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Duration 4 Days 24 CPD hours This course is intended for System administrators System engineers Migration engineers Migration architects Overview By the end of the course, you should be able to meet the following objectives: Describe core VMware HCX use cases and common triggers for mobility Describe the core components and features of VMware HCX Describe a real-life example of a VMware HCX project Identify all major Cloud Providers offering and supporting VMware HCX Describe the features of VMware HCX services Explain the different deployment types for VMware HCX and choose the correct components to deploy for a particular use case Understand the resource, network, and VMware ESXi™ and VMware vCenter requirements for VMware HCX Install and configure VMware HCX Understand, deploy, and manage the HCX Service Mesh Understand Network Extension and Traffic Engineering Create Network Extension and enable Mobility Optimized Networking Understand WAN Optimization Understand the different migration types and be able to choose the best one for different applications and workloads Incorporate VMware HCX into a disaster recovery strategy Design a VMware HCX deployment for different use cases Manage the lifecycle of VMware HCX This four-day course gives you knowledge and practical exercises sufficient to manage VMware HCX© and to migrate virtual machines using VMware HCX. The course focuses on configuration and management of VMware HCX. The course equips system administrators with the knowledge, skills, and abilities to achieve competence in migrating virtual machines. Course Introduction Introductions and course logistics Course objectives Introduction to VMware HCX Describe workload mobility challenges that VMware HCX addresses Recognize use cases for VMware HCX Identify all major Cloud providers offering and supporting VMware HCX HCX Services and Deployment Types Describe the functions of VMware HCX components Recognize the services provided by VMware HCX Recognize when to use different deployment types for VMware HCX Be able to choose which components to install and configure for a different VMware HCX service HCX Deployment Identify the resource, network, and ESXi/VMware vCenter server requirements for VMware HCX Understand the VMware vCenter user roles and access requirements Describe the installation workflow Install, activate, and configure VMware HCX manager Understand the compute and network profile requirements for VMware HCX and its services Create site pair, compute, and network profiles Describe and manage the HCX Service Mesh Create and configure a HCX Service Mesh Network Extension Describe network extension use case and benefits Compare the HCX-Network extension service with other solutions Examine Network Extension capabilities and topology Create a Network Extension Describe network traffic packet flow Describe Mobility Optimized Networking Enable Mobility Optimized Networking Describe the TCP Flow Conditioning and Application path resiliency feature of VMware HCX Recognize the key benefits of TCP flow conditioning and Application path resiliency Describe WAN optimization Workload Mobility Describe different migration types Recognize the limitation of each migration method and consideration when planning a migration Understand Bulk and Replication Assisted VMware HCX© vMotion© migration methods Migrate a VM using Bulk migration Describe cold and vMotion migration method Migrate a VM using HCX vMotion migration Examine non VMware vSphere© workload migration Migrate a VM using an OS assisted migration method Business Continuity and Disaster Recovery Examine disaster recovery concepts Describe disaster recovery networks Plan and create disaster recovery networks Describe VM protection operations Protect, recover, and test recovery and reverse replication of a VM Understand VMware HCX and SRM integration and value HCX Lifecycle Management Backup and restore the VMware HCX manager Locate and access VMware HCX logs Troubleshoot VMware HCX Plan for VMware HCX manager and component updates Customer Design Scenarios Design a VMware HCX deployment Choose workload mobility methods for the migration Discuss customer requirements and proposed design Discuss components, services, and migration methods for the scenario

Duration 365 Days 2190 CPD hours The VMware Enterprise Learning Subscription delivers convenient and flexible training that lets your team train at their own pace ? when and where they want.This all-inclusive offering gives each licensed user 365 days of access to all content available through the Learning Zone, including all VMware On Demand courses. Subscribers also get a free VMware certification exam voucher. The mInimum ELS Subscription order is for 5 users. Content includes: 50+ Full On Demand courses with labs, equivalent to instructor led VMware course content 1,200+ videos 80+ eLearning short courses 500+ exam prep videos Certification exam voucher for each licensed subscriber Additional course details:Notes Delivery by TDSynex, Exit Certified and New Horizons an VMware Authorised Training Centre (VATC) Nexus Humans VMware Enterprise Learning Subscription (1-Year Term) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the VMware Enterprise Learning Subscription (1-Year Term) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 1 Days 6 CPD hours This course is intended for This introductory-level course is great for experienced technical professionals working in a wide range of industries, such as software development, data science, marketing and advertising, finance, healthcare, and more, who are looking to use the latest AI and machine learning techniques in their day to day. The hands-on labs in this course use Python, so you should have some familiarity with Python scripting basics. Overview Working in an interactive learning environment, led by our engaging OpenAI expert you'll: Understand the capabilities and products offered by OpenAI and how to access them through the OpenAI API. set up an OpenAI environment on Azure, including creating an Azure virtual machine and configuring the environment to connect to Azure resources. Gain hands-on experience building a GPT-3 based chatbot on Azure and implement advanced natural language processing capabilities. Use the OpenAI API to access GPT-3 and generate high-quality text Learn how to use Whisper to improve the quality of text generation. Understand the capabilities of DALL-E and use it to generate images for unique and engaging visuals. Geared for technical professionals, Quick Start to Azure AI Basics for Technical Users is a fun, fast paced course designed to quickly get you up to speed with OpenAI?s powerful tools and functionality, and to provide hands-on experience in setting up an OpenAI environment on Azure. Guided by our AI expert, you?ll explore the capabilities of OpenAI's GPT-3, Whisper and DALL-E, and build a chatbot on Azure. It will provide you with the knowledge and resources to continue your journey in AI and machine learning and have a good understanding of the potential of OpenAI and Azure for your projects. First, you?ll dive into the world of OpenAI, learning about its products and the capabilities they offer. You'll also discover how Azure's offerings for AI and machine learning can complement OpenAI's tools and resources, providing you with a powerful combination for your projects. And don't worry if you're new to Azure, we'll walk you through the process of setting up an account and creating a resource group. As you progress through the course, you'll get the chance to work with OpenAI's GPT-3, one of the most advanced large language models available today. You'll learn how to use the OpenAI API to access GPT-3 and discover how to use it to generate high-quality text quickly and easily. And that's not all, you'll also learn how to build a GPT-3 based chatbot on Azure, giving you the opportunity to implement advanced natural language processing capabilities in your chatbot projects. The course will also cover OpenAI Whisper, an OpenAI tool that can improve the quality of text generation, allowing you to create more coherent and natural language content. And you will learn about OpenAI DALL-E, an OpenAI tool that can generate images, giving you the ability to create unique and engaging visuals to enhance your content and projects. Introduction to OpenAI and Azure Explore OpenAI and its products, as well as Azure's offerings for AI and Machine Learning, allowing you to understand the tools and resources available to you for your AI projects. Explore OpenAI and its products Explore Azure and its offerings for AI and Machine Learning Get Hands-On: Setting up an OpenAI environment on Azure Walk through the process of setting up an OpenAI environment on Azure, giving you the hands-on experience needed to start building your own projects using OpenAI and Azure. Create an Azure virtual machine and installing the OpenAI SDK Configure the OpenAI environment and connecting to Azure resources Explore OpenAI GPT-3 Learn about GPT-3, one of OpenAI's most powerful language models, and how to use it to generate high quality text, giving you the ability to create natural language content quickly and easily. Review GPT-3 and its capabilities Use the OpenAI API to access GPT-3 Get Hands-on: Building a GPT-3 based chatbot on Azure Learn how to build a GPT-3 based chatbot on Azure, giving you the opportunity to learn how to implement advanced natural language processing capabilities in your chatbot projects. Setup an Azure Function and creating a chatbot Integrate GPT-3 with the chatbot OpenAI Whisper Explore Whisper, an OpenAI tool that can improve the quality of text generation, allowing you to create more coherent and natural language content. Explore Whisper and its capabilities Use Whisper to improve the quality of text generation OpenAI DALL-E Explore DALL-E, an OpenAI tool that can generate images, giving you the ability to create unique and engaging visuals to enhance your content and projects. Explore DALL-E and its capabilities Use the OpenAI API to access DALL-E What?s Next: Keep Going! Other ways OpenAI can impact your day to day Explore great places to check for expanded tools and add-ons for Azure OpenAI Where to go for help and support Quick Look at Generative AI and its Business Implications Understanding Generative AI Generative AI in Business Ethical considerations of Generative AI

Duration 1 Days 6 CPD hours This course is intended for This basic course is for: Business Analyst Systems Engineer Software Engineer Requirements Engineer Requirements Manager Requirements Team Leader Overview Build projects in DOORS, including defining data structure, linking schema, attributes, and access permissions Use DOORS external linking facilities Share DOORS information with 3rd parties Control the flow of changes through your DOORS database Apply configuration management and backup strategies to your DOORS data This course builds on the content learned in the IBM Engineering Requirements Management DOORS V9.6 Foundation course. It is designed for those who will be in the role of team lead or project manager, or who want to learn more about advanced DOORS end-user functionality. It discusses creating and structuring DOORS projects, defining linking relationships and attributes, setting access permissions, and managing change. It also discusses external linking, working with spreadsheets, and applying configuration management strategies to DOORS data. Course Outline Build projects in DOORS, including defining data structure, linking schema, attributes, and access permissions Use DOORS external linking facilities Share DOORS information with 3rd parties Control the flow of changes through your DOORS database Apply configuration management and backup strategies to your DOORS data

Duration 2 Days 12 CPD hours This course is intended for Audience: Data Scientists, Software Developers, IT Architects, and Technical Managers. Participants should have the general knowledge of statistics and programming Also familiar with Python Overview ? NumPy, pandas, Matplotlib, scikit-learn ? Python REPLs ? Jupyter Notebooks ? Data analytics life-cycle phases ? Data repairing and normalizing ? Data aggregation and grouping ? Data visualization ? Data science algorithms for supervised and unsupervised machine learning Covers theoretical and technical aspects of using Python in Applied Data Science projects and Data Logistics use cases. Python for Data Science ? Using Modules ? Listing Methods in a Module ? Creating Your Own Modules ? List Comprehension ? Dictionary Comprehension ? String Comprehension ? Python 2 vs Python 3 ? Sets (Python 3+) ? Python Idioms ? Python Data Science ?Ecosystem? ? NumPy ? NumPy Arrays ? NumPy Idioms ? pandas ? Data Wrangling with pandas' DataFrame ? SciPy ? Scikit-learn ? SciPy or scikit-learn? ? Matplotlib ? Python vs R ? Python on Apache Spark ? Python Dev Tools and REPLs ? Anaconda ? IPython ? Visual Studio Code ? Jupyter ? Jupyter Basic Commands ? Summary Applied Data Science ? What is Data Science? ? Data Science Ecosystem ? Data Mining vs. Data Science ? Business Analytics vs. Data Science ? Data Science, Machine Learning, AI? ? Who is a Data Scientist? ? Data Science Skill Sets Venn Diagram ? Data Scientists at Work ? Examples of Data Science Projects ? An Example of a Data Product ? Applied Data Science at Google ? Data Science Gotchas ? Summary Data Analytics Life-cycle Phases ? Big Data Analytics Pipeline ? Data Discovery Phase ? Data Harvesting Phase ? Data Priming Phase ? Data Logistics and Data Governance ? Exploratory Data Analysis ? Model Planning Phase ? Model Building Phase ? Communicating the Results ? Production Roll-out ? Summary Repairing and Normalizing Data ? Repairing and Normalizing Data ? Dealing with the Missing Data ? Sample Data Set ? Getting Info on Null Data ? Dropping a Column ? Interpolating Missing Data in pandas ? Replacing the Missing Values with the Mean Value ? Scaling (Normalizing) the Data ? Data Preprocessing with scikit-learn ? Scaling with the scale() Function ? The MinMaxScaler Object ? Summary Descriptive Statistics Computing Features in Python ? Descriptive Statistics ? Non-uniformity of a Probability Distribution ? Using NumPy for Calculating Descriptive Statistics Measures ? Finding Min and Max in NumPy ? Using pandas for Calculating Descriptive Statistics Measures ? Correlation ? Regression and Correlation ? Covariance ? Getting Pairwise Correlation and Covariance Measures ? Finding Min and Max in pandas DataFrame ? Summary Data Aggregation and Grouping ? Data Aggregation and Grouping ? Sample Data Set ? The pandas.core.groupby.SeriesGroupBy Object ? Grouping by Two or More Columns ? Emulating the SQL's WHERE Clause ? The Pivot Tables ? Cross-Tabulation ? Summary Data Visualization with matplotlib ? Data Visualization ? What is matplotlib? ? Getting Started with matplotlib ? The Plotting Window ? The Figure Options ? The matplotlib.pyplot.plot() Function ? The matplotlib.pyplot.bar() Function ? The matplotlib.pyplot.pie () Function ? Subplots ? Using the matplotlib.gridspec.GridSpec Object ? The matplotlib.pyplot.subplot() Function ? Hands-on Exercise ? Figures ? Saving Figures to File ? Visualization with pandas ? Working with matplotlib in Jupyter Notebooks ? Summary Data Science and ML Algorithms in scikit-learn ? Data Science, Machine Learning, AI? ? Types of Machine Learning ? Terminology: Features and Observations ? Continuous and Categorical Features (Variables) ? Terminology: Axis ? The scikit-learn Package ? scikit-learn Estimators ? Models, Estimators, and Predictors ? Common Distance Metrics ? The Euclidean Metric ? The LIBSVM format ? Scaling of the Features ? The Curse of Dimensionality ? Supervised vs Unsupervised Machine Learning ? Supervised Machine Learning Algorithms ? Unsupervised Machine Learning Algorithms ? Choose the Right Algorithm ? Life-cycles of Machine Learning Development ? Data Split for Training and Test Data Sets ? Data Splitting in scikit-learn ? Hands-on Exercise ? Classification Examples ? Classifying with k-Nearest Neighbors (SL) ? k-Nearest Neighbors Algorithm ? k-Nearest Neighbors Algorithm ? The Error Rate ? Hands-on Exercise ? Dimensionality Reduction ? The Advantages of Dimensionality Reduction ? Principal component analysis (PCA) ? Hands-on Exercise ? Data Blending ? Decision Trees (SL) ? Decision Tree Terminology ? Decision Tree Classification in Context of Information Theory ? Information Entropy Defined ? The Shannon Entropy Formula ? The Simplified Decision Tree Algorithm ? Using Decision Trees ? Random Forests ? SVM ? Naive Bayes Classifier (SL) ? Naive Bayesian Probabilistic Model in a Nutshell ? Bayes Formula ? Classification of Documents with Naive Bayes ? Unsupervised Learning Type: Clustering ? Clustering Examples ? k-Means Clustering (UL) ? k-Means Clustering in a Nutshell ? k-Means Characteristics ? Regression Analysis ? Simple Linear Regression Model ? Linear vs Non-Linear Regression ? Linear Regression Illustration ? Major Underlying Assumptions for Regression Analysis ? Least-Squares Method (LSM) ? Locally Weighted Linear Regression ? Regression Models in Excel ? Multiple Regression Analysis ? Logistic Regression ? Regression vs Classification ? Time-Series Analysis ? Decomposing Time-Series ? Summary Lab Exercises Lab 1 - Learning the Lab Environment Lab 2 - Using Jupyter Notebook Lab 3 - Repairing and Normalizing Data Lab 4 - Computing Descriptive Statistics Lab 5 - Data Grouping and Aggregation Lab 6 - Data Visualization with matplotlib Lab 7 - Data Splitting Lab 8 - k-Nearest Neighbors Algorithm Lab 9 - The k-means Algorithm Lab 10 - The Random Forest Algorithm

Duration 3 Days 18 CPD hours This course is intended for This course is aimed at anyone who wants to harness the power of data analytics in their organization including: Business Analysts, Data Analysts, Reporting and BI professionals Analytics professionals and Data Scientists who would like to learn Python Overview This course teaches delegates with no prior programming or data analytics experience how to perform data manipulation, data analysis and data visualization in Python. Mastery of these techniques and how to apply them to business problems will allow delegates to immediately add value in their workplace by extracting valuable insight from company data to allow better, data-driven decisions. Outcome: After attending this course, delegates will: Be able to write effective Python code Know how to access their data from a variety of sources using Python Know how to identify and fix data quality using Python Know how to manipulate data to create analysis ready data Know how to analyze and visualize data to drive data driven decisioning across your organization Becoming a world class data analytics practitioner requires mastery of the most sophisticated data analytics tools. These programming languages are some of the most powerful and flexible tools in the data analytics toolkit. From business questions to data analytics, and beyond For data analytics tasks to affect business decisions they must be driven by a business question. This section will formally outline how to move an analytics project through key phases of development from business question to business solution. Delegates will be able: to describe and understand the general analytics process. to describe and understand the different types of analytics can be used to derive data driven solutions to business to apply that knowledge to their business context Basic Python Programming Conventions This section will cover the basics of writing R programs. Topics covered will include: What is Python? Using Anaconda Writing Python programs Expressions and objects Functions and arguments Basic Python programming conventions Data Structures in Python This section will look at the basic data structures that Python uses and accessing data in Python. Topics covered will include: Vectors Arrays and matrices Factors Lists Data frames Loading .csv files into Python Connecting to External Data This section will look at loading data from other sources into Python. Topics covered will include: Loading .csv files into a pandas data frame Connecting to and loading data from a database into a panda data frame Data Manipulation in Python This section will look at how Python can be used to perform data manipulation operations to prepare datasets for analytics projects. Topics covered will include: Filtering data Deriving new fields Aggregating data Joining data sources Connecting to external data sources Descriptive Analytics and Basic Reporting in Python This section will explain how Python can be used to perform basic descriptive. Topics covered will include: Summary statistics Grouped summary statistics Using descriptive analytics to assess data quality Using descriptive analytics to created business report Using descriptive analytics to conduct exploratory analysis Statistical Analysis in Python This section will explain how Python can be used to created more interesting statistical analysis. Topics covered will include: Significance tests Correlation Linear regressions Using statistical output to create better business decisions. Data Visualisation in Python This section will explain how Python can be used to create effective charts and visualizations. Topics covered will include: Creating different chart types such as bar charts, box plots, histograms and line plots Formatting charts Best Practices Hints and Tips This section will go through some best practice considerations that should be adopted of you are applying Python in a business context.

