Good MarriageRSA ClearTrust 5.5 has introduced useful features that support the emerging Web services SAML 1.1 standard. Its self-service eases user administration. Dynamic transaction rules marry business logic to security authorization decisions, with the ability to query both internal and external partner Web services for dynamic decision support. These features, when combined with the ability to expose the API as Web service, create a centralized security architecture with intelligent and distributed capabilities.

    While accessing three administrative interface links from a browser-based dashboard served our purposes, combining the consoles into a single interface would make ClearTrust a more efficient tool for security managers. But given the robust and intuitive feature set in ClearTrust 5.5, we can't complain.

    Snapshot

    RSA ClearTrust 5.5RSA SecurityPrice: Starts at $26/user

    PURPOSE: ClearTrust 5.5 provides access and federated identity management for Web services.

    REQUIREMENTS: Requires Microsoft Windows 2000/2003, Red Hat Linux Enterprise or SuSE Linux, Sun Solaris 8/9, IBM AIX or HP-UX; Oracle, Microsoft SQL Server 2000, Sybase, Microsoft Active Directory or Sun One Directory Server; Sun One Webserver, Microsoft IIS 5.0/6.0 or Apache; BEA WebLogic 8.1, IBM WebSphere or Tomcat.

    KEY FEATURES:- Provides SAML 1.1-based Web service authentication.- Comprehensive API Web service integration.- Dynamic transaction authorization based on business logic.

    PROS:- Federated identity maps user IDs across Web services and sites.- Automated user account management and workflow.

    CONS:- Three separate administrative GUI's.

    VERDICT: RSA ClearTrust 5.5 delivers key components that target the need for federated identity with support for emerging Web service standards. User self-service eases the administrative burden for authorizing Web services authentication. RSA offers a robust feature set and practical solution.

    George Wrenn, CISSP, is a technical editor for Information Security, a graduate fellow at the Massachusetts Institute of Technology and a director of security at a financial services firm.



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    References :


    Dropmark : http://killexams.dropmark.com/367904/12846686
    Dropmark-Text : http://killexams.dropmark.com/367904/12941908
    Blogspot : http://killexams-braindumps.blogspot.com/2018/01/exactly-same-000-998-questions-as-in.html
    Wordpress : https://wp.me/p7SJ6L-2Jw
    Box.net : https://app.box.com/s/etsak2zf3o466kv6fkru5fg5tz4xst50






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