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Which Skills Should You Master Before Taking Microsoft 70-778 Exam? Use Practice Tests to Get Them Faster!

The Microsoft 70-778 exam is part of the fulfillment for the award of the MCSA: BI Reporting certification. Configure Dashboards, Apps, & Reports in BI

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Master Before Taking Microsoft 70-778 Exam

The Microsoft 70-778 exam is part of the fulfillment for the award of the MCSA: BI Reporting certification. The candidates with this credential have the capacity to manipulate data in an organizational environment. Those professionals who can take the test and pursue the credential include data analysts, BI experts, and other job roles that require responsibility for the creation of reports with the use of Power BI.

Those individuals who want to take Microsoft 70-778 are required to have a good understanding of the process involved in using Power BI to carry out data analysis. They should also be proficient in implementing data transformations, linking to data courses, visualizing and modeling data through the use of Microsoft Power BI Desktop. They also need to know how to configure dashboards using Power BI service.

Microsoft 70-778

In addition to this, the test takers should know about the implementation of direct connectivity to SQL Server Analysis Services and Microsoft SQL Azure. They also need to know all about executing data analysis using Microsoft Excel. The topics covered in the Microsoft 70-778 exam include consuming and transforming data using Power BI Desktop, configuring dashboards in Power BI Service, and modeling and visualizing data. We will explore these objectives shortly.

1. Why Power BI?

Power BI refers to a suite of enterprise analytics tools designed to analyze data and share insights. The dashboards of Power BI offer a 360-degree view for the enterprise users giving them the most critical metrics from a single place. It can be updated in real time and is available on any device.

With a single click, the users can explore data on their dashboard with the use of seamless tools that help with easy access to information.It is pretty simple to create a dashboard due to the numerous available connections to well-known enterprise applications, and it comes with total pre-built dashboards that help the individuals get to work immediately.

For the professional data analysts who have the job role of performing analytics and reporting to their organizations, Power BI offers a tool that makes your job easier and effective. Power BI service securely publishes reports to your company and installs automated data refresh to enable every team member access to the latest information in the organization.

Earning the MCSA: BI Reporting credential is very strategic; especially if your organization has begun to implement Power BI for data analytics. If you are thinking of pursuing this certification, it is important that you know more about it and what it entails. To obtain MCSA: BI Reporting, you will need to pass two exams. These are Microsoft 70-778 and Microsoft 70-779. In this article, we will cover the details of the first test that you need to pass. Let’s dive into it.

Microsoft 70-778 Exam Tests

2. Microsoft 70-778 Exam Details

Before attempting Microsoft 70-778 dumps , you must develop capacity in the usage of Power BI and Excel to design, deploy, and develop solutions using Power Query, Power Pivot, Power Map, and Power BI.As for the structure, the certification test is made up of about 50 multiple-choice questions, and the candidates have 120 minutes for the completion. There are also some scenario-based questions that you should expect in the exam. Other possible types that you can encounter mentioned on the official Microsoft website. There are even video tutorials on how to answer one or another.

To pass the Microsoft 70-778 certification test, you have to score at least 700 points on a scale of 100 to 1000. Depending on your location, you can take the exam online or at a testing center. You should check the certification page to know which one you can take in your country of residence. You can also take an online proctored test.This variant allows you to take this exam wherever you want. You just need a webcam and a microphone so that the proctor can see you.

3. Microsoft 70-778 Exam Objectives

This Microsoft exam evaluates specific skills. That is why the students must be able to demonstrate a level of competence in the topics to be able to go through them in the actual test. In this section, we will look at the objectives that the candidates need to study before the very exam.

3.1. Combine& Transform Data with the Use of Power BI Desktop

This exam objective requires that the individuals learn how to connect to data sources, perform transformations, and cleanse data. It may include connecting to folders, files, and databases; importing from Excel; connecting to Big Data, SAAS (SQL Server Analysis Services), and SQL Azure. It also covers the design & implementation of fundamental & advanced transformation; changing a data format for support visualization; applying business rules. This topic also requires that the candidates manage incomplete data and fulfill data quality prerequisites.

3.2. Model & Visualize Data

The applicants will be evaluated on optimizing and creating data models. This includes managing relationships; manually type in data; using Power Query; optimizing models for reporting. It also entails creating calculated tables, columns, and measures. The test takers should also understand all about measuring performance with cards, KPIs, and gauges; creating hierarchies; creating and formatting interactive visualizations; managing custom reporting solutions.

3.3. Configure Dashboards, Apps, and Reports in Power BI Service

This covers a number of components and the students must develop skills in each area. The candidates are required to develop capacity in accessing on-premises data; configuring a dashboard; publishing and embedding reports; configuring apps & apps workspace. Competence in these areas will increase the chance of success in the Microsoft 70-778 exam.

4. Conclusion

When looking for prep materials, the Microsoft learning platform is the first place for you to start. You can go through various recommended study tools to start your preparation. Another platform you should consider for your preparation process is Exam-Labs.

This website offers various study materials, including training videos, practice tests, brain dumps, study guides, and a host of other tools to help you pass your Microsoft 70-778 certification test at the first attempt. You can use both sites to master your skills and knowledge and gain success in your self-improvement.

