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Microsoft 98-388 : Introduction to Programming Using Java Exam

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Exam Number : 98-388
Exam Name : Introduction to Programming Using Java
Vendor Name : Microsoft
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EXAM ID : 98-388
EXAM NAME : Introduction to Programming Using Java
Candidates for this exam are application developers working with Java 6 SE or later, secondary and immediate post-secondary level students of software development, or entry-level software developers.

Candidates should have at least 150 hours or instruction or hands-on experience with Java, are familiar with its features and capabilities, and understand how to write, debug and maintain well-formed, well documented Java code.

- Understand Java fundamentals (15-20%)
- Work with data types, variables, and expressions (40-45%)
- Implement flow control (15-20%)
- Perform object-oriented programming (10-15%)
- Compile and debug code (5-10%)

Understand Java fundamentals (15-20%)
Describe the use of main in a Java application
=> signature of main, why it is static; how to consume an instance of your own class;
command-line arguments
Perform basic input and output using standard packages
=> print statements; import and use the Scanner class
Evaluate the scope of a variable
=> declare a variable within a block, class, or method
Work with data types, variables, and expressions (40-45%)
Declare and use primitive data type variables
=> data types, including byte, char, int, double, short, long, float, boolean; identify when precision is lost; initialization; how primitives differ from wrapper object types such as Integer and Boolean
Construct and evaluate code that manipulates strings
=> string class and string literals, comparisons, concatenation, case and length; String.format methods; string operators; converting a primitive data type to a string; the immutable nature of strings; initialization; null Construct and evaluate code that creates, iterates, and manipulates arrays and array lists
=> one- and two-dimensional arrays, including initialization, null, size, iterating elements, accessing elements; array lists, including adding and removing elements, traversing the list
Construct and evaluate code that performs parsing, casting and conversion
=> implementing code that casts between primitive data types, converts primitive types to equivalent object types, or parses strings to numbers Construct and evaluate arithmetic expressions
=> arithmetic operators, assignment, compound assignment operators, operator precedence
Implement flow control (15-20%)
Construct and evaluate code that uses branching statements
=> if, else, else if, switch; single-line versus block; nesting; logical and relational operators Construct and evaluate code that uses loops
=> while, for, for each, do while; break and continue; nesting; logical, relational, and unary operators
Perform object-oriented programming (10-15%)
Construct and evaluate a class definition
=> constructors; constructor overloading; one class per .java file; this keyword; inheritance and overriding at a basic level
Declare, implement, and access data members in a class
=> private, public, protected; instance data members; static data members; using static final to create constants; describe encapsulation
Declare, implement, and access methods
=> private, public, protected; method parameters; return type; void; return value; instance methods; static methods; overloading
Instantiate and use a class object in a program
=> instantiation; initialization; null; accessing and modifying data members; accessing methods; accessing and modifying static members; importing packages and classes
Compile and debug code (5-10%)
Troubleshoot syntax errors, logic errors, and runtime errors
=> print statement debugging; output from the javac command; analyzing code for logic errors; console exceptions after running the program; evaluating a stack trace Implement exception handling
=> try catch finally; exception class; exception class types; display exception information

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Microsoft Introduction information source

Porsche revs up its use of Splunk to tame Apache Kafka Streamzilla | 98-388 exam Questions and Free PDF

(graphic by means of Artist and zabiyaka from Pixabay )

Porsche needs little introduction, being one of the vital noted luxurious automobile producers on earth. although, plenty like the rest of the automobile business, Porsche is having to blend its actual property with the digital world in a bid to deliver wrap-round features for customers, together with monitoring of vehicle efficiency and customized experiences. 

This requires connecting motors with sensors to create digital twins, which move facts lower back to Porsche HQ. not handiest this, but Porsche's production lines are increasingly connected in a bid to enhance high-quality and efficiency. These statistics streams are used via a lot of Porsche product teams and as they turn into greater critical to the car company's company, require cautious monitoring and assist. 

Enter Streamzilla - the centrally managed carrier run via Sridhar Mamella, facts Streaming supervisor at Porsche, which uses open supply application (Apache Kafka) to seize all of Porsche's streaming information needs. however as Streamzilla has grown alongside Porsche's digital capabilities, so has the should computer screen the statistics logs, in a bid to enrich reliability and guide. 

in consequence, Porsche is the use of Splunk to tame its growing Apache Kafka property, bringing it below handle and offering inside product teams with a carrier that is predictable and has high availability. 

speaking at Splunk's annual person conference, .conf, leader of the venture, Mamella explained Porsche's considering in the back of the company's Kafka implementation. He said: 

Our intention is to carry the Porsche experience into the digital future. That being pointed out, this requires a robust infrastructure and a solution at scale. At Porsche, teams crucial whatever thing to decouple their architecture and accelerate the information flow. They appeared a ways and they regarded huge, after which they found out the problem may well be solved via a expertise referred to as Apache Kafka. 

it be a allotted system, originally developed at LinkedIn, it's used for facts streaming, log storage and circulate processing. What's so cool about that? Kafka's main improvement, aside from enabling the decoupled transportation of information in precise time, is its ability to scale. so you can now quite simply add or get rid of Kafka brokers from your device automatically and alter your workload. This capability additionally makes it incredibly obtainable. 

