Free PDF Quiz 2026 C1000-173: Newest IBM Cloud Pak for Data v4.7 Architect Reliable Test Dumps

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IBM C1000-173 Exam Overview:

Certification Vendor:	IBM
Exam Name:	IBM Cloud Pak for Data v4.7 Architect
Exam Number:	C1000-173
Exam Duration:	90 minutes
Exam Price:	USD 200
Real Exam Qty:	62
Certificate Validity Period:	No fixed expiration
Related Certifications:	IBM Certified Specialist - Cloud Pak for Data IBM Certified Solution Advisor - Cloud Pak for Data
Available Languages:	English
Exam Format:	Scenario-based questions, Multiple choice
Passing Score:	66%
Recommended Training:	Solution Architect: IBM Cloud Pak for Data v4.x IBM Cloud Pak for Data Architecture and Implementation
Exam Registration:	IBM Certification Registration Pearson VUE
Sample Questions:	IBM C1000-173 Sample Questions
Exam Way:	Online proctored or onsite at authorized test centers
Pre Condition:	No mandatory prerequisites; recommended experience: 6+ months with Cloud Pak for Data, knowledge of OpenShift/Kubernetes, data architecture and governance concepts
Official Syllabus URL:	https://www.ibm.com/certify/exams/C1000-173

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IBM C1000-173 Exam Syllabus Topics:

Topic	Details
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	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 3	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 4	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 5	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.

IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q151-Q156):

NEW QUESTION # 151
What happens when the maximum number of new service instances is reached in Watson Discovery?

A. A reset command is required.
B. The user is warned that all service instances will stop.
C. There is no option to create more.
D. The oldest service instance is deleted to maintain the maximum number.

Answer: C

NEW QUESTION # 152
What is the default schedule for the diagnostics monitor when using the Alerting APIs?

A. Every 15 minutes
B. Every 30 minutes
C. Every 10 minutes
D. Every 5 minutes

Answer: C

Explanation:
You can use the IBM Software Hub monitoring and alerting framework to monitor the state of the platform.
You can set up events to alert when action is needed, based on thresholds that you define.
By default, IBM Software Hub is initialized with one monitor that runs every ten minutes. The diagnostic monitor records the status of deployments, StatefulSets, and persistent volume claims. It also tracks your system usage of virtual processors (vCPUs) and memory. The data that is collected can be used for analysis and to alert customers in a production environment based on set alert rules.

NEW QUESTION # 153
What makes a multi-engine architecture advantageous in watsonx.data?

A. Increased system complexity
B. Enhanced query optimization across different engines
C. Reduced data security
D. Limited data processing capabilities

Answer: B

NEW QUESTION # 154
How does data replication facilitate multi-site data distribution?

A. By increasing the complexity of data management
B. By limiting data access to a single location
C. By increasing the complexity of data management
D. By ensuring data is available across different geographical locations

Answer: D

NEW QUESTION # 155
Which statement describes MPP (Massively Parallel Processing) Database architecture?

A. An analytics environment that improves performance by dividing the data across many nodes.
B. Two or more databases kept in sync via two-phase commit.
C. A transactional system which uses multiple nodes for maximum availability.
D. A data warehouse that needs all compute nodes to access a shared data store simultaneously.

Answer: A

Explanation:
MPP, or Massively Parallel Processing, is a database architecture model where data is divided and processed across multiple compute nodes in parallel. Each node works independently on a portion of the data, dramatically improving query performance and throughput for analytics workloads. This model is ideal for big data and analytical queries, not transactional workloads. It differs from shared-disk models or replication strategies like two-phase commit. The correct definition involves distributed data and parallel query execution, as described in option C.

NEW QUESTION # 156
......

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