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Test Number : 70-767
Test Name : Implementing a SQL Data Warehouse
Vendor Name : Microsoft
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Exam Title : Microsoft Certified Solutions Expert (MCSE) -Data Management and Analytics
Exam ID : 70-767
Exam Duration : 150 mins
Questions in test : 40-60
Passing Score : 700 / 1000
Official Training : 20767: Implementing a SQL Data Warehouse (five days)
Exam Center : Pearson VUE
Real Questions : Microsoft Implementing a Data Warehouse using SQL Real Questions
VCE practice exam : Microsoft 70-767 Certification VCE Practice Test

Topic Details Weights Design, implement, and maintain a data warehouse Design and implement dimension tables
- Design shared and conformed dimensions, determine support requirements for slowly changing dimensions, determine attributes, design hierarchies, determine star or snowflake schema requirements, determine the granularity of relationship by using fact tables, determine auditing or lineage requirements, determine keys and key relationships for a data warehouse, implement dimensions, implement data lineage of a dimension table Design and implement fact tables
- Identify measures, identify dimension table relationships, create composite keys, design a data warehouse that supports many-to-many relationships, implement semi-additive measures, implement non-additive measures Design and implement indexes for a data warehouse workload
- Design an indexing solution; select appropriate indexes; implement clustered, non-clustered, filtered, and columnstore indexes Design storage for a data warehouse
- Design an appropriate storage solution, including hardware, disk, and file layout Design and implement partitioned tables and views
- Design a partition structure to support a data warehouse, implement sliding windows, implement partition elimination, design a partition structure that supports the quick loading and scale-out of data 35-40% Extract, transform, and load data Design and implement an extract, transform, and load (ETL) control flow by using a SQL Server Integration Services (SSIS) package
- Design and implement ETL control flow elements, including containers, tasks, and precedence constraints; create variables and parameters; create checkpoints, sequence and loop containers, and variables in SSIS; implement data profiling, parallelism, transactions, logging, and security Design and implement an ETL data flow by using an SSIS package
- Implement slowly changing dimension, fuzzy grouping, fuzzy lookup, audit, blocking, non-blocking, and term lookup transformations; map columns; determine the appropriate transform object for a given task; determine appropriate scenarios for Transact-SQL joins versus SSIS lookup; design table loading by using bulk loading or standard loading; remove extra rows or bad rows by using deduplication Implement an ETL solution that supports incremental data extraction
- Design fact table patterns, enable Change Data Capture, create a SQL MERGE statement Implement an ETL solution that supports incremental data loading
- Design a control flow to load change data, load data by using Transact-SQL Change Data Capture functions, load data by using Change Data Capture in SSIS Debug SSIS packages
- Fix performance, connectivity, execution, and failed logic issues by using the debugger; enable logging for package execution; implement error handling for data types; implement breakpoints; add data viewers; profile data with different tools; perform batch clean-up Deploy and configure SSIS packages and projects
- Create an SSIS catalog; deploy packages by using the deployment utility, SQL Server, and file systems; run and customize packages by using DTUTIL 40-45% Build data quality solutions Create a knowledge base
- Create a Data Quality Services (DQS) knowledge base, determine appropriate use cases for a DQS knowledge base, perform knowledge discovery, perform domain management Maintain data quality by using DQS
- Add matching knowledge to a knowledge base, prepare a DQS for data deduplication, create a matching policy, clean data by using DQS knowledge clean data by using the SSIS DQS task, install DQS Implement a Master Data Services (MDS) model
- Install MDS; implement MDS; create models, entities, hierarchies, collections, and attributes; define security roles; import and export data; create and edit a subscription; implement entities, attributes, hierarchies, and business rules Manage data by using MDS
- Use MDS tools, use the Master Data Services Configuration Manager, create a Master Data Manager database and web application, deploy a demo model using MDSModelDeploy.exe, use the Master Data Services web application, use the Master Data Services Add-in for Excel, create a Master Data Management hub, stage and load data, create subscription views 15-20%