Mamella explained that just a couple of years in the past, Porsche had a number of groups the usage of impartial Kafka clusters, all with their own use cases, managed on their personal. This become working, but for doing whatever thing like creating digital twins of all Porsche vehicles, this requires records being despatched from dozens of programs from the world over. This become unsustainable within the existing deploy and Porsche decided to consolidate. He mentioned: 

An out of date digital twin has no price. So updating the current edition needed to turn up and in a authentic approach. They had distinctive product groups the usage of Kafka, but most likely keeping a data flow solution was now not their core business, so it's the place they ended up with diverse clusters, most of them were overlooked, which in the end don't have been most advantageous for a client that expects things to be best…just like their Porsche. 

A principal service

As stated above, the decision became made to build a central records streaming platform, in line with Apache Kafka, referred to as Streamzilla, which might act as a shared carrier company inside Porsche. Alongside the platform, the Streamzilla group also offers consulting services and open documentation for Kafka advantage. Mamella referred to: 

This intended each technological and organizational change. They act because the mediator between product groups when they need access to streaming statistics, a totally new approach with a lot of skills and lots of pondering ahead of the time. 

The information method is now to allow the usage of facts alongside information domains, permitting a number of product teams and quite a lot of departments to make use of and enrich the information when they want it. a perfect instance will be the implementation of digital twins. This enables the organization to introduce new methods with optimized nice, minimized time to market, even as the usage of a consistently up-to-date and managed solution. 

Mamella and his group developed the platform from scratch the use of open supply accessories, with a purpose to evade vendor lock-in. The add-ons protected: Kafka brokers, ZooKeeper nodes, MirrorMakers, Proxies, software Layer Gateways. 

Mamella observed that managing this become effortless sufficient originally, as Porsche changed into best the usage of one among each and every of those add-ons and had four Docker containers. He said: 

That changed into fairly manageable within the delivery. They might identify errors, they could act on them, they could provide an exquisite respectable provider. however as the platform grew, more and more add-ons have been brought, like Kafka join, a rest proxy, the schema registry, and much more components from the Lafka ecosystem that would add cost to the conclusion consumer (product teams)

This number of accessories turned into extraordinarily hard for us to sustain with. They had the most excellent crew, the greatest builders and engineers, however nevertheless every now and then it was extraordinarily tough to identify and act on error in due time. we're speakme about production stage techniques, replicated all across the globe, with true-time statistics coming in. And now with out the correct device, it turned into starting to get tedious. that's the point where they determined to make use of Splunk. 

One device to rule all of them

Splunk changed into implemented with a purpose to display screen the entire open supply stack. Porsche already had an in-condo Splunk team, so getting them in to deploy the service and construct dashboards might happen fairly quickly, in response to Mamella. He introduced: 

The requirements for monitoring grew, which became quite simply managed by Splunk. however after a while, they realised it be no longer best significant for us, but also the inner consumer too. Reliability and authentic systems are always helpful. So they wanted to provide direct cost to them, so we, as a platform company, deploy monitoring for his or her systems. They deploy signals so that some thing records is coming in for them, monitoring whether it is best or no longer. 

many of the time they vital analysis when asking their operations team why that they had errors the entire time, what tasks vital to be done whereas that they had these mistakes - this time awaiting reply might quite simply be eliminated by featuring the fundamental information as self carrier, which is at the core. 

The Streamzilla crew deploy alerting for ACLs, access handle lists inside Kafka. So if any mistakes took place, the corresponding subject matter owner would be contacted by using the Splunk alerting gadget. Mamella delivered: 

protection related suggestions changed into also extremely valuable for the consumer. As that you may think about for an organization like Porsche, they take protection very severely. So the interior client would acquire blunders, or error logs, or protection counsel at their fingertips. 

We additionally supplied functionality the place they could see the Kafka purchaser records, they added monitoring for replication errors, so connectivity problems and misconfigurations can be effectively recognized. Getting all this suggestions helped us identify areas that may wish some growth, however on the equal time merits the customer, which is an immeasurable improvement.

in view that imposing Splunk, Porsche has seen many advantages. for instance, it has integrated the Splunk alerting device into Microsoft teams, which capacity that engineers get signals automatically when an error happens and can take motion, at all times inside seconds. This has resulted in sooner decision instances - an eighty five% reduction in imply time to resolution, really. Mamella introduced: 

but the time except they seen incidents is rarely the simplest point - the logs are provided in an effectively comprehensible layout, which frees up critical supplies and allows us to center of attention on establishing the platform and setting up company price. and eventually by using minimising operational overhead, they might liberate the time of the Streamzilla crew, which would had been blocked up by way of having to identify concerns and the options to that (26% improved charge efficiency). 

A system like Splunk capability you have every single piece of counsel you need at a single look. 

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