PERL programmers need a clear roadmap for improving their skills. Intermediate PERL teaches a working knowledge of PERL's objects, references, and modules — all of which makes the language so versatile and effective. This class offers a thorough introduction to intermediate programming in PERL. Topics include packages and namespaces, references and scoping, manipulating complex data structures, writing and using modules, package implementation, and using CPAN.
Attendees to P-315: Intermediate PERL Programming will receive TechNow approved course materials and expert instruction.
Instructor comments: Instructor kept it interesting and brought a wealth of knowledge to the classroom environment. Kept a good pace and provided relevant examples.
TechNow has heard many students talk about virtualized/remote training that TechNow Does Not Do. While training our most recent offering of PA-215: Palo Alto Networks Firewall Essentials FastTrack a student told his story of how he endend up in our course. His story we have heard for other technologies like Cisco, VMware, BlueCoat and other products.
A large percentage of training is moving to the virtualized/remote lab environments. Students are asked to use some variant of remote access software and remote into the training company's lab environment. Our student in our Palo Alto Networks Firewall course informed us that he went to a very costly offering of that course from the vendor and was not able to perform any labs. There were either network connectivity issues, or issues with the remote access software, or other problems. The whole training experience was very frustrating and not productive.
We keep our labs open to students if they would like after hours, or before hours access. Repeatedly going through a lab engrains that knowledge for later recall. Touching hardware is so critical in understanding the problems that arise when a cable comes loose, or a cable gets plugged in the wrong port. There are other scenarios such as just pulling the power cable, or turning off a power strip, or accidently overwriting a configuration. These disaster scenarious requires hands-on physical access to hardware. Preventing and recovering from disasters is what it's all about, and that requires hands-on, instructor led, real hardware.
CompTIA SecAI+ is the first certification in CompTIA’s expansion series, designed to help you secure, govern and responsibly integrate artificial intelligence into your cybersecurity operations. You’ll build the skills to defend AI systems, meet global compliance expectations and use AI to enhance threat detection, automation and innovation—so you can strengthen your expertise and help keep your organization’s systems and data secure.
SecAI+ helps you build practical AI security and automation skills on top of your existing expertise, so you can secure AI deployments, use AI‑assisted security tools with confidence, and stay ready for the next step in your cybersecurity career.
Course Objectives:
Apply AI concepts to strengthen your organization’s cybersecurity posture
Secure AI systems using advanced controls and protections to safeguard data, models, and infrastructure
Leverage AI technologies to automate workflows, accelerate incident response, and scale security operations
Navigate global GRC frameworks to ensure ethical and compliant AI adoption across industries
Defend against AI-driven threats like adversarial attacks, automated malware, and malicious use of generative AI
Integrate AI securely into DevSecOps pipelines and enterprise security strategies.
Dates/Locations:
No Events
Prerequisites: Recommended experience: 3–4 years in IT and 2+ years hands-on cybersecurity; Security+, CySA+, PenTest+, or equivalent recommended
SecAI+ (V1) exam objectives summary
Basic AI concepts related to cybersecurity (17%)
Explain core AI principles and terminology: Machine learning, deep learning, natural language processing, and automation.
Identify AI applications in security: Use cases for AI in threat detection, defense, and security operations.
Recognize AI-driven threats: Automated phishing, polymorphic malware, adversarial machine learning, and malicious use of generative AI.
Securing AI systems (40%)
Implement security controls: Protect AI systems, data, and models using robust technical safeguards.
Secure AI deployment environments: Apply best practices across on-premises, cloud, and hybrid infrastructures.
Mitigate adversarial risks: Defend against attacks targeting AI models, data pipelines, and inference layers.
AI-assisted security (24%)
Enhance detection and response: Use AI-driven tools to identify anomalies, detect threats, and accelerate incident remediation.
Automate security workflows: Integrate AI for event triage, alert correlation, and response orchestration.
Apply AI techniques in operations: Incorporate AI into threat modeling, behavior analysis, and continuous monitoring.
AI governance, risk, and compliance (19%)
Understand regulatory frameworks: Identify global governance requirements and their implications for AI adoption.
Integrate GRC into AI projects: Incorporate governance, risk management, and compliance practices throughout the AI lifecycle.
Ensure responsible AI use: Apply ethical guidelines, legal standards, and industry frameworks such as GDPR and NIST AI RMF.
In this course, the students will implement various data platform technologies into solutions that are in-line with business and technical requirements, including on-premises, cloud, and hybrid data scenarios incorporating both relational and NoSQL data. They will also learn how to process data using a range of technologies and languages for both streaming and batch data.
The students will also explore how to implement data security, including authentication, authorization, data policies, and standards. They will also define and implement data solution monitoring for both the data storage and data processing activities. Finally, they will manage and troubleshoot Azure data solutions which includes the optimization and disaster recovery of big data, batch processing, and streaming data solutions.
TechNow has worked worldwide enterprise infrastructures for over 20 years and has developed demos and labs to exemplify the techniques required to demonstrate cloud technologies and to effectively manage security in the cloud environment.
Attendees to DP-200: Implementing an Azure Data Solution will receive TechNow approved course materials and expert instruction.
Date/Locations:
No Events
Course Duration: 4 days
Course Outline:
Azure for the Data Engineer
Working with Data Storage
Enabling Team Based Data Science with Azure Databricks
Building Globally Distributed Databases with Cosmos DB
Working with Relational Data Stores in the Cloud
Performing Real-Time Analytics with Stream Analytics
Orchestrating Data Movement with Azure Data Factory
Securing Azure Data Platforms
Monitoring and Troubleshooting Data Storage and Processing
Prerequisites :
In addition to their professional experience, students who take this training should have technical knowledge equivalent to the following courses:
