Which data type could support an index definition on a table?
Geospatial data types can support index definitions in Teradata. Geospatial indexing is used to optimize spatial queries, enabling efficient searching and retrieval of data based on spatial coordinates (e.g., points, lines, polygons).
The other data types do not support indexing directly:
DATASET: This is used to store unstructured or semi-structured data and typically does not support indexing.
JSON: While JSON data can be used within Teradata, it is typically not indexed in the traditional sense.
Large Object (LOB): LOBs (like BLOBs or CLOBs) are generally used for storing large amounts of unstructured data and are not suitable for indexing due to their size and nature.
The data science team reports that they do not have enough memory to run in-database Python scripts when the scripts operate simultaneously.
Which workload management feature should the Administrator use to resolve this issue?
Using throttles in workload management allows the Administrator to limit the concurrency of Python scripts running in the system. By controlling the number of Python scripts that can run simultaneously, you can prevent memory exhaustion and ensure that enough resources are available for each script to execute without causing failures due to memory constraints.
An Administrator manages a Vantage system that experiences large loads and updates during the night, on weekdays. On Saturday night, significant analytical processing occurs using the data from the prior weeks. The results of this processing are saved to support rapid reporting in the following week.
The business requires this Vantage system to be available as soon as possible in case of a catastrophic system failure.
Which backup strategy meets this need?
This strategy strikes a balance between minimizing recovery time and reducing the overall storage and performance impact during backups.
Weekly full backup ensures that a complete copy of the data is available at the start of the week, which is critical for quick recovery in the event of a catastrophic system failure.
Cumulative backups on Tuesday and Friday ensure that any changes made since the last full backup are captured without needing to apply multiple delta backups, reducing the time required for restoration.
Delta backups on other days provide incremental backups of the system with minimal performance impact, ensuring the system is consistently backed up without using excessive resources.
Which table identifies whether a particular workload is suffering from a shortage of AMP Worker Tasks?
The ResUsageSawt table captures detailed statistics related to AMP Worker Tasks (AWTs). It provides insight into how many AWTs are in use, waiting, or available, which helps identify whether a particular workload is suffering from a shortage of AWTs.
ResUsageSpma provides information about memory usage and swapping but not specifically about AWTs.
ResUsageSps focuses on session-level resource usage but does not track AMP Worker Tasks.
ResUsageSvpr provides data on virtual processor (vproc) usage and performance, not specifically on AWTs.
ResUsageSawt is the correct table to examine for AWT-related issues.
On a Vantage system, the data load process has recently become much slower than normal and is now running for two hours in low concurrency. During the time the process is running, the
Administrator measured the following average values from ResUsage:
System CPU busy = 75%
WIO = 25%
Inter-AMP parallelism = 90%
Average BYNET usage = 10%
Which resource is most constrained?
WIO (Work in I/O) is at 25%, which indicates that the system is spending a significant amount of time waiting for I/O operations to complete. This suggests that I/O is the most constrained resource in this scenario. A high WIO typically points to I/O bottlenecks, causing delays in processes like data loading.
The other resources appear to be less constrained:
CPU: The system is busy with 75% CPU usage, which indicates the CPU has available capacity (not fully utilized).
BYNET: With 10% BYNET usage, there is no indication of network congestion.
Parallelism: Inter-AMP parallelism is at 90%, which suggests that parallel processing is functioning well and not the bottleneck.
Thus, the data load slowdown is likely due to I/O constraints, making I/O the most constrained resource in this case.
