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NEW QUESTION: 1
Which command will display the active configuration?
A. user@router# show
B. user@router> show system commit
C. user@router> show system configuration
D. user@router> show configuration
Answer: D

NEW QUESTION: 2
After studying the figure below, select the correct statement.

A. Gss93.sav is the dataset name.
B. DataSet2 is the dataset name.
C. DataSet2 is the file name.
D. DataSet2 Is both the file and dataset name.
Answer: A

NEW QUESTION: 3

必要に応じて、次のログイン資格情報を使用します。
Azure Username: xxxxx
Azure Password: xxxxx
The following information is for technical support purposes only:
Lab Instance: 10277521
You plan to generate large amounts of real-time data that will be copied to Azure Blob storage.
You plan to create reports that will read the data from an Azure Cosmos DB database.
You need to create an Azure Stream Analytics job that will input the data from a blob storage named storage10277521 to the Cosmos DB database.
To complete this task, sign in to the Azure portal.
Answer:
Explanation:
See the explanation below.
Explanation
Step 1: Create a Stream Analytics job
1. Sign in to the Azure portal.
2. Select Create a resource in the upper left-hand corner of the Azure portal.
3. Select Analytics > Stream Analytics job from the results list.
4. Fill out the Stream Analytics job page.

5. Check the Pin to dashboard box to place your job on your dashboard and then select Create.
6. You should see a Deployment in progress... notification displayed in the top right of your browser window.
Step 2: Configure job input
1. Navigate to your Stream Analytics job.
2. Select Inputs > Add Stream input > Azure Blob storage

3. In the Azure Blob storage setting choose: storage10277521. Leave other options to default values and select Save to save the settings.
Reference:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-quick-create-portal

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 You analyzed TerramEarth's business requirement to reduce downtime, and found that they can achieve a majority of time saving by reducing customers' wait time for parts You decided to focus on reduction of the 3 weeks aggregate reporting time Which modifications to the company's processes should you recommend?
A. Migrate from FTP to SFTP transport, develop machine learning analysis of metrics, and increase dealer local inventory by a fixed factor.
B. Increase fleet cellular connectivity to 80%, migrate from FTP to streaming transport, and develop machine learning analysis of metrics.
C. Migrate from FTP to streaming transport, migrate from CSV to binary format, and develop machine learning analysis of metrics.
D. Migrate from CSV to binary format, migrate from FTP to SFTP transport, and develop machine learning analysis of metrics.
Answer: B
Explanation:
The Avro binary format is the preferred format for loading compressed data. Avro data is faster to load because the data can be read in parallel, even when the data blocks are compressed.
Cloud Storage supports streaming transfers with the gsutil tool or boto library, based on HTTP chunked transfer encoding. Streaming data lets you stream data to and from your Cloud Storage account as soon as it becomes available without requiring that the data be first saved to a separate file. Streaming transfers are useful if you have a process that generates data and you do not want to buffer it locally before uploading it, or if you want to send the result from a computational pipeline directly into Cloud Storage.
References: https://cloud.google.com/storage/docs/streaming
https://cloud.google.com/bigquery/docs/loading-data