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DP-203 exam dumps - Data Engineering on Microsoft Azure | Braindumps

Microsoft DP-203 : Data Engineering on Microsoft Azure Exam Dumps

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Exam Number : DP-203
Exam Name : Data Engineering on Microsoft Azure
Vendor Name : Microsoft
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DP-203 Exam Format | DP-203 Course Contents | DP-203 Course Outline | DP-203 Exam Syllabus | DP-203 Exam Objectives

Test Detail:
The DP-203 exam, Data Engineering on Microsoft Azure, is designed to validate the skills and knowledge of data engineers working with Azure technologies for data storage, processing, and analytics. The exam assesses candidates' abilities to design and implement data solutions using various Azure services and tools.

Course Outline:
The course for DP-203 certification covers a wide range of topics related to data engineering on Microsoft Azure. The following is a general outline of the key areas covered:

1. Introduction to Data Engineering on Azure:
- Understanding the role of a data engineer in Azure environments.
- Overview of Azure data services and their capabilities.
- Familiarization with data engineering concepts and best practices.

2. Data Storage and Processing:
- Azure data storage options, including Azure Storage, Azure Data Lake Storage, and Azure SQL Database.
- Implementing data ingestion and transformation using Azure Data Factory.
- Introduction to big data processing with Azure Databricks and HDInsight.

3. Data Orchestration and Integration:
- Implementing data orchestration workflows with Azure Logic Apps.
- Integration of data from various sources using Azure Synapse Pipelines.
- Familiarization with Azure Event Grid and Azure Service Bus for event-driven data processing.

4. Data Governance and Security:
- Implementing data security and compliance measures in Azure.
- Configuring access controls and encryption for data at rest and in transit.
- Understanding data privacy, governance, and auditing in Azure.

5. Data Analytics and Visualization:
- Introduction to Azure Synapse Analytics for data warehousing and analytics.
- Implementing data analytics solutions using Azure Analysis Services and Azure Power BI.
- Familiarization with Azure Machine Learning for predictive analytics and machine learning models.

Exam Objectives:
The DP-203 exam evaluates the candidate's knowledge and skills in the following key areas:

1. Designing and implementing data storage solutions on Azure.
2. Implementing data integration and orchestration workflows.
3. Configuring and managing data security and compliance measures.
4. Implementing data processing and analytics solutions.
5. Monitoring, troubleshooting, and optimizing data solutions on Azure.

Exam Syllabus:
The exam syllabus for DP-203 provides a detailed breakdown of the topics covered in each exam objective. It includes specific tasks, tools, and concepts that candidates should be proficient in. The syllabus may cover the following areas:

- Designing and implementing Azure data storage solutions
- Data ingestion, transformation, and orchestration using Azure Data Factory
- Data security, privacy, and compliance measures on Azure
- Configuring and optimizing data processing workflows
- Implementing data analytics and visualization solutions

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Microsoft Data Study Guide


Top 5 Must-Have Apps for Students on Windows

Top 5 Must-Have Apps for Students on Windows

New Delhi, India - August 18, 2023 —

Image credit - Go Guardian

Are you a student searching the internet environment for resources that will accelerate your academic progress? Look nowhere else! Windows applications have developed into essential tools for students in this technologically advanced age. Streamlining activities, increasing efficiency, and improving educational opportunities. 

The way you study, interact, and achieve is set to change thanks to these 5 windows apps for students. Which may help you create top-notch assignments and organise research treasures. Let's explore the realm of digital empowerment and learn about the essential applications. That will make your life as a student not only bearable but actually extraordinary.

Boost Your Studies: Top 5 Windows Apps Every Student Needs

Here are five crucial Windows applications that students may use to their advantage in their academic endeavours:

1. Microsoft Office Suite (Word, Excel, PowerPoint, OneNote)

Word, Excel, PowerPoint, and OneNote are all part of the Microsoft Office Suite. The pupils really need to have this set of programmes. Writing essays and reports, using Excel to manage data and create spreadsheets, making presentations using PowerPoint. Keeping digital notes in OneNote are all effective uses of Microsoft Word. Students may use these tools to keep organised, produce polished papers, and work with classmates on group assignments.

2. Google Chrome

For research, online tasks, and resource access, a dependable and feature-rich web browser is crucial. Grammar checks, citation makers, and note-taking tools. These are just a few of the productivity-boosting extensions and applications available for Google Chrome. You can access your bookmarks and settings from one device to another thanks to its synchronisation ability.

