[2026年08月] を試そう！リアルC1000-173問題集で100%無料C1000-173試験問題集 [Q75-Q91]

[2026年08月] を試そう！リアルC1000-173問題集で100%無料C1000-173試験問題集

C1000-173のPDF問題集試験問題有効なC1000-173問題集

IBM C1000-173 認定試験の出題範囲：

トピック	出題範囲
トピック 1	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.
トピック 2	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.
トピック 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.

質問 # 75
Which of the following is a key feature of IBM Cloud Pak for Data's architecture?

A. A containerized architecture based on Kubernetes
B. Built-in data warehousing
C. Limited integration with cloud services
D. Integration with only on-premise systems

正解：A

解説：
IBM Cloud Pak for Data is built on a containerized architecture, leveraging Kubernetes and OpenShift. This allows the platform to be flexible, scalable, and portable across various environments, including on-premises, public clouds, and hybrid clouds.

質問 # 76
What is a Data Refinery flow in Cloud Pak for Data?

A. A data storage location.
B. A visualization tool.
C. A machine learning model.
D. An ordered set of data operations.

正解：D

解説：
A Data Refinery flow in Cloud Pak for Data is an ordered set of data operations (transformations) that are applied to tabular data. It is used to cleanse, shape, and prepare data for analysis or machine learning. Users can apply filters, joins, aggregations, and custom expressions. It is not a storage location (A), ML model (B), or a visualization tool (D), though visual previews of transformed data are available.

質問 # 77
How does IBM Cloud Pak for Data support disaster recovery and business continuity planning?

A. By providing automated backup and restore capabilities
B. By supporting high-availability cluster configurations
C. By enabling cross-region data replication
D. All of the above

正解：D

質問 # 78
Which data processing engine is used for Data Privacy Masking flows?

A. DataStage
B. Spark
C. dbt
D. Presto

正解：B

解説：
Data Privacy Masking flows in IBM Cloud Pak for Data utilize Apache Spark as the underlying data processing engine. Spark enables large-scale, distributed data masking operations for structured data, supporting high-performance transformations and compliance with privacy regulations. While DataStage can perform similar operations, the default and recommended engine for Data Privacy flows in CP4D is Spark.
dbt and Presto are not used for this masking functionality.

質問 # 79
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?

A. IBM Data Virtualization, Data Privacy, and IBM Knowledge Catalog
B. DataStage, SPSS Modeler, and Python Notebooks
C. Watson Discovery and Watson Assistant
D. Data Refinery, Watson Pipeline, and Watson Studio

正解：A

解説：
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.

質問 # 80
The data integration team at a financial services company has always struggled to manage resources as the number of integration jobs changes throughout the month.
Which two settings are available when configuring Dynamic Workload Management for a specific DataStage instance?

A. Job Count (JobCount)
B. Auto-scaling (computePodsMin, computePodsMax)
C. Job Configuration File (APT_CONFIG_FILE)
D. ETL/ELT Mode (ETL, ELT, or Hybrid)
E. Job Log Retention (log_retention)

正解：A、B

質問 # 81
When planning for the data management console in Db2, what functionality is crucial for effective database management?

A. Comprehensive monitoring and management features
B. Restricting user access to essential functionalities only
C. Avoiding integration with other Db2 services
D. Limited visibility over data and queries

正解：A

質問 # 82
What must be created to enable the Cloud Pak for Data platform to use a company's custom CA certificate to validate certificates from internal servers?

A. A secret that contains the company's CA certificate.
B. A configmap that contains the company's CA certificate.
C. A secret containing a wildcard certificate for all internal servers.
D. A configmap that contains all internal server certificate chains.

正解：B

解説：
To enable IBM Cloud Pak for Data to trust certificates from internal servers using a custom Certificate Authority (CA), the correct method is to create a Kubernetes ConfigMap that contains the CA certificate. This ConfigMap is referenced by the platform's foundational services to include the CA in the trusted root store.
Secrets are typically used for storing sensitive data like private keys and TLS certificates but are not used for adding trusted root CAs at the platform level. A ConfigMap is explicitly required by the platform to inject the CA trust into the certificate validation chain.

