1V0-41.20 Practice Questions

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NEW QUESTION: 1
A user has created a VPC with CIDR 20.0.0.0/16 using the wizard. The user has created a public subnet CIDR (20.0.0.0/24. and VPN only subnets CIDR (20.0.1.0/24. along with the VPN gateway (vgw-12345. to connect to the user's data centre. Which of the below mentioned options is a valid entry for the main route table in this scenario?
A. Destination: 20.0.0.0/24 and Target: vgw-12345
B. Destination: 0.0.0.0/0 and Target: vgw-12345
C. Destination: 20.0.1.0/16 and Target: vgw-12345
D. Destination: 20.0.0.0/16 and Target: ALL
Answer: B
Explanation:
The user can create subnets as per the requirement within a VPC. If the user wants to connect VPC from his own data centre, he can setup a public and VPN only subnet which uses hardware VPN access to connect with his data centre. When the user has configured this setup with Wizard, it will create a virtual private gateway to route all traffic of the VPN subnet. Here are the valid entries for the main route table in this scenario: Destination: 0.0.0.0/0 & Target: vgw-12345 (To route all internet traffic to the VPN gateway. Destination: 20.0.0.0/16 & Target: local (To allow local routing in VPC.
Topic 3, Volume C

NEW QUESTION: 2
View the Exhibit to examine the output produced by the following query at three different times since the database instance started and has experienced workloads of different capacities:

What do you infer from this?
Exhibit:

A. All sessions are connected to the database instance in dedicated mode, and no RMAN or parallel query operations have been performed.
B. The database instance is running with manual shared memory management.
C. The database instance is running with manual PGA management.
D. The database instance has the MEMORY_TARGET value set to a nonzero value.
Answer: D
Explanation:
Explanation/Reference:
Explanation:
MEMORY_TARGET specifies the Oracle system-wide usable memory. The database tunes memory to the MEMORY_TARGET value, reducing or enlarging the SGA and PGA as needed.
In a text-based initialization parameter file, if you omit MEMORY_MAX_TARGET and include a value for MEMORY_TARGET, then the database automatically sets MEMORY_MAX_TARGET to the value of MEMORY_TARGET. If you omit the line for MEMORY_TARGET and include a value for MEMORY_MAX_TARGET, the MEMORY_TARGET parameter defaults to zero. After startup, you can then dynamically change MEMORY_TARGET to a nonzero value, provided that it does not exceed the value of MEMORY_MAX_TARGET.

NEW QUESTION: 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.
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. BigQuery and Cloud Bigtable
B. Cloud Bigtable and Cloud SQL
C. BigQuery and Cloud Storage
D. Cloud Datastore and Cloud Bigtable
Answer: A