3. Notion

Hypothesis An all-in-one workspace tool called Notion combines functionality for note-taking, task management, and collaboration. It’s ideal for making project plans, to-do lists, class timetables, and study guides. Notion is an effective tool for boosting individual and team productivity because of its versatility. Which enables you to tailor layouts to meet your chosen organisational approach.

4. Zotero

Students may organise and cite sources for research papers and other assignments. With the aid of the free reference management tool Zotero. You may gather, arrange, and reference sources from many websites and databases with this tool. Additionally, Zotero interfaces with word programmes like Microsoft Word to make it simpler to add citations. Also build bibliographies using various citation formats.

5. Anki

Anki is a potent flashcard programme that makes use of spaced repetition to speed up learning and memory retention. To help them remember ideas, terminology, and important information, students may make digital flashcards for a variety of courses. Based on your comments, Anki's algorithm modifies the timing of card evaluations to enhance learning.

Tips for Efficient Use of the Windows Applications for Students

Here are 3 best tips to be considered:

1. Keyboard Shortcuts Mastery: Take the time to get familiar with important Windows keyboard shortcuts. You'll spend less time exploring menus as a result, which will improve your productivity. Your workflow may be greatly accelerated, for example. By utilising "Ctrl+C" to copy, "Ctrl+V" to paste, and "Alt+Tab" to transition between active apps.

2. Desktop Organisation: Utilise a clear, uncluttered background and folder organisation to keep your desktop free of clutter. Use virtual desktops to divide up various jobs or projects. Decreasing visual distractions, and pin commonly used programmes to the taskbar for fast access.

3. Scheduled Backups and Updates: Use Windows' built-in utilities or third-party applications to schedule routine automated backups of your crucial information. Enable automatic Windows updates to keep your system updated with the most recent security fixes and performance enhancements.This avoids any data loss and maintains the efficiency of your machine.


Utilise technology to your advantage and elevate your student experience with these 5 windows apps for students. Each programme provides a different benefit that may improve your productivity and learning. From mastering organisation with Notion to slaying tasks with Microsoft Office Suite. Therefore, these tools may help you take notes, work on projects together, or maintain organisation. Bid stress farewell and welcome to a more productive and fruitful academic experience. You'll be glad you took use of these fantastic resources in the future. 

To read more about the latest tech and Business related news and tips, you can visit FirstTechSpot. 

Contact Info:Name: Amit KumarEmail: Send EmailOrganization: Tech MagazinesAddress: RZ 107 A, Sukhi Ram Park, Uttam Nagar New Delhi, India- 110059Phone: +91 7678210369Website:

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The science of forecasting ever more extreme weather

The world’s weather is presenting a paradox. In many regions, conditions are unprecedented, different to anything seen before. Yet they are also more predictable than ever.

This summer’s extreme heat across Europe and the US was foretold days in advance. Last year, four days before Britain experienced 40C for the first time, the UK’s national weather service, the Met Office, issued a red warning — announcing an 80 per cent chance of record-breaking temperatures.

“The actual forecasting, and deciding how to message the forecast, was one of the easier things we had to do,” says Will Lang, the meteorological service’s head of situational awareness. “It was almost scary how clear-cut it was.”

Economists and political pollsters regularly see their predictions upturned by real-world outcomes. In contrast, weather forecasters have managed steady improvements in modelling a chaotic system.

The Met Office encapsulates this process. Remembered by many Brits for failing to forecast a 1987 storm that killed 18 people in the UK and four people in France, it is today ranked as one of the world’s best-performing public weather services. Based in Exeter, south-west England, and largely funded by the UK government, it says its four-day forecast is now as accurate as its one-day forecast was 30 years ago.

Much of the credit goes to the expanding network of satellites, which provide data on cloud cover, humidity, wind and other variables, and increases in computing power, which allow more complex calculations. Already a five-day forecast in the US is accurate about 90 per cent of the time.

Better forecasts are giving governments and companies more opportunity to mitigate the costs of heatwaves and extreme weather, which are already large enough to be seen in gross domestic product figures. So far this year, the US has experienced 12 weather-climate events costing more than $1bn, including 10 severe storms.

But even “normal” weather causes all manner of shortfalls and inconveniences, from failed crop harvests to delayed flights. Increasingly accurate forecasting has the potential to improve performance across a swath of sectors.

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Abundant data has created new opportunities for specialised or “hyper local” forecasts, mostly for business. Since the weather holds huge sway over things like heating oil and natural gas, when Citadel, the world’s largest hedge fund, hired Sebastian Barrack to expand its commodities trading in 2017, one of his first moves was to hire a 20-strong team of weather forecasters. Citadel now credits that forecasting team for much of its success in commodities trading, which accounted for about half of its $16bn in returns last year.

