C1000-173 Latest Mock Test | C1000-173 Visual Cert Exam
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IBM C1000-173 Exam Syllabus Topics:
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IBM Cloud Pak for Data v4.7 Architect Sample Questions (Q28-Q33):
NEW QUESTION # 28
Which two Cloud Pak for Data services implement data masking to support secure data sharing?
- A. IBM Knowledge Catalog
- B. Data Privacy
- C. DataStage
- D. Db2 Data Gate
- E. SPSS
Answer: A,B
Explanation:
Data masking in IBM Cloud Pak for Data is primarily supported by IBM Knowledge Catalog and Data Privacy services. IBM Knowledge Catalog enforces masking through Data Protection Rules, which dynamically mask sensitive fields when data is accessed through virtualized connections.
Data Privacy allows creating masking flows and rules that transform datasets while maintaining usability for analytics, ensuring sensitive data is hidden or obfuscated. DataStage and Db2 Data Gate are ETL and data replication tools, respectively, and SPSS is an analytics tool, none of which natively implement comprehensive masking as a core capability.
NEW QUESTION # 29
Why is data replication crucial for real-time data changes?
- A. To decrease data accuracy
- B. To ensure data consistency across systems
- C. To increase data processing time
- D. To delay data updates
Answer: B
NEW QUESTION # 30
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 Log Retention (log_retention)
- B. Auto-scaling (computePodsMin, computePodsMax)
- C. ETL/ELT Mode (ETL, ELT, or Hybrid)
- D. Job Count (JobCount)
- E. Job Configuration File (APT_CONFIG_FILE)
Answer: B,D
NEW QUESTION # 31
How can multi-tenancy be achieved with multiple instances of Cloud Pak for Data?
- A. By deploying a single instance for all tenants
- B. Through dedicated instances per tenant for isolation
- C. Avoiding namespace usage
- D. Assigning all resources to a single tenant
Answer: B
NEW QUESTION # 32
What does Watson OpenScale require to generate statistics?
- A. Complete test data set
- B. Training data
- C. Test data subset (10%)
- D. Access to Pipeline
Answer: B
Explanation:
* Training Data Statistics:Watson OpenScale needs to understand the characteristics of the data the model was trained on. This includes things like the distribution of features, sensitive attributes (for fairness monitoring), and how the model performed on this initial data. These "training data statistics" are crucial for:
* Fairness Configuration:Recommending fairness attributes, reference, and monitored groups.
* Bias Detection:Calculating fairness metrics (like disparate impact) by comparing runtime behavior to the learned training data distribution.
* Explainability:Generating explanations by understanding the distribution of values in the training data to create meaningful perturbations.
* Drift Detection:Building a drift detection model that compares runtime data to the training data to identify shifts.
While Watson OpenScale also consumespayload data(the data sent to the deployed model for predictions) at runtime to calculate various metrics and perform monitoring, the initial setup and the ability to generate meaningfulstatisticsfor things like fairness and drift fundamentally rely on understanding thetraining data
NEW QUESTION # 33
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