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TeraData TDVAN5 Exam Syllabus Topics:
Topic
Details
Topic 1
- Database Management: This section tests the skills of database administrators in managing access rights and advanced analytic functions. It covers logging benefits, privilege types, and the impact of changing global parameters on user sessions.
Topic 2
- Monitoring Vantage: This section of the exam measures the skills of IT professionals such as system administrators. It covers how to effectively use Viewpoint to investigate system conditions, monitor external queries, and administer both Viewpoint and monitored systems.
Topic 3
- Performance Management: This section of the exam evaluates the skills of TeraData database administrators. It focuses on strategies for managing statistics, implementing DBQL logging, and using performance data to troubleshoot and resolve system issues.
Topic 4
- Security Management & Auditing: This section of the exam measures the skills of such database managers. It covers the features, functionality, and benefits of access logging and advanced security configurations.
TeraData HCIA-Datacom V1.0 Sample Questions (Q48-Q53):
NEW QUESTION # 48
What is the maximum payload size for CSV and JSON data formats that the NOS_READ function can read, when it is based on the LATIN character set?
- A. 32 MB
- B. 16 MB
- C. 8 GB
- D. 2 GB
Answer: A
Explanation:
When using the NOS_READ function in Teradata to read data in CSV and JSON formats based on the LATIN character set, the maximum payload size that can be read is 32 MB. This limit ensures efficient processing and handling of large datasets, while also maintaining performance.
NEW QUESTION # 49
BATCH_USER submits stored procedures, utility jobs, and other requests. All BATCH_USER work classifies into workload WD-Batch based on username criterion.
System performance has slowed due to AWT exhaustion during the batch window. The Administrator determined the AWT exhaustion is due to high concurrency of stored procedure calls from BATCH_USER.
How can the Administrator effectively throttle these stored procedures?
- A. Define an AWT resource limit, classify on username = BATCH_USER and utility = Stored Procedure, and set the limit.
- B. Create a new workload, classify on username = BATCH_USER and statement type = CALL, and set a throttle.
- C. Define a system throttle, classify on username = BATCH_USER and stored procedure = *.*, and set the limit.
- D. Create a utility limit, classify on username = BATCH_USER and utility = Stored Procedure, and set the limit.
Answer: B
Explanation:
In this scenario, the issue arises due to high concurrency of stored procedure calls, which are exhausting AWTs (AMP Worker Tasks). To address this, the administrator needs to throttle the specific stored procedure calls made by BATCH_USER. The most effective way to do this is to create a new workload that specifically targets those calls (statement type = CALL) from BATCH_USER and then applies a throttle to limit concurrency. This helps manage the system's AWTs by controlling how many stored procedures can be run concurrently.
Other options are less suitable as they either do not target stored procedures specifically or may not provide the necessary granularity in controlling AWT usage for stored procedure calls.
NEW QUESTION # 50
An analytics team uses a multibillion row table that is relevant to a great number of queries with different filters and joins. The Administrator needs to identify an effective strategy to collect statistics on this table.
Which statistics should be collected?
- A. Full-table
- B. [Dynamic AMP Sample
- C. Sampled
- D. Summary
Answer: B
Explanation:
Dynamic AMP Sample is an efficient method for collecting statistics on large tables. It collects sample statistics from a subset of AMPs (Access Module Processors) in the system, making it much faster and less resource-intensive than collecting full-table statistics, while still providing sufficiently accurate information for the optimizer.
Full-table statistics collection would be too resource-intensive for a multibillion-row table, potentially causing performance issues due to the size of the data.
Sampled statistics might be an option, but Dynamic AMP Sample is generally preferred because it provides a more efficient and balanced approach in large distributed systems like Teradata.
Summary statistics typically apply to aggregate data rather than large, detailed tables, and would not be sufficient for query optimization across different filters and joins.
Hence, Dynamic AMP Sample is the most effective strategy in this scenario.
NEW QUESTION # 51
A customer has to use Data Mover with legacy tools to transfer data from the production system to the disaster recovery (DR) system. Both systems are on-prem, but located in different geographies.
Where should the Data Mover Server be deployed to provide optimum data transfer?
- A. Location does not affect data transfer bandwidth.
- B. In the target environment
- C. In the cloud
- D. In the source environment (production]
Answer: D
Explanation:
Data Mover Server should be deployed in the source environment (production) for optimum data transfer. This setup ensures that the data is transferred as efficiently as possible from the production system to the disaster recovery (DR) system. By having the server close to the source, it can access the data more quickly and efficiently initiate the transfer process, minimizing delays caused by geographic distance.
Location does not affect data transfer bandwidth: Location does affect data transfer bandwidth due to network latency and distance between systems.
In the cloud: Using a cloud environment would introduce unnecessary complexity and potential latency since both systems are on-prem.
In the target environment (DR): Deploying the Data Mover Server in the target environment could introduce latency issues, as it would have to pull data from the production system over long distances.
Thus, placing the server in the source environment is optimal for reducing latency and maximizing data transfer efficiency.
NEW QUESTION # 52
A Vantage customer is using the Hive Connector in QueryGrid to access a foreign server and push the processing onto the Hadoop Cluster. The Vantage Administrator is concerned about the performance implication of the temporary space that is consumed by these queries during execution.
What must the Vantage Administrator investigate?
- A. Spool space and temporary database name defined on the link
- B. Temp space assigned to the Teradata user defined in the authorization object
- C. Perm space assigned to the user executing the QueryGrid query
- D. The default temporary NOS location defined on the connector
Answer: A
Explanation:
When using the Hive Connector in QueryGrid to push processing onto a Hadoop cluster, queries generate temporary data that consumes spool space on the Vantage system. The temporary database name defined on the link is where this temporary data is stored during execution.
The administrator should focus on investigating the spool space allocations and configurations related to the QueryGrid link because this directly impacts performance, especially when managing large datasets or complex queries.
The other options are less relevant:
Perm space assigned to the user is not directly related to the temporary space used during query execution.
The default temporary NOS location pertains to object storage and is unrelated to Hadoop or QueryGrid processing.
Temp space assigned to the Teradata user is also less relevant, as the spool space and the QueryGrid link are the primary concerns for temporary space usage in this scenario.
NEW QUESTION # 53
......
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