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NEW QUESTION: 1
MJTelco Case Study
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to-many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
Scale and harden their PoC to support significantly more data flows generated when they ramp to more

than 50,000 installations.
Refine their machine-learning cycles to verify and improve the dynamic models they use to control

topology definition.
MJTelco will also use three separate operating environments - development/test, staging, and production
- to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
Scale up their production environment with minimal cost, instantiating resources when and where

needed in an unpredictable, distributed telecom user community.
Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.

Provide reliable and timely access to data for analysis from distributed research workers

Maintain isolated environments that support rapid iteration of their machine-learning models without

affecting their customers.
Technical Requirements
Ensure secure and efficient transport and storage of telemetry data
Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately
100m records/day
Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis.
Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud's machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
MJTelco is building a custom interface to share data. They have these requirements:
1. They need to do aggregations over their petabyte-scale datasets.
2. They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?
A. Cloud Bigtable and Cloud SQL
B. BigQuery and Cloud Storage
C. BigQuery and Cloud Bigtable
D. Cloud Datastore and Cloud Bigtable
Answer: C

NEW QUESTION: 2
Überblick
Der folgende Abschnitt der Prüfung ist ein Labor. In diesem Abschnitt führen Sie eine Reihe von Aufgaben in einer Live-Umgebung aus. Während Ihnen die meisten Funktionen wie in einer Live-Umgebung zur Verfügung stehen, sind einige Funktionen (z. B. Kopieren und Einfügen, Navigieren zu externen Websites) nicht beabsichtigt.
Die Bewertung basiert auf dem Ergebnis der Ausführung der im Labor angegebenen Aufgaben. Mit anderen Worten, es spielt keine Rolle, wie Sie die Aufgabe ausführen. Wenn Sie sie erfolgreich ausführen, erhalten Sie für diese Aufgabe eine Gutschrift.
Die Labore sind nicht separat geplant, und diese Prüfung kann mehr als ein Labor umfassen, das Sie absolvieren müssen. Sie können so viel Zeit verwenden, wie Sie für jedes Labor benötigen. Sie sollten Ihre Zeit jedoch angemessen verwalten, um sicherzustellen, dass Sie die Labore und alle anderen Abschnitte der Prüfung in der angegebenen Zeit abschließen können.
Bitte beachten Sie, dass Sie nach dem Einreichen Ihrer Arbeit durch Klicken auf die Schaltfläche Weiter in einem Labor NICHT mehr zum Labor zurückkehren können.
Um das Labor zu starten
Sie können das Labor starten, indem Sie auf die Schaltfläche Weiter klicken.
Sie haben kürzlich eine virtuelle Maschine mit dem Namen Web01 erstellt.
Sie müssen eine neue 80-GB-Standarddatenplatte mit dem Namen Web01-Disk1 an Web01 anschließen.
Was sollten Sie über das Azure-Portal tun?
Answer:
Explanation:
See explanation below.
Explanation
Add a data disk
Step 1. In the Azure portal, from the menu on the left, select Virtual machines.
Step 2. Select the Web01 virtual machine from the list.
Step 3. On the Virtual machine page, , in Essentials, select Disks.

Step 4. On the Disks page, select the Web01-Disk1 from the list of existing disks.
Step 5. In the Disks pane, click + Add data disk.
Step 6. Click the drop-down menu for Name to view a list of existing managed disks accessible to your Azure subscription. Select the managed disk Web01-Disk1 to attach:

References:
https://docs.microsoft.com/en-us/azure/virtual-machines/linux/attach-disk-portal

NEW QUESTION: 3
BIAppUserという名前のログインを作成します。ログインはレポートデータベースにアクセスできる必要があります。
データベース内のBIAppUserログインへのアクセスを許可する必要があります。
Transact-SQLステートメントをどのように完成させるべきですか？回答するには、適切なTransact-SQLセグメントを正しい場所にドラッグします。各Transact-SQLセグメントは、1回、複数回、またはまったく使用しないことができます。コンテンツを表示するには、ペイン間の分割バーをドラッグするか、スクロールする必要があります。

Answer:
Explanation:

Explanation

Box 1: Reporting
The user is to be created in the Reporting database.
Box 2: CREATE USER
Box 3: FOR LOGIN [BIAppUser]
Users are created per database and are associated with logins. You must be connected to the database in where you want to create the user. Here is some sample Transact-SQL that creates a user:
CREATE USER readonlyuser FROM LOGIN readonlylogin;
References: https://azure.microsoft.com/en-us/blog/adding-users-to-your-sql-azure-database/