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NEW QUESTION: 1
A. ExecuteNonQuery
B. ExecuteScalar
C. ExecuteReader
D. ExecuteDbDataReader
Answer: B
Explanation:
ExecuteScalar
Executes the command, and returns the first column of the first row in the result set. Additional columns or rows are ignored.
NEW QUESTION: 2
Examine the parameters for your database instance:
Which three statements are true about the process of automatic optimization by using cardinality feedback?
A. The optimizer can re optimize a query only once using cardinality feedback.
B. The optimizer does not monitor cardinality feedback if dynamic sampling and multicolumn statistics are enabled.
C. The optimizer automatically changes a plan during subsequent execution of a SQL statement if there is a huge difference in optimizer estimates and execution statistics.
D. After the optimizer identifies a query as a re-optimization candidate, statistics collected by the collectors are submitted to the optimizer.
E. The optimizer enables monitoring for cardinality feedback after the first execution of a query.
Answer: B,C,E
Explanation:
Explanation/Reference:
Explanation:
C: During the first execution of a SQL statement, an execution plan is generated as usual.
D: if multi-column statistics are not present for the relevant combination of columns, the optimizer can fall back on cardinality feedback.
(not B)* Cardinality feedback. This feature, enabled by default in 11.2, is intended to improve plans for repeated executions.
optimizer_dynamic_sampling
optimizer_features_enable
* dynamic sampling or multi-column statistics allow the optimizer to more accurately estimate selectivity of conjunctive predicates.
Note:
* OPTIMIZER_DYNAMIC_SAMPLING controls the level of dynamic sampling performed by the optimizer.
Range of values. 0 to 10
* Cardinality feedback was introduced in Oracle Database 11gR2. The purpose of this feature is to automatically improve plans for queries that are executed repeatedly, for which the optimizer does not estimate cardinalities in the plan properly. The optimizer may misestimate cardinalities for a variety of reasons, such as missing or inaccurate statistics, or complex predicates. Whatever the reason for the misestimate, cardinality feedback may be able to help.
NEW QUESTION: 3
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.
You train and register a machine learning model.
You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.
You need to deploy the web service.
Solution:
Create an AksWebservice instance.
Set the value of the auth_enabled property to True.
Deploy the model to the service.
Does the solution meet the goal?
A. No
B. Yes
Answer: B
Explanation:
Key-based authentication.
Web services deployed on AKS have key-based auth enabled by default. ACI-deployed services have key-based auth disabled by default, but you can enable it by setting auth_enabled = TRUE when creating the ACI web service. The following is an example of creating an ACI deployment configuration with key-based auth enabled.
deployment_config <- aci_webservice_deployment_config(cpu_cores = 1,
memory_gb = 1,
auth_enabled = TRUE)
Reference:
https://azure.github.io/azureml-sdk-for-r/articles/deploying-models.html