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Microsoft Implementing cheat sheet

artificial intelligence: Cheat sheet | 70-767 test dumps and test Questions

study artificial intelligence fundamentals, company use situations, and extra in this beginner's ebook to the use of AI within the business.

artificial intelligence (AI) is the subsequent huge element in business computing. Its uses are available many forms, from basic tools that respond to customer chat, to advanced computing device researching methods that predict the trajectory of a whole organization. popularity doesn't always cause familiarity, and despite its constant appearance as a state-of-the-paintings function, AI is often misunderstood. 

in order to support company leaders take into account what AI is able to, the way it will also be used, and where to begin an AI experience, or not it's essential to first dispel the myths surrounding this big jump in computing technology. gain knowledge of extra during this AI cheat sheet. this article is also available as a download, Cheat sheet: artificial intelligence (free PDF).

SEE: All of TechRepublic's cheat sheets and smart person's courses

what's synthetic intelligence?

When AI comes to mind, it be effortless to get pulled into a global of science-fiction robots like information from superstar Trek: The next generation, Skynet from the Terminator series, and Marvin the paranoid android from The Hitchhiker's e-book to the Galaxy. 

The truth of AI is nothing like fiction, notwithstanding. as a substitute of entirely autonomous thinking machines that mimic human intelligence, they reside in an age where computers can also be taught to perform confined projects that contain making judgments akin to these made via people, but are removed from being able to motive like human beings. 

up to date AI can function graphic cognizance, keep in mind the herbal language and writing patterns of humans, make connections between various kinds of data, establish abnormalities in patterns, strategize, predict, and more. 

All artificial intelligence comes all the way down to one core concept: demo consciousness. on the core of all functions and forms of AI is the fundamental ability to determine patterns and make inferences according to those patterns. 

SEE: synthetic intelligence: A enterprise leader's ebook (free PDF) (TechRepublic)

AI is never in fact intelligent in the approach they define intelligence: It cannot feel and lacks reasoning advantage, it does not reveal preferences or have opinions, and it be no longer able to do anything else outdoor of the very slim scope of its training. 

That doesn't mean AI is rarely positive for agencies and buyers attempting to solve actual-world issues, it simply capacity that we're nowhere near machines that can in fact make independent decisions or arrive at conclusions devoid of being given the appropriate statistics first. artificial intelligence continues to be a wonder of technology, but it surely's nonetheless removed from replicating human intelligence or really clever habits.

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What can synthetic intelligence do?

AI's vigor lies in its potential to turn into incredibly professional at doing the issues humans teach it to. Microsoft and Alibaba independently constructed AI machines able to more desirable analyzing comprehension than people, Microsoft has AI it truly is enhanced at speech attention than its human builders, and a few researchers are predicting that AI will outperform humans in most everything in less than 50 years.

That doesn't mean these AI creations are really clever--only that they're able to performing human-like projects with more suitable efficiency than us error-susceptible organic beings. in case you were to try, say, to provide a speech focus AI an image-attention task, it will fail fully. All AI techniques are built for extremely specific initiatives, and they would not have the skill to do the rest. 

because the COVID-19 pandemic began in early 2020, synthetic intelligence and computing device gaining knowledge of has viewed a surge of exercise as businesses rush to fill holes left via personnel forced to work remotely, or people that've lost jobs because of the economic pressure of the pandemic. 

The short adoption of AI all the way through the pandemic highlights another essential element that AI can do: exchange human laborers. in response to Gartner, seventy nine% of agencies are at the moment exploring or piloting AI initiatives, that means those tasks are within the early publish-COVID-19 stages of building. What the pandemic has accomplished for AI is trigger a shift in priorities and applications: as a substitute of specializing in financial analysis and client perception, submit-pandemic AI projects are focusing on consumer experience and value optimization, Algorithmia found.

Like different AI applications, customer journey and cost optimization are in response to pattern attention. in the case of the former, AI bots can perform many fundamental customer service initiatives, releasing employees as much as handiest tackle circumstances that need human intervention. AI like this has been notably common during the pandemic, when people forced out of name centers put stress on the customer provider conclusion of business.