質問 # 83
What is one benefit that collaborators in a catalog have in IBM Knowledge Catalog?

A. They can see all the credentials associated with the data.
B. They have full access to unstructured data in the catalog.
C. They can only access data stored in PDF documents.
D. They can access data assets without needing separate credentials.

正解：D

解説：
Collaborators in IBM Knowledge Catalog are granted access to data assets that have been properly governed and made available through connections. Once a connection is established by an administrator or asset owner, users with collaborator roles can access the data without needing to re-enter credentials. This simplifies secure data consumption and aligns with enterprise access control policies. They do not see underlying credentials, and access is not limited to document types like PDFs.

質問 # 84
Comparing data replication with an ETL tool, which feature is exclusive to data replication concerning real-time data changes?

A. Batch processing of data changes
B. Reduced data consistency
C. Deferred data update capabilities
D. Immediate data synchronization

正解：D

質問 # 85
What are the limitations regarding the number of instances that can be created with Watson Assistant?

A. No more than 5 instances per IBM Cloud account
B. Unlimited instances as long as there is sufficient storage
C. Only one instance is allowed per network to ensure security
D. The limit is set based on the subscription model chosen

正解：D

質問 # 86
Which component of IBM Cloud Pak for Data enables data integration and transformation?

A. Watson Studio
B. DataStage
C. Watson Knowledge Catalog
D. OpenShift

正解：B

解説：
IBM DataStage is a powerful data integration and ETL (Extract, Transform, Load) tool within Cloud Pak for Data. It helps organizations integrate and transform data from various sources to ensure consistency and quality before feeding it into analytics systems.

質問 # 87
How does watsonx.data provide data sharing between Db2 Warehouse, Netezza, and any other data management solution?

A. Iceberg tables
B. Secure File Transfer Protocol (SFTP)
C. JSON file format
D. watsonx.data proprietary fast loader

正解：A

解説：
watsonx.data uses Apache Iceberg tables as the open table format for data sharing across platforms like Db2 Warehouse, Netezza, and other compatible data management solutions.
Iceberg provides a transactional and schema-evolution-friendly table layer, allowing multiple engines to read and write data concurrently. This approach avoids proprietary loaders or simple file transfers and ensures efficient interoperability between different systems.

質問 # 88
What is the purpose of configuring access to a Git repository associated with a project in Cloud Pak for Data?

A. To enhance data visualization in JupyterLab or RStudio.
B. To delete the repository and create a new one.
C. To collaborate with others, manage file versions, and enable branching.
D. To manage the deployment of a model to a project space.

正解：C

解説：
Configuring access to a Git repository in Cloud Pak for Data projects allows teams to collaborate on code, notebooks, and assets while benefiting from version control and branching. This setup ensures that all project files can be tracked, reverted, or merged, enabling collaborative development and continuous integration workflows. It is not used for model deployment management (B) or visualization enhancements (C). Option D is unrelated to the actual purpose of Git integration.

質問 # 89
How do Massively Parallel Processing (MPP) and Symmetric Multiprocessing (SMP) differ in a Db2 environment?

A. MPP requires a shared memory architecture, whereas SMP does not
B. MPP is less suited for complex analytical tasks compared to SMP
C. SMP is more scalable than MPP
D. MPP divides tasks among multiple processors, while SMP uses a single processor

正解：D

質問 # 90
Are there any special considerations for the client to migrate existing server jobs to DataStage in Cloud Pak for Data?

A. Server jobs are automatically migrated to Watson Pipeline flows.
B. Server jobs can be converted to parallel jobs prior to migration using MettleCI.
C. Server jobs migrate directly to parallel jobs that always require modification.
D. Server jobs migrate with no changes.

正解：B

解説：
Legacy DataStage server jobs are not automatically compatible with DataStage on Cloud Pak for Data, which uses a parallel engine architecture. MettleCI is the recommended tool to convert server jobs into parallel jobs before migration. This conversion allows reusability and ensures the migrated jobs can run efficiently in the CP4D environment. Direct migration without modification (option D) is not possible, and they do not migrate to Watson Pipelines (option A).

質問 # 91
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