Despite leaps in forecast accuracy, blind spots remain. Forecasters struggle with certain weather phenomena: localised thunderstorms, which can trigger flash flooding, are still at the outer limits of their abilities. Google, Nvidia and Huawei are among the technology companies developing AI-based forecasting, which aims to bridge some of the gaps.

Not to be outdone, the Met Office is also investing in machine learning and supercomputing power. Beyond being the UK’s national service and archive, it provides its forecasts to other countries and sells insights to private clients in fields as wide-ranging as aviation, retail, construction, agriculture, mining and insurance.

The investment has already aided the Met Office in its most important task: forewarning. The 1987 storm, which the office forecast would hit France and Spain rather than the UK, was difficult to predict because of a sting jet — a burst of powerful winds lasting just a few hours.

Today, its forecasters would be able to identify the storm hitting the UK as a low probability event, allowing a warning to be issued. “We work on a mantra of ‘no surprises’,” says Lang.

How forecasters observe the weather

Meteorologists bring together observation data from scores of sources and use computer models and supercomputers to make predictions hours, days, weeks and seasons ahead. Tiny changes in the Earth’s inherently chaotic system can lead to big changes in weather patterns, so the observations are continually updated. The UK’s Met Office uses about half a million observations a day


Geostationary satellites hover above the Earth at around 36,000km, making observations 24 hours a day. They monitor infrared radiation from the Earth’s surface and atmosphere. They also track cloud cover for indirect information on wind speed and direction

Polar orbiting satellites orbit at a much lower altitude, around 850km above the Earth. These satellites provide information on snow and ice cover and can help track fires and volcanic eruptions due to the better resolution of their images

Between space and the ground are the layers of our atmosphere, the exosphere, thermosphere, mesosphere and stratosphere. We live in the troposphere, which extends upwards to about 10km above sea level and is where we find most of the weather

Weather balloons

Hundreds of weather balloons are released each day. They can operate at heights of 50km and can withstand temperatures of up to minus 95C. They carry instruments to measure air temperature, pressure and humidity. A radio signal sends the data back to Earth

Rossby waves

These are horizontal air currents in the jet stream, at roughly the same height as the cruising altitude for commercial aircraft. These 10,000km-long currents form when polar air moves towards the equator and tropical air moves poleward. Their existence is responsible for much of the weather in the middle and higher latitudes. They are monitored by meteorologists and are predictable for one to two weeks ahead

Radar systems, land stations and buoys

Networks of weather radar can detect types of particles – rain, snow, hail, even swarms of insects – in real time and map their intensity and motion. Land stations measure atmospheric pressure, cloud visibility and height, the wind, temperature and humidity. Buoys transmit similar data about the weather at sea, as well as wave height and sea surface temperature

‘Pushing against chaos’

The science of weather forecasting dates back to the mid-19th century. In 1854, Robert FitzRoy, formerly the captain of HMS Beagle, on which Charles Darwin sailed, founded the Met Office. Its initial role was to improve safety for ships, by gathering a few scattered observations and charting them.

By the 1920s, there was an attempt at more sophisticated prediction. Lewis Fry Richardson, a British mathematician, honed a series of equations, based on Newtonian physics, that could calculate how pressure, temperature and other variables interact.

Being an intrinsically chaotic system, the weather is highly sensitive to small changes in initial conditions: the proverbial butterfly can cause a hurricane. As a forecaster, “you’re pushing against chaos”, says Paul Davies, the Met Office’s chief meteorologist. It wasn’t until the 1960s that better data and computing power allowed a step-change.

Data quality is crucial. The UK has a few hundred observation stations containing meteorological instruments, often at airports. These are now complemented by sensors on aircraft, ships and buoys, as well as remote sensing by radar, satellite and weather balloons. (Satellites alone struggle to read the lowest part of the atmosphere.)

Everyday devices are now capable of feeding into forecasters’ models. “We’re not at the stage yet of using the temperature from your car, but most cars are measuring temperature and it would be technically quite easy to share that data,” says Douglas Parker, a professor of meteorology at the National Centre for Atmospheric Science, University of Leeds.

Weather forecasting also marks successful international co-operation. Through the UN World Meteorological Organization, established in 1950, countries agree to share their weather data.

In 1997, the Met Office worked with a grid of boxes 90km wide. That resolution has gradually shrunk to 10km today for the global grid. For the UK, the resolution is 1.5km. These boxes are stacked vertically, into the troposphere and the lower stratosphere. Meteorologists now debate whether there is an optimum resolution, below which the accuracy of forecasts no longer improves significantly. “There might be other ways that we can use our computer power to increase the accuracy,” says Helen Roberts, a Met Office forecaster.