Duration 4 Days 24 CPD hours This course is intended for If you want to advance from being a front-end developer to a full-stack developer and learn how Node.js can be used for hosting full-stack applications, this course is for you. Knowledge of JavaScript's basic syntax and experience with popular front-end libraries such as jQuery is required. You should also have used JavaScript with HTML and CSS, but not necessarily Node.js. Overview By the end of this course, you'll have the skills you need to tackle any real-world JavaScript development problem using a modern JavaScript approach, both for client and server sides.After completing this course, you will be able to: Apply the core concepts of functional programming Build a Node.js project that uses the Express.js library to host an API Create unit tests for a Node.js project to validate it Use the Cheerio library with Node.js to create a basic web scraper Develop a React interface to build processing flows Use callbacks as a basic way to bring control back This is your one-stop solution to mastering modern JavaScript. This course covers the latest features of JavaScript and advanced concepts, such as modularity, testing, and asynchronous programming. By the end of the course, you?ll know how to create a full-stack JavaScript application using NodeJS and how to use JavaScript in functional programming. JavaScript, HTML, and the DOM HTML and the DOM Developer Tools Node.js and npm What is Node.js? Node Version Manager (nvm) Node Package Manager (npm) Node.js APIs and Web Scraping Globals FileSystem APIs HTTP APIs What is Scraping? RESTful APIs with Node.js What is an API? What is REST? Useful Defaults and Easy Inputs Middleware The Contents of a JWT MongoDB Modular JavaScript ES6 Modules Object-Oriented Programming (OOP) npm Package? Code Quality Clear Naming Unit Tests Integration Tests End-to-End Testing Puppeteer Advanced JavaScript Language Features Supported in ES5, ES6, ES7, ES8, and ES9 OOP in JavaScript Sorting Maps and Sets Math, Date, and String Symbols, Iterators, Generators, and Proxies Asynchronous Programming Callback Hell Async and Await Event-Driven Programming and Built-In Modules Eventing Node.js Built-In Modules Handling Large Files in Node.js Functional Programming with JavaScript Functions ? First-Class Citizens Pure Functions Higher-Order Functions Function Composition Immutability and Side Effects Introduction to GraphQL Language Schemas and Queries

Duration 5 Days 30 CPD hours This course is intended for Security-operations (SecOps), or security, orchestration, automation, and response (SOAR) engineers, managed security service providers (MSSPs), service delivery partners, system integrators, and professional services engineers Overview This training is designed to enable a SOC, CERT, CSIRT, or SOAR engineer to start working with Cortex XSOAR integrations, playbooks, incident-page layouts, and other system features to facilitate resource orchestration, process automation, case management, and analyst workflow.The course includes coverage of a complete playbook-development process for automating a typical analyst workflow to address phishing incidents. This end-to-end view of the development process provides a framework for more focused discussions of individual topics that are covered in the course. The Cortex? XSOAR 6.2: Automation and Orchestration (EDU-380) course is four days of instructor-led training that will help you: Configure integrations, create tasks, and develop playbooks.Build incident layouts that enable analysts to triage and investigate incidents efficientlyIdentify how to categorize event information and map that information to display fields.Develop automations, manage content, indicator data, and artifact stores, schedule jobs, organize users and user roles, oversee case management, and foster collaboration Course Outline 1 - Core functionality and Feature Sets 2 - Enabling and Configuring Integrations 3 - Playbook Development 4 - Classification and Mapping 5 - Layout Builder 6 - Solution Architecture - Docker 8 - Automation Development & Debugging 9 - Content Management 10 - Indicators 11 - Jobs and Job Scheduling 12 - Users and Role Management 13 - Integration Development Additional course details: Nexus Humans Palo Alto Networks : Cortex XSOAR 6.8: Automation and Orchestration (EDU-380) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success. While we feel this is the best course for the Palo Alto Networks : Cortex XSOAR 6.8: Automation and Orchestration (EDU-380) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you. Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.

