QlikView 11 Developer Certification Examination exam Dumps

QV_Developer_11 exam Format | Course Contents | Course Outline | exam Syllabus | exam Objectives

Identify Requirements (23% of the exam)
Describe how the Qlik Sense capabilities can assist in analysis of data.
Determine which Qlik products and QSE features/methods are needed to meet business requirements.
Translate business needs into technical requirements.

Design Application (22% of the exam)
Organize the application layout to meet business needs.
Determine which visualizations meet business requirements.
Determine design elements needed for flexibility, ease of use, and maintenance.

Prepare and Load Data (25% of the exam)
Apply the steps to create the associative data model using the data manager.
Troubleshoot basic data modeling and data issues.

Develop Application (30% of the exam)
Configure dashboards and visualizations to illustrate business insights.
Apply steps to share business insights.

- Practical experience developing production-quality applications in Qlik Sense
- Experience creating dimensions, measures and objects for the master library
- Understands user experience and design best practices and has experience building visualizations.

- Practical experience developing multiple production-quality applications in Qlik Sense
- Ability to write Qlik Sense LOAD scripts and validate data
- Basic understanding of Extract-Transform-Load (ETL)
- Create and use connectors to various data sources
- Understand the QVD layer and architecture of the Qlik platform
- Ability to architect data to provide optimal performance
- Familiar with SQL and relational databases

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Qlik CEO Lars Björk Discusses huge records and synthetic “Stupidity”

big facts has been a hot theme for awhile now, inserting businesses similar to Qlik in an in-demand market—that also receives noisier and noisier with the aid of the hour.

Startups and greater application developers trip over every different making an attempt be the provider that varieties out mountains of digital suggestions. Microsoft has groups of researchers right here invariably creating new how you can work with massive information; local startups akin to Datadog, with its acquisition of Mortar records, offers analytics and a platform for developing huge information apps. huge information analytics is additionally a driver of jobs within the metropolis’s fintech business.

obviously the flurry of undertaking makes it difficult to stand out for any developer during this space. Qlik (NASDAQ:QLIK) is attempting to accomplish that by using specializing in making big information standard and easy to take into account; its Qlik feel application offers a visual approach to decipher records analytics says CEO Lars Björk.

based in Lund, Sweden, Qlik moved its headquarters about ten years in the past to Radnor, PA, and has extra offices in cities including manhattan, Boston, Dallas, San Francisco, and Raleigh, NC, in addition to Europe, Latin america, and Asia. Qlik is a $600 million a yr company and Björk mentioned it should be “a little above that this 12 months.” whereas now not as massive as say Oracle or SAP, Björk says Qlik has been in the analytics industry from its start. He came through long island closing Friday and i sat down with him to discuss techniques big records is grew to become into valuable content, and what he referred to as artificial “stupidity.”

Xconomy: what's occurring in big facts, and why does Qlik have workplaces in big apple?

Lars Björk: We’re in a time when information is exploding round us. more and more individuals are coming to a realization that if we are able to flip that into insightful suggestions, now not just for a few certified but for the extensive employee and consumer base in a firm, there is large advantage.

The outset for this enterprise changed into to do some thing that turned into according to ease of use. Make it very simple for the person to get going and make adjustments to the functions. This consumerization of business application today is common; if it doesn’t work on a smartphone, if it isn’t super intuitive, it’s simply now not going for use in any respect.

new york is our beachhead for one of our biggest verticals, monetary functions, as a result of the concentration of economic institutions. Take the 25 biggest banks on this planet, someway or style they're all customers of ours. They are literally drowning in data.

XC: What can you do with massive statistics that they aren’t doing on their personal?

LB: For a financial establishment in private wealth management, of their compliance and trading, we’re presenting deep analytics for his or her salesforce on their customers. So when a consumer calls them, they’re not just taking a look at a spreadsheet. you could examine correlations between investments, that you could examine it over time, and the way a portfolio would have done if they had invested a unique approach.

We’ve assist the new york city Marathon with big records. you could go in and think about the entire runners, old instances, and age agencies. The firm is the use of it to arrange the race.

Our electricity is that we are looking to provide the whole facts set. The preliminary component that spurred your interest, or the query that drove you to seem to be into the data isn't where you’re going to emerge as. You’re going to ask more questions reckoning on what the preliminary findings reveal you, the query you couldn’t formulate in your head before you begun looking into the data set.

XC: Are your goal clients senior executives who are looking to remember big information to plan approach, or personnel with particular projects?

LB: The sweet spot has moved from being a extra senior govt or analysts’ tool, from the CEO to the janitor. whereas executives may use it, complete salesforces, every person in finance, or working in a supply chain makes use of it. in case you go to a hospital—the physicians, nurses, administration individuals—what do they have in average? distinct records sources, and they're making an attempt to figure out a means to optimize their workflow. That’s the commonplace denominator.

XC: Do you best work with huge statistics from big organisations?

LB: massive facts is extraordinarily pleasing, nevertheless it’s a bit of a hype word. There will also be equal benefit in small facts. The factor isn’t so much the size of the data set, or the diversity, or velocity of the data. that you may get colossal advantage via highlighting stuff in a smaller facts. we are agnostic there. We may not have interaction within the same way with a extremely, very small use case, however we now have a companion neighborhood that works with smaller purchasers.

XC: How is the use of huge information altering, and the place can analytics be seen nowadays?

LB: you've got millennials coming into the personnel with the expectation that huge records analytics software works like anything else on their smartphones. one other fashion is collaboration, inspecting records in a workgroup. big records analytics is not standalone device anymore, it receives embedded in other environments. in case you go to NCAA baseball, their facts on their site is our utility.

here's now not a customary IT type of deployment the place only just a few people have the abilities to cope with it. There’s yet another trend with packaged facts as a service. What in case you may be approached through utility with a hyperlink to start acquire curated, normalized records this is without difficulty attainable as an alternative of spending time attempting to find it? It can be benchmark records from an trade, socioeconomic and macroeconomic records.

XC: What are you doing to stand out as startups and greater groups additionally get into the big statistics analytics online game?

LB: center of attention on our view of the place this business’s going. We could prefer up some of those smaller companies; we now have picked up a few of them that healthy into ours. I wouldn’t rule out competitors from the huge guys, but I don’t concern them both. They don’t are usually the imaginative ones. Innovation comes to them by way of opening their purses and purchasing anything else. they are extra like supermarkets, doing every thing in so many industries.

All we do, and were doing for more than 20 years, is analytics.

XC: What expertise adjustments do you see coming to massive information?

LB: synthetic intelligence and computer gaining knowledge of are clearly going to make inroads. It should be would becould very well be synthetic “intelligence” at the outset—somebody else spoke of it is actually synthetic “stupidity.” What you’re attempting to do is automate and take over for mundane, repetitive selections. That isn’t in fact intelligence.

computing device learning, internet of things, the skill to compile suggestions from sensors is going to blow up. we will debate how a whole lot is hype and the way an awful lot has precise cost. you could are expecting it to happen in health tech and the health trade. consider about an business that needs to go, in a short length of time, from being reactive to proactive. It’s early days, however’s relocating directly.

Trending on Xconomy

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