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NEW QUESTION: 1
Exhibit:
1 . public class X{
2 . private static int a;
3 .
5 . public static void main (String[] args){
6 . modify (a);
7 . }
8 .
9 . public static void modify (int a) {
1 0. a++;
1 1.}
1 2. }
What is the result?
A. En error "possible undefined variable" at line 5 causes compilation to fail.
B. En error "possible undefined variable" at line 10 causes compilation to fail.
C. The program runs and prints "0"
D. The program runs and prints "1"
E. The program runs but aborts with an exception.
Answer: C
NEW QUESTION: 2
You need to enable telemetry message tracing through the entire IoT solution.
What should you do?
A. Upload IoT device logs by using the File upload feature.
B. Monitor device lifecycle events.
C. Enable the DeviceTelemetry diagnostic log and stream the log data to an Azure event hub.
D. Implement distributed tracing.
Answer: D
Explanation:
IoT Hub is one of the first Azure services to support distributed tracing. As more Azure services support distributed tracing, you'll be able trace IoT messages throughout the Azure services involved in your solution.
Note:
Enabling distributed tracing for IoT Hub gives you the ability to:
Precisely monitor the flow of each message through IoT Hub using trace context. This trace context includes correlation IDs that allow you to correlate events from one component with events from another component. It can be applied for a subset or all IoT device messages using device twin.
Automatically log the trace context to Azure Monitor diagnostic logs.
Measure and understand message flow and latency from devices to IoT Hub and routing endpoints. Start considering how you want to implement distributed tracing for the non-Azure services in your IoT solution.
Reference:
https://docs.microsoft.com/en-us/azure/iot-hub/iot-hub-distributed-tracing
NEW QUESTION: 3
Note: This question is part of a series of questions that use the same scenario. For your convenience, the scenario is repeated in each question. Each question presents a different goal and answer choices, but the text of the scenario is exactly the same in each question in the series.
Start of repeated scenario
You have a Microsoft SQL Server data warehouse instance that supports several client applications.
The data warehouse includes the following tables: Dimension.SalesTerritory, Dimension.Customer, Dimension.Date, Fact.Ticket, and Fact.Order. The Dimension.SalesTerritory and Dimension.Customer tables are frequently updated. The Fact.Order table is optimized for weekly reporting, but the company wants to change it to daily. The Fact.Order table is loaded by using an ETL process. Indexes have been added to the table over time, but the presence of these indexes slows data loading.
All tables are in a database named DB1. You have a second database named DB2 that contains copies of production data for a development environment. The data warehouse has grown and the cost of storage has increased. Data older than one year is accessed infrequently and is considered historical.
The following requirements must be met:
Implement table partitioning to improve the manageability of the data warehouse and to avoid the need
to repopulate all transactional data each night. Use a partitioning strategy that is as granular as possible.
Partition the Fact.Order table and retain a total of seven years of data.
Partition the Fact.Ticket table and retain seven years of data. At the end of each month, the partition
structure must apply a sliding window strategy to ensure that a new partition is available for the upcoming month, and that the oldest month of data is archived and removed.
Optimize data loading for the Dimension.SalesTerritory, Dimension.Customer, and Dimension.Date
tables.
Incrementally load all tables in the database and ensure that all incremental changes are processed.
Maximize the performance during the data loading process for the Fact.Order partition.
Ensure that historical data remains online and available for querying.
Reduce ongoing storage costs while maintaining query performance for current data.
You are not permitted to make changes to the client applications.
End of repeated scenario
You need to implement the data partitioning strategy.
How should you partition the Fact.Order table?
A. Use a granularity of one month.
B. Create 17,520 partitions.
C. Create 1,460 partitions.
D. Create 2,557 partitions.
Answer: D
Explanation:
Explanation/Reference:
Explanation:
We create on partition for each day, which means that a granularity of one day is used.
Note: If we calculate the partitions that are needed, we get: 7 years times 365 days is 2,555. Make that
2,557 to provide for leap years.
From scenario: Partition the Fact.Order table and retain a total of seven years of data.
The Fact.Order table is optimized for weekly reporting, but the company wants to change it to daily.
Maximize the performance during the data loading process for the Fact.Order partition.
Reference: https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-tables- partition
NEW QUESTION: 4
Which two are features of Cisco Unified Communications Manager Business Edition 6000? (Choose two.)
A. It is backwards compatible, which means that it will run on old Cisco MCS hardware
B. It unifies desk phone, wireless phone. IP phone, and instant messaging.
C. It has capacity for 75 to 300 users and supports up to 10 sites
D. It runs on the Cisco MCS 7890 hardware.
E. It supports Cisco Unified Contact Center Express.
Answer: B,E