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IBM C1000-173 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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.
Topic 3
  • 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 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.

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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q129-Q134):

NEW QUESTION # 129
Which of the following best defines Disaster Recovery in IT systems?

Answer: B


NEW QUESTION # 130
Which set of DataStage features are primarily intended to improve reusability and flexibility?

Answer: A

Explanation:
DataStage promotes modularity and reusability through the use of components such as job stages, parameters, parameter sets, and environment variables. Parameters and parameter sets allow dynamic configuration of jobs, enabling reuse across environments and reducing hardcoding. Environment variables allow users to define global job behavior across projects. These elements significantly enhance development efficiency and pipeline portability. Other features like schema drift and ELT modes support execution flexibility, but they are not primarily focused on reusability.


NEW QUESTION # 131
In deciding between managed and self-managed OpenShift for Cloud Pak for Data, what are key factors to consider?

Answer: A,B,D


NEW QUESTION # 132
What are two considerations when choosing the type of storage for Cloud Pak for Data?

Answer: A,D

Explanation:
When selecting storage for Cloud Pak for Data, two critical considerations are:
Ensuring the storage supports the specific services being deployed. Not all services are compatible with every storage type (e.g., some services require block storage, others may support NFS).
The storage must provide sufficient I/O performance to meet the operational demands of the workloads.
Transmission speeds and throughput metrics are useful but not directly required as standalone criteria.
Additionally, NFS is not universally supported by all services; some services specifically require RWO (ReadWriteOnce) or RWX (ReadWriteMany) access modes.


NEW QUESTION # 133
Which Watson Pipeline component manages pipeline errors, typically used with DataStage?

Answer: C

Explanation:
In Watson Pipelines within IBM Cloud Pak for Data, error management is handled by the Error Handling component. This feature allows developers and pipeline administrators to define how pipeline failures are processed-whether to stop execution, continue, or trigger alternate flows. It ensures controlled behavior in response to job failures, particularly in complex ETL pipelines like those built with DataStage. Error Handling is a configurable element of pipeline orchestration and is typically used to enhance fault tolerance and control error propagation in production workflows.


NEW QUESTION # 134
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