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IBM C1000-173 Exam Questions
- IBM Certified Architect Certifications
- IBM Cloud Pak for Data V4.7 Certifications
- Topic 1: Plan for a Cloud Pak for Data Implementation: This section of the exam measures the skills of an Implementation Consultant and covers determining which Cloud Pak for Data services to deploy based on organizational needs. It involves sizing the Kubernetes/OpenShift cluster appropriately for workload demands and planning backup and restore strategies to ensure data protection. Planning for high availability and disaster recovery is essential to maintain uninterrupted service. Multi-tenancy requirements must be understood to support multiple user groups securely on shared infrastructure. Migration requirements need assessment to transition existing data and workloads smoothly.
- Topic 2: Security Requirements: This domain targets a Security Architect and focuses on planning security for a Cloud Pak for Data deployment. It includes managing certificates that secure communications, identity management systems, and access and authorization controls to enforce secure and compliant user and service interactions. The auditing features and their integration with enterprise audit systems are crucial to ensure traceability and accountability. Asset interchange security involves safeguarding data movement between services.
- Topic 3: Architect with AI Series: This section measures the skills of an AI Solution Architect and includes designing architectures for solutions involving various IBM Watson AI services. Architecting with Watson Assistant involves creating conversational AI interfaces. Watson Discovery solutions focus on building cognitive search and content analytics applications. Watson Pipelines solutions involve orchestrating data science workflows. Watson OpenScale architectures enable AI model monitoring and governance. Architecting with Match 360 supports personalized engagement by integrating multi-channel customer insights.
- Topic 4: Architect with Analytic Services: This domain targets a Data Analytics Architect and focuses on designing solutions using analytic components of Cloud Pak for Data. Architecting with DataStage involves creating data integration and ETL pipelines. Data Refinery architectures prepare and cleanse data for analysis. Db2 Big SQL solutions address querying large distributed data sets with SQL, facilitating analytics across diverse data sources.
- Topic 5: Architect with Data Governance Services: This section evaluates skills of a Data Governance Specialist, covering the architecture of knowledge catalogs to enable data discovery and management. Data Privacy architectures ensure compliance with regulatory requirements around data protection. Knowledge Accelerators involve designing solutions that speed up data governance processes through predefined policies and templates.
- Topic 6: Architect with Data Source Services: This domain measures expertise of a Data Integration Architect and includes architecting solutions with data replication technologies to synchronize data across systems. IBM Data Virtualization architecture enables access to and querying of disparate data sources without data movement. Architecting with watsonx.data focuses on building intelligent data lakes and query engines. Architecting with Db2-related services involves designing scalable data management and processing architectures using IBM's Db2 platforms.
Free IBM C1000-173 Exam Actual Questions
Note: Premium Questions for C1000-173 were last updated On Jul. 29, 2025 (see below)
Insurance industry datasets frequently include personally identifiable information (PII) and many data analysts need access to datasets but not to PII.
Which Cloud Pak for Data services leverage Data Protection Rules?
IBM Cloud Pak for Data includes built-in Data Protection Rules to enforce access control on sensitive data, such as PII. These rules are integrated directly into services like IBM Data Virtualization, Data Privacy, and IBM Knowledge Catalog. When analysts or applications access data through these services, the platform automatically masks, obfuscates, or restricts access to sensitive fields based on the defined policies. This ensures compliance with data privacy regulations and organizational security policies without manual intervention.
What is the purpose of the IBM Data Replication service?
The IBM Data Replication service in Cloud Pak for Data is designed to integrate and synchronize data between various systems, ensuring that data in target systems is kept up-to-date with the source systems. It supports near real-time replication and change data capture (CDC) mechanisms, making it ideal for analytics environments that require continuous synchronization. The service is not a tool for database activity monitoring (A), creating unified virtual views (B), or performing heavy data transformations (D).
What steps are required for setting up IBM Data Replication for Cloud Pak for Data?
To set up IBM Data Replication in Cloud Pak for Data, administrators must follow a sequence that begins with defining the source (such as a transactional database), then defining the target system (such as a data lake or warehouse), and finally configuring the replication process. This setup allows for near real-time data synchronization. User definitions, server setup, and data management rules are involved elsewhere in the platform but are not the essential steps for replication configuration.
Which plug-in is used by the Cloud Pak for Data Audit Logging service to forward audit records to a SIEM system?
The Audit Logging service in IBM Cloud Pak for Data uses Fluentd as the core log forwarding mechanism. Fluentd output plug-ins are configured to route audit logs to external SIEM systems such as Splunk or QRadar. These plug-ins are versatile and support multiple formats and transport protocols. Other options listed---like Logstash, OSS/J, or Kafka---are not the designated default forwarding mechanisms used within the CP4D Audit Logging architecture.
Which Db2 Big SQL component uses system resources efficiently to maximize throughput and minimize response time?
StreamThrough is a high-performance component used in Db2 Big SQL within IBM Cloud Pak for Data that is optimized to manage data streams and queries efficiently. It is designed to maximize throughput and minimize query response times by optimizing memory usage, resource allocation, and processing logic. Unlike Hive or Analyzer, which are used for query execution and analysis, StreamThrough enables efficient pipeline execution by streamlining data handling. Scheduler is used for job timing but does not influence runtime efficiency directly. StreamThrough is purpose-built to enhance performance through optimal resource usage.
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