Duration 2 Days 12 CPD hours This course is intended for Cybersecurity analysts and engineers and security operations specialists, as well as administrators and product deployers. Overview Successful completion of this instructor-led course with hands-on lab activities should enable you to: Describe the architecture and components of the Cortex XDR family Use the Cortex XDR management console Create Cortex XDR agent installation packages, endpoint groups, and policies Deploy Cortex XDR agents on endpoints Create and manage exploit and malware prevention profiles Investigate alerts and prioritize them using starring and exclusion policies Tune Security profiles using Cortex XDR exceptions Perform and track response actions in the Action Center Perform basic troubleshooting related to Cortex XDR agents Deploy a Broker VM and activate the Local Agents Settings applet Understand Cortex XDR deployment concepts and activation requirements Work with the Customer Support Portal and Cortex XDR Gateway for authentication and authorization This instructor-led training enables you to prevent attacks on your endpoints. After an overview of the Cortex XDR components, the training introduces the Cortex XDR management console and demonstrates how to install agents on your endpoints and how to create Security profiles and policies. The training enables you to perform and track response actions, tune profiles, and work with Cortex XDR alerts. The training concludes with discussions about basic troubleshooting of the agent, the on-premises Broker VM component, and Cortex XDR deployment. Course Outline This class is comprised of the following modules: Module 1 - Cortex XDR Family Overview Module 2 - Cortex XDR Main Components Module 3 - Cortex XDR Mangement Components Module 4 - Profiles and Policy Rules Module 5 - Malware Protection Module 6 - Exploit Protection Module 7 - Cortex XDR Alerts Module 8 - Tuning Policies using Exceptions Module 9 - - Response Actions Module 10 - Basic Agent Troubleshooting Module 11 - Broker VM Overview Module 12 - Deployment Consideration

Duration 4.375 Days 26.25 CPD hours This course is intended for The target audience for this course is all Oracle professionals. Among the specific groups for whom this course will be helpful are: Application designers and database developers Database administrators Web server administrators Overview This eTextbook begins with an explanation of the intent and usage of the PL/SQL programming language for database applications. Important reasons why one should incorporate PL/SQL modules within the application architecture right from the initial design and planning phase are presented. Next one learns how to begin building executable PL/SQL program units. One learns about each of the major segments of a working program and how these interact with each other during program execution, including the important error or exception handling capabilities of the language. The final section goes beyond the basics and begins to explore advanced topics that will be useful later in the Sideris Oracle PL/SQL developer textbook series. One learns techniques and features that allow modular and reusable programs to be developed, increasing productivity and maintainability of database applications. his eTextbook introduces one to Oracle database programming using the PL/SQL programming language. One will learn the syntax, structure and features of the language. This textbook will also lay the foundation for the entire Sideris Oracle PL/SQL programming series, allowing one to progress from introductory topics to advanced application design and programming and finally onto writing complex high-performance applications. his eTextbook introduces one to Oracle database programming using the PL/SQL programming language. One will learn the syntax, structure and features of the language. This textbook will also lay the foundation for the entire Sideris Oracle PL/SQL programming series, allowing one to progress from introductory topics to advanced application design and programming and finally onto writing complex high-performance applications.