Expected C1000-056 Exam Topics, as suggested by IBM :

• Module 1: Create and configure App Connect Enterprise components/ Describe and configure link level security
• Module 2: Install the App Connect Enterprise Development environment/ Describe and explain the Web administration console
• Module 3: Implement functionality used for error handling, logging and monitoring/ Integration Architecture, Planning, and Design
• Module 4: Gather and analyze message flow statistics and resource statistics/ Understand App Connect Enterprise topologies
• Module 5: Configure and use trace nodes in a message flow/ Explain the core architecture, components, and operating environments
• Module 6: Define and override application properties using policies and user defined properties/ Development of IBM App Connect Enterprise Toolkit Solutions
• Module 7: Gather logging diagnostic information/ Plan for migration from previous versions
• Module 8: Use the debugger to locate and resolve problems/ Understand container based installation
• Module 9: Build applications using common integration patterns/ Build solutions with basic built in nodes
• Module 10: Build integration solutions with patterns/ Build and deploy App Connect Enterprise solutions
• Module 11: Solution Assembly, Configuration and Deployment/ Configure and use user trace facilities

• Module 1: Demonstrate knowledge of scenarios for application of machine learning/ Explain the fundamental terms and concepts of design thinking
• Module 2: Show knowledge of how to communicate technical results to business stakeholders/ Explain and calculate different types of matrix operations
• Module 3: Demonstrate mastery preparing and cleaning unstructured text data/ Explain the different types of fundamental Data Science
• Module 4: Demonstrate basic understanding of natural language processing/ Explain the general properties of common probability distributions
• Module 5: Translate business opportunities into a machine learning scenario/ Show knowledge of data exploration techniques and data anomaly detection
• Module  6: Describe the differences between traditional programming and machine learning/ Use data summarization and visualization techniques to find relevant insight
• Module 7: Demonstrate knowledge on open source technologies for deployment of AI solutions/ Demonstrate expertise cleaning data and addressing data anomalies
• Module 8: Demonstrate foundational knowledge of using python as a tool for building AI solutions/ Show knowledge of feature engineering and dimensionality reduction techniques
• Module 9: Describe the key considerations when selecting a platform for AI model deployment/ Demonstrate successful application of model validation and selection methods
• Module 10: Identify IBM technology capabilities for building, deploying, and managing AI models/ Identify use cases where artificial intelligence solutions can address business opportunities
• Module 11: Show knowledge of the benefits of cloud computing for building and deploying AI models/ Demonstrate knowledge of requirements for model monitoring, management and maintenance

Expected C1000-162 Exam Topics, as suggested by IBM :

• Module 1: Offense Analysis: This topic is all about identifying how the offense happened, where that particular offense happened, and which players involved in the offense.
• Module 2: Rules and building block design: In this topic questions about Interpreting rules that test for regular expressions. It also discusses creation and management of reference sets. The topic also point outs the need for QRadar Content Packs. Lastly the exam topic describes different types of rules such as behavioral, anomaly and threshold rules.
• Module 3: Threat Hunting: Threat hunting starts with results which are presented in an offense. Moreover, the topic also focuses on evidence inside an offense, including event and flow details. It also delves into triggered rules, payloads, and filters to differentiate real threats from false ones.
• Module 4: Dashboard Management: The topic is all about the dashboard tab which focuses on specific areas of network security. Questions about using the default QRadar dashboard and using Pulse also appear in this topic.
• Module 5: Searching and Reporting: In this topic, you study how to effectively use QRadar’s search capability. You learn how to use QRadar’s search capabilities such as filtering event, asset related data, flow, and creating quick and advanced searches. This topic delves into using various parts of the QRadar UI as well.

• Module 1: Compute Options: This part of the exam assesses the knowledge of data architects, focusing on the functionality of VPC on IBM Cloud through detailed feature use cases. It also includes understanding architectural considerations for IBM Cloud container platforms.
• Module 2: Designing Cloud Solutions: This section is aimed at business intelligence developers and covers describing the three IBM Cloud service models, examining automation for application platforms using IBM Cloud Schematics.
• Module 3: Data Analytics and Data Management: In this section, data analysts are evaluated on their understanding of the capabilities and features of IBM Analytics Engine on IBM Cloud.
• Module 4: IBM Cloud Storage Options: This section tests the knowledge of data architects about storage options and capabilities on IBM Cloud, including File Storage, Block Storage for VPC, and Object Storage.
• Module 5: IBM Networking Options: This portion assesses the ability of data architects to explain Direct Link deployment considerations, hybrid cloud integration via IBM Secure Gateway, and cloud-native application network requirements for containers.
• Module 6: Security: This section is designed for professionals with a focus on cloud security. It evaluates the knowledge of IBM Cloud Professionals to understand and deploy key architectural concepts related to IBM Cloud Security, the use of IBM Key Protect, and Hyper Protect Crypto Services.
• Module 7: IBM Cloud Resiliency Features: This part assesses the understanding of data architects about the benefits of deploying applications in multizone regions (MZR), disaster recovery concepts, and resilient solutions.
• Module 8: Observability Capabilities: This section measures the ability of data architects to identify monitoring and alerting features in IBM Cloud Monitoring and analyze the functionalities of IBM Log Analysis through detailed use cases.

Expected C2090-623 Exam Topics, as suggested by IBM :

• Module 1: Identify how authorization is achieved and implemented/ Describe the steps of creating a datasource
• Module 2: Demonstrate troubleshooting techniques in the server environment/ Identify the architecture and components
• Module 3: Identify techniques to organize report content in the portal/ Describe installation options and configurations
• Module 4: Describe how to implement and manage audit logging/ Demonstrate tasks to implement configuration and performance tuning
• Module 5: Identify how to use additional performance tools/ Describe tasks used to manage components
• Module 6: Describe the steps and options in content administration/ Describe how authentication is implemented
• Module 7: Describe the request flow of report execution/ Describe maintaining configuration settings
• Module 8: Describe using administrative portal options/ Demonstrate balancing the server components
• Module 9: Describe how to manage and schedule reports/ Describe configuring the memory settings
• Module 10: Describe system backup and restoration/ Describe using LifeCycle Manager (LCM)
• Module 11: Describe how to implement and work with tracing/ Describe how to manage report execution