Oracle 1Z0-047 : Oracle Database SQL Expert Exam

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Exam Number : 1Z0-047
Exam Name : Oracle Database SQL Expert
Vendor Name : Oracle
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Exam Title : Oracle Database SQL Expert
Exam ID : 1Z0-047
Exam Product Version : SQL and PL/SQL
Exam Duration : 90 minutes
Questions in exam : 60
Passing Score : 66%
Format : Multiple Choice
Exam Center : Pearson VUE - Oracle
Recommended Practice : 1Z0-047 Online VCE Practice Test

Restricting and Sorting Data - Limit the rows that are retrieved by a query
- Sort the rows that are retrieved by a query
- Use substitution variables
- Use the SQL row limiting clause
- Create queries using the PIVOT and UNPIVOT clause
- Use pattern matching to recognize patterns across multiple rows in a table
Using Single-Row Functions to Customize Output - Describe various types of functions that are available in SQL
- Use character, number, and date and analytical (PERCENTILE_CONT, STDDEV, LAG, LEAD) functions in SELECT statements
- Use conversion functions
Reporting Aggregated Data Using the Group Functions - Identify the available group functions
- Use group functions
- Group data by using the GROUP BY clause
- Include or exclude grouped rows by using the HAVING clause
Displaying Data from Multiple Tables - Use equijoins and nonequijoins
- Use a self-join
- Use outer joins
- Generate a Cartesian product of all rows from two or more tables
- Use the cross_outer_apply_clause
Using Subqueries to Solve Queries - Use subqueries
- List the types of subqueries
- Use single-row and multiple-row subqueries
-Create a lateral inline view in a query
Using the Set Operators - Explain set operators
- Use a set operator to combine multiple queries into a single query
- Control the order of rows returned
Manipulating Data - Describe the DML statements
- Insert rows into a table
- Update rows in a table
- Delete rows from a table
- Control transactions
Using DDL Statements to Create and Manage Tables - Categorize the main database objects
- Review the table structure
- Describe the data types that are available for columns
- Create tables
- Create constraints for tables
-Describe how schema objects work
- Truncate tables, and recursively truncate child tables
- Use 12c enhancements to the DEFAULT clause, invisible columns, virtual columns and identity columns in table creation/alteration
Creating Other Schema Objects - Create simple and complex views with visible/invisible columns
- Retrieve data from views
- Create, maintain and use sequences
- Create private and public synonyms
Managing Objects with Data Dictionary Views - Query various data dictionary views Controlling User Access - Differentiate system privileges from object privileges
- Grant privileges on tables and on a user
- View privileges in the data dictionary
- Grant roles
- Distinguish between privileges and roles
Managing Schema Objects - Manage constraints
- Create and maintain indexes including invisible indexes and multiple indexes on the same columns
- Create indexes using the CREATE TABLE statement
- Create function-based indexes
- Drop columns and set column UNUSED
- Perform flashback operations
- Create and use external tables
Manipulating Large Data Sets - Manipulate data using subqueries
- Describe the features of multitable INSERTs
- Use multitable inserts
- Unconditional INSERT
- Pivoting INSERT
- Conditional ALL INSERT
- Conditional FIRST INSERT
- Merge rows in a table
- Track the changes to data over a period of time
- Use explicit default values in INSERT and UPDATE statements
Generating Reports by Grouping Related Data - Use the ROLLUP operation to produce subtotal values
- Use the CUBE operation to produce crosstabulation values
- Use the GROUPING function to identify the row values created by ROLLUP or CUBE
- Use GROUPING SETS to produce a single result set
Managing Data in Different Time Zones Use various datetime functions
- TZ_OFFSET
- FROM_TZ
- TO_TIMESTAMP
- TO_TIMESTAMP_TZ
- TO_YMINTERVAL
- TO_DSINTERVAL
- CURRENT_DATE
- CURRENT_TIMESTAMP
- LOCALTIMESTAMP
- DBTIMEZONE
- SESSIONTIMEZONE
- EXTRACT
Retrieving Data Using Subqueries - Use multiple-column subqueries
- Use scalar subqueries
- Use correlated subqueries
- Update and delete rows using correlated subqueries
- Use the EXISTS and NOT EXISTS operators
- Use the WITH clause
Hierarchical Retrieval - Interpret the concept of a hierarchical query
- Create a tree-structured report
- Format hierarchical data
- Exclude branches from the tree structure
Regular Expression Support - Use meta Characters
- Use regular expression functions to search, match and replace
- Use replacing patterns
- Use regular expressions and check constraints



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Oracle SQL education

a way to become a data scientist devoid of getting a Ph.D. | 1Z0-047 PDF Braindumps and exam Cram

photograph: NanoStockk, Getty pictures/iStockphoto

big information: extra should-read coverage

facts science is a box offering quite a lot of distinctive profession path alternatives, and Glassdoor.com named it the number one job in a number of contemporary years. 

Northeastern college lists on its web page a comprehensive set of capabilities jobs related to data science together with business intelligence developer, data/functions/infrastructure architect, desktop researching scientist/engineer, and, of course, the common information scientist position.

SEE: massive information management information (free PDF) (TechRepublic)

My colleague Alison DeNisco Rayome covered statistics science ultimate year and provided a plethora of particulars related to the subject. I recently spoke with Martijn Theuwissen, co-founder of DataCamp, a data science academic company, to study greater in regards to the thought.

Scott Matteson: What abilities are vital to be a knowledge scientist? 

Martijn Theuwissen: there's a standard false impression that to become a knowledge scientist one should be aware of facts, linear algebra, calculus, programming, databases, computer discovering, I could go on. Some even say a Ph.D. is required. This couldn’t be further from the truth. 

truly, any person can develop into a data scientist. All you want is a getting to know plan with measurable goals, and a fundamental realizing of the frequent statistics science languages like SQL, Python, and R.

but let’s step returned a little bit to peer what facts science truly is. information science can frequently be segmented as descriptive analytics, predictive analytics, and prescriptive analytics. 

  • Descriptive analytics is well-nigh describing information that your company already has within the sort of reports, dashboards, or other ways to share data visualizations and abstract data. 
  • Predictive analytics is the realm of prediction and computing device getting to know: for instance, classifying even if an electronic mail is unsolicited mail or no longer, in line with its content, even if a client will churn, according to interactions with your enterprise, or whether a tumor is benign or malignant, according to diagnostic imaging. 
  • Prescriptive analytics, or decision science, brings rigor to decision making with the aid of tying it to the statistics world. sure, desktop discovering is attractive, but the lion’s share of the value data science has created today throughout most verticals is really in descriptive analytics with the aid of serving important abstract statistics, visualizations and dashboards to relevant interior stakeholders. 
  • SEE: What does an information scientist do? We talked to at least one to learn about this normal and profitable box (TechRepublic)

    And any one can do that! I’ve seen facts scientists on advertising, business, and product teams deserve to redefine their personal roles as their “non-technical” teammates have discovered some SQL and records visualization in Python or R to do work and create price that became in the past impossible. And these are only one of the vital abilities that we are looking to aid construct statistics fluency all the way through the world. 

    Scott Matteson: can you seem to be within your organization to locate the advantageous skills? should still companies flip to schooling and training? 

    Martijn Theuwissen: sure to both. There are statistics scientists at every company. Instituting a mentor program, for instance, mixed with a continuous learning curriculum can significantly enhance records fluency throughout a company.

    And this is not an option — it’s an integral. information is king in company. facts science is a means through which which you can use statistics to make company decisions. devoid of the fundamental statistics science skills, personnel can’t make these vital decisions. 

    As your group becomes greater at ease with the language of data, they’ll be more comfortable bringing statistics to bear on important business decisions. it will develop into clear that some crew individuals are extra comfy using facts potential than others are. encourage the proficient ones to mentor others. Even at DataCamp, where statistics science is our company, some americans don’t work with records constantly. after they need assist on a fancy issue, they pair up with people that do. 

    SEE: how to fail as an information scientist: 3 general error (TechRepublic)

    It’s all about shared tools, abilities and obligations — they can dramatically enhance communication and figuring out between employees, which ultimately improves place of work subculture.

    Scott Matteson: Can employees be expert in statistics science? 