One of those ways is so-called ensemble forecasting. Traditional models are “deterministic”: observations are plugged into a model, which provides a single forecast. In ensemble forecasting, the observations are tweaked in random ways, and the model is run multiple times simultaneously on the different data sets.

Helen Roberts and a temperature scale from 2022 Meteorologist Helen Roberts says human forecasters retain an important role alongside the computer models © Met Office

The random tweaks compensate for the limitations of the observations and of the model. They give a probabilistic account of different outcomes; reasonable worst-case scenarios can be picked up earlier. “Introducing randomness into this incredibly complex system actually improves it,” says Roberts. “It gives us a plausible range of scenarios.”

The Met Office’s global ensemble forecast currently uses 12 different scenarios. This involves vast amounts of computing power and electricity. By the end of 2025, the service will only use ensemble forecasting, enabled by one of the world’s largest supercomputers, initially budgeted at £1.2bn. (The supercomputer, based in Microsoft data centres and intended to process 60 quadrillion calculations per second, was due to be ready last summer, but was held back by Covid chip delays. The Met Office now expects it to be online in spring 2024.)

Human forecasters retain a role. They compare the models’ outputs to real weather observations and adjust them for biases. “For the time being, human meteorologists provide so much added value and insight above raw model data,” says Roberts, adding that their intervention is most necessary in unprecedented events.

But the models should perform robustly even as the world warms. Extreme weather is not necessarily hard to predict: July’s European heatwave involved a “loopy” jet stream, which allowed high pressure to settle for many days in five regions of the northern hemisphere. “We believe we’re solving equations of physics that are fundamental. So if we move into a hotter world, the equations should still be valid,” says Leeds university’s Parker.

Some places are easier to forecast than others. Britain has comparatively unpredictable weather because it sits off the edge of a continental landmass and next to a large ocean. It is affected by six air masses, large volumes of air, including ones from north Africa and Greenland that bring different levels of humidity and temperature.

But prediction is easier than in the tropics, where there are fewer observation stations, where the effect of the Earth’s rotation, a stabilising force on the weather, is weaker, and where thunderstorms can be frequent.

“In a chaotic system, there is a timescale over which [things] are no longer predictable,” says Parker. “We think for our mid-latitude world it is something like two weeks. For the tropics, it might be a day [at least for rainfall].”

The Met Office has meteorologists embedded with customers, particularly airlines and airports, who want tailored interpretation of forecasts. Demand for tailored services is likely to grow. As the UK’s share of renewable energy increases — from 11 per cent in 2012 to more than 30 per cent in 2022 — understanding how hard the wind will blow or the sun shine has become key to ensuring the lights stay on.

The UK’s National Grid Electricity System Operator is investing in increasing the frequency and quality of the forecasts it receives, including using machine learning. It is also working on analysis of satellite imagery of clouds to see if its solar radiation forecasts can become more accurate.

Whiffle, a specialist forecaster that grew out of a project at Delft University of Technology in the Netherlands, has taken the grid concept and supercharged it. Rather than focusing on 10km boxes, it says it can predict the weather based on areas as small as 10 metres squared.

This can give wind farm operators advance notice of how much they are likely to generate from individual turbines, aiding their trading in power markets. It can also help with the optimal placing of wind turbines within a bloc.

“The margins on these projects are not normally huge, generally in the region of 6-10 per cent,” says Remco Verzijlbergh, Whiffle’s co-founder. “So if the yield you get from the wind is lower than you hoped for, the economics can quickly blow up.” The company works closely with General Electric and Shell, among other customers.

Better weather ahead

Last month, exactly a year after accurately predicting record-breaking temperatures, the Met Office found itself under scrutiny — from England cricket fans. The fans wanted dry weather to enable England to try to beat Australia in an Ashes Test match in Manchester.

The Met Office suggested that no play would be possible on the third day of the Test, a Friday. A rival forecasting service, AccuWeather, offered a more optimistic view, which was quickly adopted by fans. The England team itself judged that an app called Home and Dry, which blends data from the Met Office and the European Centre for Medium-Range Weather Forecasts, was the most accurate guide. “It’s impossible to know with weather forecasts,” sighed a BBC radio commentator.

Overall, the Met Office’s forecast was largely accurate, and England’s rain-affected match finished in a draw. But the episode highlights one of the shortcomings of current weather forecasts: pinpointing exactly when and where rain will fall.

