QUIZ 2025 TDVAN5: RELIABLE HCIA-DATACOM V1.0 TEST CRAM

Quiz 2025 TDVAN5: Reliable HCIA-Datacom V1.0 Test Cram

Quiz 2025 TDVAN5: Reliable HCIA-Datacom V1.0 Test Cram

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TeraData TDVAN5 Exam Syllabus Topics:

TopicDetails
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
  • 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 3
  • 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.
Topic 4
  • 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.

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TeraData HCIA-Datacom V1.0 Sample Questions (Q48-Q53):

NEW QUESTION # 48
There is a call center application that repetitively sends tactical queries to Vantage These queries use a few small tables that are joined on the primary index column.
The following maps are defined in the system:
* TD_GlobalMap
* TD_Map1
* TD_DataDictionaryMap
* TD_1AmpSparseMap_1Node
What can be done to optimize these queries?

  • A. Assign these tables to TD_1AmpSparseMap_1Node using different colocation names for each table.
  • B. Assign these tables to TD_Map1 using the same colocation name for every table.
  • C. Assign these tables to TD_Map1 using different colocation names for each table.
  • D. Assign these tables to TD_1AmpSparseMap_1Node using the same colocation name for every table

Answer: D

Explanation:
Example:
TD_1AmpSparseMap_1Node is a sparse map that assigns the data to a single AMP (or a few AMPs), and using the same colocation name ensures that the tables are collocated on the same AMP. This helps in efficient joining because no data redistribution is required between AMPs when tables are joined on the primary index.
Using different colocation names for each table (options A and D) would place the tables on different AMPs, leading to less efficient joins since data would need to be shuffled between AMPs.
TD_Map1 is a predefined map in the system but does not specifically optimize small, frequently accessed tables in the same way that TD_1AmpSparseMap_1Node does, which is more suitable for these scenarios.
Thus, using the same colocation name within the TD_1AmpSparseMap_1Node ensures that the joins are AMP-local, optimizing the repetitive tactical queries.


NEW QUESTION # 49
Which workload management technique should the Administrator use to reject all QueryGrid queries during a pre-defined critical batch state?

  • A. A filter that includes the following functions:
    TD_SYSFNLIB.QGInitiatorExport
    TD_SYSFNLIB.QGInitiatorlmport
    TD_SYSFNLIB.QGRemoteExport
    TD_SYSFNLIB.QGRemotelmport
  • B. An exception that excludes the following Foreign Servers:
    TD_SYSFNLIB.QGInitiatorExport
    TD_SYSFNLIB.QGInitiatorlmport
    TD_SYSFNLIB.QGRemoteExport
    TD_SYSFNLIB.QGRemotelmport
  • C. An exception that includes the following Foreign Servers:
    TD_SYSFNLIB.QGInitiatorExport
    TD_SYSFNLIB.QGInitiatorlmport
    TD_SYSFNLIB.QGRemoteExport
    TD_SYSFNLIB.QGRemotelmport
  • D. A filter that excludes the following functions:
    TD_SYSFNLIB.QGInitiatorExport
    TD_SYSFNLIB.QGInitiatorlmport
    TD_SYSFNLIB.QGRemoteExport
    TD_SYSFNLIB.QGRemotelmport

Answer: C

Explanation:
In this case, using an exception with the Foreign Servers related to QueryGrid functions will prevent queries involving those foreign servers from running. This is a typical method to block or reject specific workloads during critical times, such as a batch processing window.
Exceptions in workload management are used to define specific conditions under which queries or workloads should be rejected or managed differently. By including these QueryGrid-related functions in an exception, the administrator ensures that any QueryGrid queries involving the listed foreign servers will be rejected during the critical batch state.
Options B and D, which mention filters, are not appropriate for rejecting queries. Filters are used for monitoring or routing purposes, not for outright rejection of workloads.
Option C, which suggests excluding the foreign servers in an exception, would not achieve the goal, as this would mean those queries are not affected by the exception. The goal here is to include these functions in an exception to actively reject them.


NEW QUESTION # 50
After a recent migration, a request has started to take significant time to complete. Upon a detailed investigation of the EXPLAIN plan, it is found that an accidental unconstrained product join on a very uniformly-distributed large table was the prime reason for the issue. The Administrator needs to use workload management to detect when this request is running.
Which criteria should the Administrator select for this issue?

  • A. CPU Utilization
  • B. CPU Skew
  • C. CPU Disk Ratio
  • D. AWT Wait Time

Answer: B

Explanation:
CPU Skew is a metric that measures the uneven distribution of CPU usage across AMPs (Access Module Processors). In the case of an accidental unconstrained product join on a large, uniformly distributed table, certain AMPs may handle significantly more work than others, leading to high CPU Skew. This skew occurs because the product join results in an inefficient execution plan, where data from the large table is unnecessarily compared row-by-row with another table.
Option A (AWT Wait Time) refers to the time queries spend waiting for available AMP Worker Tasks, but it is not directly related to detecting the inefficiencies caused by product joins.
Option C (CPU Disk Ratio) measures the relationship between CPU usage and disk I/O. While it could indicate inefficiency, it doesn't directly pinpoint product join issues like CPU Skew does.
Option D (CPU Utilization) reflects overall CPU usage but doesn't indicate imbalance across AMPs, which is critical for detecting issues like product joins.


NEW QUESTION # 51
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. Create a new workload, classify on username = BATCH_USER and statement type = CALL, and set a throttle.
  • B. Define an AWT resource limit, classify on username = BATCH_USER and utility = Stored Procedure, and set the limit.
  • 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: A

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 # 52
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 NUSI on Receiptline on each of the columns: ProductTypeId, OrderTypeId, VendorId.
  • C. Create a sparse join index on Receiptline, with CustomerNumber as an input parameter.
  • D. Instead of directly accessing table Receiptline. a global temporary table should be used, that needs to contain prefiltered rows with CustomerNumber = <parameter>.

Answer: C

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 # 53
......

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