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NEW QUESTION: 1
Ann, a user, is unable to access new applications that are assigned to her department in the corporate application store. Which of the following is the MOST likely cause?
A. Poor network connectivity
B. Certificate problem
C. Incorrect user group
D. VPN issues
Answer: C

NEW QUESTION: 2
Database1という名前のAzure SQLデータベースと、HubAおよびHubBという名前の2つのAzureイベントハブがあります。各ソースから消費されるデータを次の表に示します。

ドライバーによるマイルあたりの平均運賃を計算するには、Azure Stream Analyticsを実装する必要があります。
各ソースのStream Analytics入力をどのように構成する必要がありますか？回答するには、回答領域で適切なオプションを選択します。
注：それぞれの正しい選択は1ポイントの価値があります。

Answer:
Explanation:

Explanation:
HubA: Stream
HubB: Stream
Database1: Reference
Reference data (also known as a lookup table) is a finite data set that is static or slowly changing in nature, used to perform a lookup or to augment your data streams. For example, in an IoT scenario, you could store metadata about sensors (which don't change often) in reference data and join it with real time IoT data streams. Azure Stream Analytics loads reference data in memory to achieve low latency stream processing Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-use-reference-data

NEW QUESTION: 3
Which three statements are true the Automatic Tuning Optimizer (ATO)?
A. It picks up resource-intensive SQL statements from the ADDM and recommends the use of materialized views to improve query performance.
B. It identifies the syntactic, semantic, or design problems with structure of SQL statements leading to poor performance and suggests restricting the statements.
C. It identifies the objects with stale or missing statistics and gathers statistics automatically.
D. It investigates the effect of new or modified indexes on the access paths for a workload and recommends running that statistics through the SQL Access Advisor.
E. It identifies resource-intensive SQL statements, runs them through the SQL Tuning Advisor, and implements the recommendations automatically.
F. It recommends a SQL profile to help create a better execution plan.
Answer: A,E
Explanation:
Under tuning mode, the optimizer can take several minutes to tune a single statement. It is both time and resource intensive to invoke Automatic Tuning Optimizer every time a query must be hard-parsed. Automatic Tuning Optimizer is meant for complex and high-load SQL statements that have nontrivial impact on the database.
Automatic Database Diagnostic Monitor (ADDM) proactively identifies high-load SQL statements that are good candidates for SQL tuning (see Chapter 6, "Automatic Performance Diagnostics"). The automatic SQL tuning feature also automatically identifies problematic SQL statements and implements tuning recommendations during system maintenance windows as an automated maintenance task.
The Automatic Tuning Optimizer performs the following types of tuning analysis:
Statistics Analysis SQL Profiling
Access Path Analysis
SQL Structure Analysis
Alternative Plan Analysis
Note:
*Oracle Database uses the optimizer to generate the execution plans for submitted SQL
statements. The optimizer operates in the following modes:
Normal mode
The optimizer compiles the SQL and generates an execution plan. The normal mode
generates a reasonable plan for the vast majority of SQL statements. Under normal mode,
the optimizer operates with very strict time constraints, usually a fraction of a second.
Tuning mode
The optimizer performs additional analysis to check whether it can further improve the plan
produced in normal mode. The optimizer output is not an execution plan, but a series of
actions, along with their rationale and expected benefit for producing a significantly better
plan. When running in tuning mode, the optimizer is known as the Automatic Tuning
Optimizer.