The physics behind rainfall is an “extremely non-linear process,” says Parker of Leeds university. Most rain starts as ice crystals or snowflakes and turns to rain as it descends within a cloud. It is affected by multiple factors, including interaction with other water in the clouds, the topography of the land below and the level of aerosols in the atmosphere, including salt from the sea and smoke from cities. It is so difficult to track rainfall that the Met Office mines social media data for mentions.

Given the difficulties in forecasting, attention is being paid to “nowcasting”: tracking rain over the extremely short term. In 2021, DeepMind, an artificial intelligence arm of Google, and the Met Office published a paper in the journal Nature suggesting that machine learning could outperform traditional, physics-based models at nowcasting rainfall within the next 90 minutes. “Two hours’, three hours’ time — that’s where the real power of machine learning could kick in,” says Davies, the Met Office’s chief meteorologist.

Paul Davies and a map of polar currents The chief meteorologist at the Met Office, Paul Davies, says machine learning could make it possible to do hyper-accurate short-term forecasting © Met Office/Open Government Licence

Machine learning has also been trialled over longer time horizons. Researchers at Nvidia said last year that their model was able to pick up extreme weather events such as hurricanes two to six weeks in advance. It required much less computing power than traditional forecasting, running 320 ensemble forecasts in just three minutes. Nvidia now claims its week-long forecasts outperform those of the European Centre for Medium-Range Weather Forecasts for variables such as precipitation.

“The interesting thing is how [AI-based forecasts] opens up for the private sector to compete with national weather services, because you don’t need the same infrastructure of scientists or computing experts to make that work,” says Parker.

But models trained on historic data may not be reliable in a changing climate. “If we have an AI that’s a statistical fit to the climate that we know, and then we have a different climate, it’s not so clear that that model will continue to be accurate. We’ve got no guarantees,” Parker adds. For example, the possible collapse of the Atlantic Meridional Overturning Circulation, the system of ocean currents that helps to stabilise Europe’s climate, could create unprecedented conditions and challenge any models that have not been trained for such scenarios.

Improvements in medium-range forecasts, whether by AI or other methods, are a focus for meteorologists. Such probabilities could help retailers to decide what to stock, for example, and farmers to decide what to plant. “We might not be able to make a really strong statement about what the weather’s going to be like this summer or this winter, but even if we just talk about a slight shift in the odds — a 10 per cent increased chance of it being a hotter summer — combined with a lot of other information that you’re getting, that might just be enough to tip the balance,” says the Met Office’s Lang.

Will Lang and a map of isobars from a storm in February 2023 Will Lang, the meteorological service’s head of situational awareness, says the Met Office works on a mantra of ‘no surprises’ © Met Office/Open Government Licence

But mid-range forecasts would not help individuals plan holidays or weddings. “You’re never going to be able to forecast your barbecue a month in advance,” says Parker.

Conveying such nuanced weather information is a challenge. Already the public has access to an array of weather apps, often with hourly forecasts and shiny maps that foster an illusion of certainty. The Met Office is studying how to give a sense of the probabilities involved.

With extreme events, communication can be counterintuitive. In 2017, a small but intense storm hit the Cornish coastal village of Coverack, causing flash floods. On the radar, “you could hardly see it — it was just a little point”, says Davies. As a result, people nearby were unaware and did not try to flee the area. “Had we produced the perfect warning, we might have lost lots of lives,” he adds. People nearby “might have rushed to the car and been drowned”.

Since the Coverack storm, the UK has endured a number of unprecedented weather incidents. In 2020, a study by the Met Office had concluded that, while hot summers were rising, “the current chance of seeing days above 40C is extremely low”. Two years later, it happened. Now the service says 40C temperatures “could be the norm” by the middle of the century, even if the world takes some steps to limit carbon emissions.

Experts emphasise that even accurate warnings are no substitute for long-term adaptation and mitigation strategies, which help society cope with intense heat and rainfall. “Underneath is climate change. It affects everything,” says Davies. “How do we communicate that, but also present it in such a way people don’t see us as nanny state?”

Graphic visualisation by Bob Haslett and Kari-Ruth Pedersen

This article has been amended since original publication to correct the time frame connected to Citadel’s commodities trading returns

Climate Capital

Where climate change meets business, markets and politics. Explore the FT’s coverage here.

Are you curious about the FT’s environmental sustainability commitments? Find out more about our science-based targets here

ChatGPT Was Given $20K To Invest in Stocks and Crypto — Here’s How It Made $2,000 in a Month

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DP-203 - Data Engineering on Microsoft Azure techniques
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