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Java SE 11 Developer

  • Exam Number/Code : 1z1-819
  • Exam Name : Java SE 11 Developer
  • Questions and Answers : 213 Q&As
  • Update Time: 2019-01-10
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NEW QUESTION: 1
A customer has setup IBM Security Access Manager V9.0 hardware appliances (AP. A1, A2) in a cluster. The appliances (A1, A2) that host reverse proxies are labeled as
"restricted" in the cluster. AP is the Primary of the cluster.
There is one reverse proxy in appliance A1 and two in appliance A2. These reverse proxies are being tuned for performance. The appliance A1 and A2 max socket range has been set to 64510.
What is true in this situation?
A. Appliance A2 with two reverse proxies will perform better than Appliance A1 with one reverse proxy
B. The reverse proxy in appliance A1 can have a maximum thread count of 30,000
C. Both reverse proxies in appliance A2 can have a maximum thread count of 30,000.
D. The appliance AP cannot be used for running reverse proxies to balance load
Answer: B

NEW QUESTION: 2
You need to make all of the rows in the table bold in the Views/RunLog/GetLog.cshtml view.
Which code segment should you use?
A. Table > th:last-child { font-weight: bold; }
B. Table+first-child{ font-weight: bold; }
C. Table>tr>th:nth-child{2){font-weight: bold; }
D. Table > tr {font-weight: bold;}
Answer: D
Explanation:
Topic 2, Web Application
Background
You are developing an online shopping web application.
Business Requirements A user is not required to provide an email address. If a user enters an email address, it must be verified to be a valid email address. Information about the first product on the product page must fade out over time to encourage the user to continue browsing the catalog.
--
--
Administrators must be able to edit information about existing customers. Administrators also must be able to specify a default product on the product page.
Technical Requirements
General:
The web store application is in a load-balanced web farm. The load balancer is not
configured to use server affinity.
The web store application is an ASP.NET MVC application written in Visual Studio
2012.
Products:
--
--
The value of the productId property must always be greater than 0.
The Products page for mobile devices must display to mobile users. The Products
page for desktop devices must display to desktop users.
Storage:
--
The data must be stored in a serialized XML data format. Serialized objects must be schema-independent.
Exception handling:
-
Exceptions originating from IIS must display a page with support contact information.
--
Some page links expire, and users who access these links encounter 404 errors. Exceptions must be logged by using the WriteLog method of the Utility class.
--
Browser and device support:
The application must support image format conversions from .bmp to .jpeg for
mobile devices.
The application must support image format conversions from .bmp to .png for
desktop devices.
Application Structure



NEW QUESTION: 3
A statement-level access path hint can be applied to which type of SQL statement?
A. Statements included in a CREATE PROCEDURE statement only.
B. Both static and dynamic SQL statements.
C. Dynamic SQL statements only.
D. Static SQL statements only.
Answer: C

NEW QUESTION: 4
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?
A. Use the NOW () function in BigQuery to record the event's time.
B. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
C. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.
D. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
Answer: B
Explanation:
Topic 3, 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.