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NEW QUESTION: 1
You have the following code (line numbers are included for reference only):

You need to identify the missing line of code at line 15. Which line of code should you identify?
A. using (fooSqlConn.BeginTransaction())
B. while (fooSqlReader.GetBoolean(0))
C. while (fooSqlReader.Read())
D. while (fooSqlReader.NextResult())
Answer: C

NEW QUESTION: 2
顧客は、プロジェクト要件を継続的に変更します。
どれはこの要件のためのベストプロジェクト管理方法論ですか？
A. Waterfall
B. PRINCE2
C. Agile
D. Bottom-up
Answer: C
Explanation:
References:
Kim Heldman, CompTIA Project+ Study Guide, 2nd Edition, Sybex, Indianapolis, 2017, p. 310

NEW QUESTION: 3
Ein Unternehmen entwickelt eine Lösung, mit der intelligente Kühlschränke Temperaturinformationen an einen zentralen Ort senden können.
Die Lösung muss Nachrichten empfangen und speichern, bis sie verarbeitet werden können. Sie erstellen eine Azure Service Bus-Instanz, indem Sie einen Namen, eine Preisstufe, ein Abonnement, eine Ressourcengruppe und einen Speicherort angeben.
Sie müssen die Konfiguration abschließen.
Welchen Azure CLI- oder PowerShell-Befehl sollten Sie ausführen?

A. Option B
B. Option A
C. Option D
D. Option C
Answer: A
Explanation:
Explanation
# Create a Service Bus messaging namespace with a unique name. Example:
namespaceName=myNameSpace$RANDOM
az servicebus namespace create --resource-group $resourceGroupName --name $namespaceName --location
eastus
References:
https://docs.microsoft.com/en-us/azure/service-bus-messaging/service-bus-quickstart-cli

NEW QUESTION: 4
Case Study: 2 - TerramEarth Case Study
Company Overview
TerramEarth manufactures heavy equipment for the mining and agricultural industries: About
80% of their business is from mining and 20% from agriculture. They currently have over 500 dealers and service centers in 100 countries. Their mission is to build products that make their customers more productive.
Company Background
TerramEarth formed in 1946, when several small, family owned companies combined to retool after World War II. The company cares about their employees and customers and considers them to be extended members of their family.
TerramEarth is proud of their ability to innovate on their core products and find new markets as their customers' needs change. For the past 20 years trends in the industry have been largely toward increasing productivity by using larger vehicles with a human operator.
Solution Concept
There are 20 million TerramEarth vehicles in operation that collect 120 fields of data per second.
Data is stored locally on the vehicle and can be accessed for analysis when a vehicle is serviced.
The data is downloaded via a maintenance port. This same port can be used to adjust operational parameters, allowing the vehicles to be upgraded in the field with new computing modules.
Approximately 200,000 vehicles are connected to a cellular network, allowing TerramEarth to collect data directly. At a rate of 120 fields of data per second, with 22 hours of operation per day.
TerramEarth collects a total of about 9 TB/day from these connected vehicles.
Existing Technical Environment

TerramEarth's existing architecture is composed of Linux-based systems that reside in a data center. These systems gzip CSV files from the field and upload via FTP, transform and aggregate them, and place the data in their data warehouse. Because this process takes time, aggregated reports are based on data that is 3 weeks old.
With this data, TerramEarth has been able to preemptively stock replacement parts and reduce unplanned downtime of their vehicles by 60%. However, because the data is stale, some customers are without their vehicles for up to 4 weeks while they wait for replacement parts.
Business Requirements
- Decrease unplanned vehicle downtime to less than 1 week, without
increasing the cost of carrying surplus inventory
- Support the dealer network with more data on how their customers use
their equipment IP better position new products and services.
- Have the ability to partner with different companies-especially with
seed and fertilizer suppliers in the fast-growing agricultural
business-to create compelling joint offerings for their customers
CEO Statement
We have been successful in capitalizing on the trend toward larger vehicles to increase the productivity of our customers. Technological change is occurring rapidly and TerramEarth has taken advantage of connected devices technology to provide our customers with better services, such as our intelligent farming equipment. With this technology, we have been able to increase farmers' yields by 25%, by using past trends to adjust how our vehicles operate. These advances have led to the rapid growth of our agricultural product line, which we expect will generate 50% of our revenues by 2020.
CTO Statement
Our competitive advantage has always been in the manufacturing process with our ability to build better vehicles for tower cost than our competitors. However, new products with different approaches are constantly being developed, and I'm concerned that we lack the skills to undergo the next wave of transformations in our industry. Unfortunately, our CEO doesn't take technology obsolescence seriously and he considers the many new companies in our industry to be niche players. My goals are to build our skills while addressing immediate market needs through incremental innovations.
For this question, refer to the TerramEarth case study. TerramEarth's 20 million vehicles are scattered around the world. Based on the vehicle's location its telemetry data is stored in a Google Cloud Storage (GCS) regional bucket (US. Europe, or Asia). The CTO has asked you to run a report on the raw telemetry data to determine why vehicles are breaking down after 100 K miles. You want to run this job on all the data. What is the most cost-effective way to run this job?
A. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a multi region bucket and use a Dataproc cluster to finish the job.
B. Move all the data into 1 region, then launch a Google Cloud Dataproc cluster to run the job.
C. Launch a cluster in each region to preprocess and compress the raw data, then move the data into a regional bucket and use a Cloud Dataproc cluster to finish the job.
D. Move all the data into 1 zone, then launch a Cloud Dataproc cluster to run the job.
Answer: A
Explanation:
Storageguarantees 2 replicates which are geo diverse (100 miles apart) which can get better remote latency and availability.
More importantly, is that multiregional heavily leverages Edge caching and CDNs to provide the content to the end users.
All this redundancy and caching means that Multiregional comes with overhead to sync and ensure consistency between geo-diverse areas. As such, it's much better for write-once-read- many scenarios. This means frequently accessed (e.g. "hot" objects) around the world, such as website content, streaming videos, gaming or mobile applications.
References: https://medium.com/google-cloud/google-cloud-storage-what-bucket-class-for-the- best-performance-5c847ac8f9f2