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TeraData TDVAN5 Exam Syllabus Topics:

Topic	Details
Topic 1	Integration: This section tests the skills of database administrators in utilizing integration tools like QueryGrid and NOS. It covers the benefits, features, and functionality of QueryGrid, the database objects supporting its implementation, and the performance implications of accessing NOS and foreign servers.
Topic 2	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 3	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 4	User Administration: This section evaluates the skills of system administrators in meeting user security requirements through roles and proxy access configurations. Candidates will also learn about the functionality and benefits of profiles, as well as the key attributes that should be set for a new user.
Topic 5	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 (Q29-Q34):

NEW QUESTION # 29
The Administrator defined the following AWT resource limits in Viewpoint Workload Designer:
* Rule 1: A limit of 50 percent of AWTs for all DSA restore jobs in the system, with reject
* Rule 2: A limit of 20 percent of AWTs for DSA restore jobs from user UserA, with delay
* Rule 3: A limit of 10 percent of AWTs for DSA restore jobs from user UserB, with delay UserB is running DSA restore jobs, consuming 10% of AWTs. Other users are running DSA restore jobs and consuming 40% of AWTs.
What will TASM do if UserA submits a DSA restore job request?

A. TASM rejects it because it would exceed the AWT limit of Rule 1.
B. TASM delays it because it does not exceed the AWT limit of Rule 2.
C. TASM rejects it because it does not exceed the AWT limit of Rule 2.
D. TASM delays it because it would exceed the AWT limit of Rule 1.

Answer: D

Explanation:
Rule 1 limits all DSA restore jobs in the system to 50% of AWTs. Currently, 40% of AWTs are consumed by other users, and UserB is consuming 10%. So, the total AWT usage is already at the 50% limit.
Since submitting a new DSA restore job from UserA would exceed the 50% limit set in Rule 1, TASM will delay the request, as Rule 1 applies system-wide.
Rule 2 applies specifically to UserA, but Rule 1 takes precedence since the total system AWT usage is already at the global 50% threshold.
Thus, TASM delays the job because it would violate the global AWT limit in Rule 1.

NEW QUESTION # 30
A capacity planner wants to keep a record of the number of rows that are added and deleted from certain tables over time and would like to obtain this information without having to change the application itself.
Which DBQL option should be enabled?

A. EXPLAIN
B. USECOUNT
C. VERBOSE XMLPLAN
D. OBJECTS

Answer: D

Explanation:
The OBJECTS option in DBQL (Database Query Logging) records the tables and other objects that are accessed by queries, including information on how many rows are added, updated, or deleted. This allows the capacity planner to track changes to specific tables without modifying the application itself.
The other options are less relevant to tracking row changes:
USECOUNT records how often specific queries are executed, but not the number of rows affected.
EXPLAIN captures the query execution plan, which doesn't provide details on rows added or deleted.
VERBOSE XMLPLAN gives detailed execution plans in XML format, but it is more focused on query execution and optimization, not tracking row modifications.

NEW QUESTION # 31
An Administrator needs to restrict developers from accessing the Vantage system during the night, between 11:00PM and 05:00AM, but they are permitted to have limited access during the rest of the day.
How should the Administrator accomplish this goal?

A. Create a system filter that matches the developers accountstring, and include a classification criteria time window between 11:00PM and 05:00AM.
B. Create a health condition that will check which user id is accessing the system, and abort any request that is received between 11:00PM and 05:00AM.
C. Create a Planned Environment that will throttle to zero the concurrency of the developers between 11:00PM and 05:00AM.
D. Change any workloads that developers match to have a throttle count of 0.

Answer: C

Explanation:
A Planned Environment in Teradata Workload Management allows the Administrator to configure different system behaviors based on time windows. By throttling the concurrency of developer workloads to zero between 11:00 PM and 05:00 AM, the Administrator effectively restricts developers from running queries or jobs during that period. Outside of that time window, you can configure a limited concurrency for the developers.
Option A (Create a system filter): A filter can be used to restrict access based on the accountstring, but it isn't the most efficient way to handle time-based restrictions.
Option B (Create a health condition): This would involve more manual intervention and isn't designed for consistent, scheduled access control like Planned Environments.
Option C (Change workloads to have a throttle count of 0): While throttling the workload could work, Planned Environments provide a more flexible and scheduled approach for handling time-based restrictions.

NEW QUESTION # 32
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?

A. Throttles to limit the concurrency of Python scripts
B. Exceptions to place Python scripts consuming too much memory into a penalty box
C. Virtual partitions to assign separate memory space to each Python script
D. Planned environments to specify periods when data scientists can run Python scripts

Answer: A

Explanation:
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.

NEW QUESTION # 33
A web application executes millions of tactical queries on different tables of a large Vantage system, as shown below:

The most frequent query of the application is using the following SQL with this variable parameter:

The application owner requested to check for optimizations to improve the runtime of the query.
What should the Administrator suggest in this situation?

A. ALTER the tables ProductType and OrderType to use a sparse map that includes only a subset of the AMPs.
B. Create a sparse join index on Receiptline, with CustomerNumber as an input parameter.
C. Create a NUSI on Receiptline on each of the columns: ProductTypeId, OrderTypeId, VendorId.
D. Instead of directly accessing table Receiptline. a global temporary table should be used, that needs to contain prefiltered rows with CustomerNumber = <parameter>.

Answer: B

Explanation:
A sparse join index can be used to store a subset of rows from a table based on the condition of the most frequently queried parameter, in this case, CustomerNumber. By creating a sparse join index on the Receiptline table with CustomerNumber as an input parameter, the query can access a smaller subset of the data more efficiently, which can significantly improve performance, especially when millions of tactical queries are involved.
The other options are less optimal for this situation:
Create a NUSI on Receiptline: While NUSIs can improve query performance, creating NUSIs on multiple columns (e.g., ProductTypeId, OrderTypeId, VendorId) may not be as effective for improving this specific query focused on CustomerNumber.
ALTER tables ProductType and OrderType: Using a sparse map for these small tables (55 and 175 rows) wouldn't provide much benefit in terms of performance improvement, as the issue is not with these tables but with the larger Receiptline table.
Use a global temporary table: While prefiltering data in a temporary table could help in certain situations, this adds complexity and maintenance overhead. Additionally, it wouldn't necessarily offer a significant performance boost compared to a sparse join index.

NEW QUESTION # 34
......

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TDVAN5 Reliable Exam Tutorial - TDVAN5 Reliable Exam Tips

100% Pass Quiz 2025 TeraData Latest TDVAN5 Reliable Exam Tutorial

TeraData TDVAN5 Exam Syllabus Topics:

TeraData HCIA-Datacom V1.0 Sample Questions (Q29-Q34):

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