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Big Data

How to Work with Strings in Google BigQuery?

How to Work with Strings in Google BigQuery? Key Features of Google BigQuery, BigQuery String Functions, BigQuery Substring Function

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How to Work with Strings in Google BigQuery

Organizations nowadays produce a massive amount of data, simply storing and organizing, which is not enough and serves no purpose. It, therefore, becomes essential for businesses not just to collect and store the data but also to analyze it to derive valuable business insights.

Still, there comes a challenge of managing, maintaining, and analyzing these exponentially growing data using the outdated data warehouse technologies and here comes the role of Google BigQuery, one of the well-known and widely accepted Cloud-based Data Warehouse Applications, which allows you to trawl through vast amounts of data and find the correct data for analysis.

It offers various functions such as BigQuery Create Table, which helps store the records, BigQuery Substring, which eases down the complex calculations, and many others.

In this article, we will introduce you to Google BigQuery and its key features? We will also have an overview of the different BigQuery String Functions and BigQuery Substrings and how to work with them.

1. What is Google BigQuery?

Google BigQuery is a cost-effective enterprise data warehouse solution and part of Google Cloud’s comprehensive data analytics platform for business agility.

It helps businesses manage and analyze the data with the help of inbuilt features like Machine Learning, Business Intelligence, and Geospatial Analysis.

Google BigQuery’s serverless architecture allows high-scale operations and execution of SQL queries over large datasets.

It is an enterprise-ready cloud-native data warehouse that covers the whole analytics ecosystem, including ingestion, processing, and storage of data, followed by advanced analytics and collaboration, enabling scalable analysis of the stored data.

2. Key Features of Google BigQuery

Key Features of Google BigQuery

Given below are some of the key features of Google BigQuery: –

  • Scalability – Google BigQuery is quite elastic in that it manages a vast amount of data and allows upscale or downscale per demand.
  • Automated Data Transfer – Google BigQuery supports automated data transfer through the BigQuery Data Transfer Service, which automates data movement into BigQuery regularly.
  • Real-Time Analytics – Google BigQuery facilitates the analysis of high-volume data in real-time.
  • User-Friendly Interface – BigQuery is a highly user-friendly platform and requires just a basic understanding of SQL commands, ETL tools, etc.
  • Multicloud Functionality – Multicloud Functionality is another feature of Google BigQuery which allows data analysis across multiple cloud platforms. BigQuery can compute the data at its original location without moving it to different processing zones.

3. BigQuery String Functions

Strings are a crucial part of the dataset whose manipulation and transformation significantly impact your analysis. There are various functions to modify and transform the Strings in Google BigQuery. Let us have a look at some of the essential BigQuery String Functions: –

a) CONCAT –

The CONCAT function helps to combine two or more strings to provide a single result. Here all the values must be Bytes or Data Types; if any of the input arguments is null, then the function will return the null value.

Syntax: –

SELECT
CONCAT(‘A’, ” “, “B”)

b) TRIMMING –

The TRIMMING function removes any particular character from the String. Trimming operations are of three types: –

c) TRIM (value1[, value2]):

TRIM removes all the leading and trailing characters that match value2. In case no character is specified, whitespaces will be removed by default.

d) LTRIM (value1[, value2]):

LTRIM Function removes the character specified from the left, and similar to the TRIM Function, if a character is not defined, it will remove the whitespaces by default.

e) RTRIM (value1[, value2]):

RTRIM Function removes the character specified from the right side, and again if no character is defined, then whitespaces will be removed by default.

Example: –

SELECT
‘Original String_’,
TRIM(‘ Original String_’) AS trimmed,
LTRIM(‘ Original String_’) AS left_trim,
RTRIM(‘ Original String_’, “_”) AS right_trim

f) REPLACE –

The REPLACE function can replace all the substrings within a string with new substrings.

Example: –

SELECT complaint_description,REPLACE (complaint_description,’Coyote’,’doggy’)as replaced_value FROM `bigquery-public-data.austin_311.311_service_requests` LIMIT 5
Here “Coyote” will be replaced with “doggy”.

g) CASE FUNCTIONS –

CASE functions are used to change the case of a particular string, and they are of two types LOWERCASE and UPPERCASE.

h) LOWERCASE –

LOWERCASE can be used to return the original String with all the alphabetic characters in the lower case for string arguments.

Syntax: –

LOWER(value)

I) UPPERCASE –

UPPERCASE can be used to return the original String with all the alphabetic characters in the upper case for string arguments.

Syntax: –

UPPER(value)

4. BigQuery Substring Function

BigQuery Substring Function helps to extract a section of the String in BigQuery. It helps make calculations and visualizations easier for the users and can be used in conjunction with other BigQuery parameters, which can help enhance the performance.

Syntax: –

SUBSTR (value, position[, length])

Conclusion

In this article, we discussed Google BigQuery and the key features that make it useful for businesses. We also discussed different BigQuery String Functions, which can be used to transform and manipulate strings in BigQuery, such as CONCAT, TRIMMING, etc., along with their syntax.

In the end, we will also discuss the BigQuery Substring function, which helps extract a section of the String in BigQuery and thus helps enhance the performance.

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