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What are the enterprise functions of synthetic intelligence?

modern AI techniques are in a position to magnificent things, and it's now not challenging to imagine what sort of business tasks and difficulty solving workouts they could be acceptable to. believe of any events assignment, even totally advanced ones, and there's a possibility an AI can do it extra precisely and without delay than a human--simply do not predict it to do science fiction-degree reasoning.

within the company world, there are quite a lot of AI applications, but perhaps none is gaining traction as an awful lot as company analytics and its end aim: Prescriptive analytics.

business analytics is a complicated set of processes that aim to mannequin the present state of a enterprise, predict the place it's going to go if saved on its present trajectory, and model potential futures with a given set of alterations. ahead of the AI age, analytics work became gradual, cumbersome, and confined in scope.

SEE: special record: Managing AI and ML in the business (ZDNet) | down load the free PDF version (TechRepublic)

When modeling the previous of a business, or not it's critical to account for almost endless variables, style via a whole lot statistics, and include all of it in an analysis that builds an entire photo of the up-to-the-current state of a company. think about the business you are in and the entire issues that should be considered, after which think about a human trying to calculate all of it--cumbersome, to assert the least.

Predicting the longer term with an established mannequin of the previous may also be handy sufficient, but prescriptive analysis, which aims to find the best possible outcomes with the aid of tweaking an organization's existing direction, may also be downright unimaginable with out AI support. 

SEE: synthetic intelligence ethics policy (TechRepublic top rate)

there are many artificial intelligence application platforms and AI machines designed to do all that heavy lifting, and the consequences are remodeling groups: What changed into once out of reach for smaller organizations is now feasible, and businesses of all sizes could make essentially the most of every useful resource by using artificial intelligence to design the ultimate future. 

Analytics could be the rising megastar of business AI, however's infrequently the simplest application of synthetic intelligence within the commercial and industrial worlds. different AI use instances for agencies consist of here. 

  • Recruiting and employment: Human beings can often miss out on qualified candidates, or candidates can fail to make themselves observed. synthetic intelligence can streamline recruiting with the aid of filtering through higher numbers of candidates more at once, and by means of noticing qualified individuals who can also go disregarded. 
  • Fraud detection: synthetic intelligence is amazing at opting for up on delicate changes and irregular conduct. If trained to video display monetary and banking site visitors, AI programs can prefer up on subtle symptoms of fraud that humans may also omit.
  • Cybersecurity: just as with fiscal irregularities, artificial intelligence is notable at detecting indications of hacking and different cybersecurity considerations.
  • statistics management: using AI to categorize raw records and locate relations between objects that were up to now unknown.
  • client members of the family: up to date AI-powered chatbots are extremely first rate at carrying on conversations thanks to herbal language processing. AI chatbots will also be an outstanding first line of client interplay.
  • Healthcare: no longer best are some AIs able to notice melanoma and different fitness considerations earlier than medical doctors, they could additionally provide remarks on affected person care based on lengthy-term data and tendencies.
  • Predicting market developments: an awful lot like prescriptive analysis in the company analytics world, AI techniques may also be proficient to predict trends in larger markets, which can cause companies getting a start on rising traits.
  • decreasing power use: synthetic intelligence can streamline power use in structures, and even throughout cities, in addition to make more desirable predictions for development planning, oil and gasoline drilling, and different power-centric initiatives.
  • advertising: AI methods may also be informed to raise the price of advertising both toward people and larger markets, assisting businesses keep cash and get greater advertising and marketing effects.
  • If an issue includes facts, there is a superb possibility that AI can support. This record is rarely finished, and new innovations in AI and computing device discovering are being made all of the time.

    additional materials

    What AI systems are available?

    When adopting an AI strategy, it's critical to understand what types of software can be found for company-concentrated AI. There are a large choice of platforms available from the regular cloud-internet hosting suspects like Google, AWS, Microsoft, and IBM, and choosing the right you'll mean the change between success and failure.

    AWS desktop learning presents a wide variety of equipment that run within the AWS cloud. AI functions, pre-developed frameworks, analytics tools, and more are all obtainable, with many designed to take the legwork out of getting all started. AWS presents pre-built algorithms, one-click on computing device researching training, and working towards equipment for developers getting begun in, or increasing their skills of AI building.