    Martijn Theuwissen: completely. however first, corporations should create consciousness that data science nowadays isn't unique to records scientists. basically, many initiatives at businesses require some stage of facts science—finance, advertising, operations, and HR, simply to identify just a few. It’s a cultural problem as tons as a expertise problem. 

    2nd, companies need to enforce upskilling initiatives that healthy the way of life of their personnel. solutions like DataCamp that supply on-demand and interactive getting to know options were certainly constructed for busy individuals. This displays a primary shift in the upskilling and reskilling initiatives taking location in lots of industries. We’re seeing a transition from L&D functions developing in-person training fabric to them, curating personalized content for their personnel the usage of online substances.

    SEE: Oracle using information science to provide agents an intelligence aspect (TechRepublic)

    most importantly, don’t take your foot off the gasoline pedal. learning isn’t a one-off, mainly in a dynamic house like records science. be certain the programs you’ve carried out are repeatable and that you just’re measuring success and boom. within the future of work, continual learning is the norm. The variety of tools developed and skills essential to clear up true business problems is becoming at once. We’ve entered an age where chronic gaining knowledge of is fundamental to staying professionally central. this is genuine frequently, however even more so in the records world.

    Scott Matteson: Do records scientists need a Ph.D.? 

    Martijn Theuwissen: There aren't any shortcuts to writing code, however with apply, anyone can construct the skills mandatory to solve complications using records, specifically with the right training tools.

    for instance, one in every of our employees pivoted from account executive to records scientist the use of DataCamp. We’ve also heard equivalent studies from our valued clientele. you then have examples of accepted records scientists devoid of formal levels. Cloudera Co-founder Jeff Hammerbacher, election forecaster Nate Silver (of FiveThirtyEight), and Moneyball mind Paul DePodesta are three that come to mind.

    here's no longer to claim there isn’t value in having a school degree in facts science. in reality, we supply DataCamp subscriptions without charge to many universities because we stand for democratizing information science, despite the training medium.

    Scott Matteson: Is being a data scientist about the ability, dedication, figuring out, or training? a mixture? 

    Martijn Theuwissen: a major a part of being an exceptional statistics scientist, which matches past having any form of diploma or practising application, is figuring out a way to conduct conversations and ask the right questions round such themes as:

  • statistics generation, collection, and storage
  • What information appears and seems like to records scientists and analysts,
  • Statistical intuition and common statistical pitfalls
  • mannequin building, laptop researching, and artificial intelligence (AI)
  • The ethics of data, big and small
  • Monica Rogati, who’s a total rock celebrity in our field, wrote an excellent article on this subject referred to as records Science Hierarchy of wants that’s worth seeking out. I’m biased, of path, but I additionally totally recommend our fresh data Science for business Leaders course to be taught more.

    SEE: true 5 issues to find out about data science (TechRepublic)

    Scott Matteson: are you able to describe what the day by day actions of an information scientist are, the usage of subjective examples?

    Martijn Theuwissen: today’s information scientists add price on an everyday foundation through conducting facts assortment and facts cleansing; developing dashboards and building experiences; records visualization; statistical inference; communicating outcomes to key stakeholders; and providing quantifiable facts to resolution makers on their consequences. 

    statistics scientists within the tech business now understand how records science works and the cost it offers. They start everyday with the aid of inserting an outstanding facts foundation in vicinity–one in order to conduct amazing analytics. From there they make the most of online experiments and other methods so one can effect in sustainable increase. last, however not least, they construct laptop getting to know pipelines and customised statistics products to help them benefit a improved knowing of their company and purchasers and make more advantageous choices.

    Scott Matteson: How long would it not take for someone to be taught the change and launch a career in facts science?

    Martijn Theuwissen: an inexpensive estimate is spending six months committed to studying full time and finishing projects. this may additionally consist of writing them out in Jupyter / R Markdown notebooks. The work may still also be posted on github and a personal blog. All of that work would equip someone well for an entry-stage place like junior statistics analyst or junior statistics scientist. From that aspect on, the key is continuous gaining knowledge of that comprises all the latest tools, thoughts, concepts, communications, and questions.


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