    Google Cloud presents equivalent AI solutions to AWS, as well as having several pre-built total AI solutions that groups can (ideally) plug into their organizations with minimal effort. Google's AI offerings include the TensorFlow open supply laptop studying library.

    Microsoft's AI platform comes with pre-generated functions, ready-to-set up cloud infrastructure, and a variety of additional AI tools that can be plugged in to present fashions. Its AI Lab additionally presents a wide range of AI apps that developers can tinker with and learn from what others have carried out. Microsoft also presents an AI faculty with educational tracks primarily for business applications. 

    Watson is IBM's version of cloud-hosted desktop researching and enterprise AI, but it surely goes a bit of additional with greater AI options. IBM presents on-web page servers customized developed for AI initiatives for agencies that do not are looking to depend on cloud hosting, and it also has IBM AI OpenScale, an AI platform that may also be built-in into different cloud internet hosting services, which could assist to steer clear of dealer lock-in.

    earlier than deciding on an AI platform, or not it's vital to investigate what kinds of competencies you have purchasable inside your firm, and what competencies you'll need to focal point on when hiring new AI crew participants. The platforms can require specialization in differing types of construction and data science talents, so make certain to devise as a result.

    further substances

    What AI knowledge will agencies should invest in?

    With enterprise AI taking so many varieties, it can also be challenging to check what competencies a firm must implement it. 

    As in the past pronounced through TechRepublic, discovering personnel with the right set of AI knowledge is the difficulty most commonly referred to by way of businesses looking to get began with artificial intelligence. 

    talents necessary for an AI mission differ in keeping with business needs and the platform getting used, although most of the largest systems (like those listed above) help most, if no longer all, of probably the most normal programming languages and expertise obligatory for AI.

    SEE: do not pass over their existing coverage about AI (TechRepublic on Flipboard)

    TechRepublic coated in March 2018 the 10 most in-demand AI talents, which is an excellent abstract of the forms of working towards a company should still examine when building or increasing a company AI team:

  • computer gaining knowledge of 
  • Python
  • records science
  • Hadoop
  • large data
  • Java 
  • information mining
  • Spark
  • SAS
  • Many company AI platforms offer working towards lessons within the specifics of running their architecture and the programming languages needed to develop more AI tools. corporations which are taken with AI may still plan to either hire new personnel or provide existing ones the time and elements critical to train in the competencies essential to make AI projects succeed.

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    How can groups delivery the usage of synthetic intelligence?

    Getting started with company AI is rarely as effortless as conveniently spending cash on an AI platform issuer and spinning up some pre-constructed fashions and algorithms. there may be an awful lot that goes into correctly including AI to a firm.

    on the coronary heart of all of it is first rate venture planning. including synthetic intelligence to a company, no count how it can be used, is just like any company transformation initiative. right here is an outline of only 1 option to approach getting begun with enterprise AI.

  • assess your AI aim. determine how AI will also be used to your firm and to what end. by means of focusing on a narrower implementation with a specific purpose, which you can more suitable allocate resources.

  • identify what must occur to get there. once you comprehend the place you need to be, which you can determine the place you are and how to make the journey. This may include starting to sort current records, gathering new statistics, hiring skill, and different pre-venture steps.

  • build a group. With an conclusion intention in sight and a plan to get there, it be time to gather the top of the line team to make it take place. this may consist of latest personnel, however don't be afraid to head backyard the firm to find probably the most qualified americans. also, be sure to permit current body of workers to coach so they have the opportunity to make contributions to the undertaking.

  • choose an AI platform. Some AI structures could be stronger applicable to selected tasks, however by using and massive they all offer similar products in an effort to compete with each other. Let your team provide options on which AI platform to select--they're the consultants who may be in the trenches.

  • begin implementation. With a aim, team, and platform, you're able to delivery working in earnest. This may not be brief: AI machines deserve to be proficient, trying out on subsets of statistics needs to be carried out, and a lot of tweaks will deserve to be made earlier than a company AI is able to hit the real world.

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