Google Cloud Digital Leader Practice Test - Questions Answers, Page 12
List of questions
Question 111
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Your team is working on building a machine learning model. There are a bunch of terminologies that are being used. What is an "instance" or an "example"?
Explanation:
One row of a dataset containing one or more input columns and possibly a prediction result.
https://developers.google.com/machine-learning/guides/rules-of-ml#terminology
Question 112
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A retail store has discovered a cost-effective solution for creating self-service kiosks. They can use existing check-out hardware and purchase a virtual customer service application. Why do they also need an API?
Explanation:
APIs can create new business value by connecting legacy systems (the checkout hardware) with new software (the virtual customer service application).
Question 113
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Your customer is making a decision on whether to move to Google Cloud. Their key concern is about 10,000 VMs that are part of their IT infrastructure used across more than 110 applications. They are apprehensive of too many changes at this stage. They want to get to Google Cloud in the easiest way possible with minimal disruption. What option would you recommend for them?
Explanation:
Migrate for Compute Engine's advanced replication migration technology copies instance data to Google Cloud in the background with no interruptions to the source workload that's running.
https://cloud.google.com/migrate/compute-engine
Question 114
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You are leading projects in an IT services company. Your customer's project requires analyzing images.
They have many 10s of 1000s of raw images that they have made available to you. Your small technology team needs to build a machine learning model. The images are unlabeled. You don't have the people or the capacity to label the images. What is your approach?
Explanation:
Google's Data Labeling Service lets you work with human labelers to generate highly accurate labels for a collection of data that you can use in machine learning models.
References:
-> https://cloud.google.com/vertex-ai/docs/datasets/data-labeling-job-> https://cloud.google.com/ai-platform/data-labeling/docs
Question 115
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You are working with the head of the IT team and planning the move of computing systems. The questionnaire indicates that they have a reporting application that runs almost 24 hours every day of the week. When there is extra load, it queues up the processing and executes tasks when there is less demand. Which of these compute options would you recommend for them?
Explanation:
- Because Compute Engine VMs are the preferred compute option as they are long-running.
Question 116
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With respect to the Core Feature of Standby Instances of Cloud SQL which one of the options is correct.?
Explanation:
The standby instance is used in high availability to replace the primary instance when failover occurs.
The standby instance doesn't appear in the Google Cloud Console. When failover occurs, connections to the primary instance are automatically transferred to the standby instance.
Cloud SQL Key Terms:
Cloud SQL instance
A Cloud SQL instance corresponds to one virtual machine (VM). The VM includes the database instance and accompanying software containers to keep the database instance up and running.
Database instance
A database instance is the set of software and files that operate the databases: MySQL, PostgreSQL or SQL Server.
High availability
Cloud SQL instances using high availability (HA) provide greater reliability than non-HA instances.
HA in Cloud SQL works by having two synchronized instances: a primary instance and a standby instance. Each instance has exactly one VM. Each instance is in a different zone in the same region.
Failover
A failover is when Cloud SQL switches serving from the original primary instance to the standby instance.
Autofailover is a mechanism that automatically triggers failover when a Cloud SQL instance didn't issue a heartbeat in the previous interval.
Standby instances
The standby instance is used in high availability to replace the primary instance when failover occurs.
The standby instance doesn't appear in the Google Cloud Console. When failover occurs, connections to the primary instance are automatically transferred to the standby instance.
Clone
When you clone a Cloud SQL instance, you create a new instance that is a copy of the source instance, but is completely independent. After cloning is complete, changes to the source instance are not reflected in the clone, and changes in the clone are not reflected in the source instance.
Replication
Replication is the ability to create copies of a Cloud SQL instance or an on-premises database, and offload work to the copies. The main reason for using replication is to scale the use of data in a database without degrading performance on the primary instance.
Read replica
The read replica is an exact copy of the primary instance. Data and other changes on the primary instance are updated in almost real time on the read replica. Send your write transactions to the primary instance, and your read requests to the read replica. The read replica processes queries, read requests, and analytics traffic, thus reducing the load on the primary instance.
Source server
Replication copies transactions from a primary instance to one or more read replicas. The primary instance is also called the source server. The source server can be a Cloud SQL primary instance, or a server outside of Google Cloud, such as an on-premises server or a server running in a different cloud. If the source server is outside of Google Cloud, we call it Replication from an external server.
Cloud SQL Auth proxy client
The Cloud SQL Auth proxy client is open source software maintained by Cloud SQL. It connects to a companion process, the Cloud SQL Auth proxy server, running on your Cloud SQL instance. You run the Cloud SQL Auth proxy client on your own servers. The Cloud SQL Auth proxy client can be used to establish a secure SSL/TLS connection to the database instance, and/or to avoid having to open the firewall. Authentication is done through Identity and Access
Management (IAM).
Question 117
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You are a database manager working for a new product that will need millions of reading and writing from the database, with zero downtime, key-value i.e. NoSQL features, no manual steps should be required to ensure consistency, repair data, synchronize writes and deletes, Which of the following database you choose?
Explanation:
Cloud BigTable
Key features
High throughput at low latency
Bigtable is ideal for storing very large amounts of data in a key-value store and supports high read and write throughput at low latency for fast access to large amounts of data. Throughput scales linearlyóyou can increase QPS (queries per second) by adding Bigtable nodes. Bigtable is built with proven infrastructure that powers Google products used by billions such as Search and Maps.
Cluster resizing without downtime
Scale seamlessly from thousands to millions of reads/writes per second. Bigtable throughput can be dynamically adjusted by adding or removing cluster nodes without restarting, meaning you can increase the size of a Bigtable cluster for a few hours to handle a large load, then reduce the cluster's size againóall without any downtime.
Flexible, automated replication to optimize any workload
Write data once and automatically replicate where needed with eventual consistencyógiving you control for high availability and isolation of reading and write workloads. No manual steps are needed to ensure consistency, repair data, or synchronize writes and deletes. Benefit from a high availability SLA of 99.999% for instances with multi-cluster routing across 3 or more regions (99.9% for single-cluster instances).
Question 118
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In terms of Cloud SQL for MySQL Features offered by Google Cloud Platform which of the statements is/are correct?
Explanation:
Cloud SQL for MySQL:
Features
- Fully managed MySQL Community Edition databases in the cloud.
- Cloud SQL instances support MySQL 8.0, 5.7 (default), and 5.6, and provide up to 624 GB of RAM and 64 TB of data storage, with the option to automatically increase the storage size, as needed.
- Create and manage instances in the Google Cloud Console.
- Instances are available in the Americas, EU, Asia, and Australia.
- Customer data is encrypted on Google's internal networks and in database tables, temporary files, and backups.
- Support for secure external connections with the Cloud SQL Auth proxy or with the SSL/TLS protocol.
- Support for private IP (private services access).
- Data replication between multiple zones with automatic failover.
- Import and export databases using mysqldump, or import and export CSV files.
- Support for MySQL wire protocol and standard MySQL connectors.
- Automated and on-demand backups and point-in-time recovery.
- Instance cloning.
- Integration with Google Cloud's operations suite logging and monitoring.
Question 119
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What issues can arise when organizations integrate third-party systems into their cloud infrastructure?
Explanation:
Because unsecured third-party systems are a cybersecurity threat.
Question 120
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A customer of yours has an SLA with their client that a particular service will respond within 4 seconds.
The end client has reported that it feels slower. Your engineers do a trial at the client site and notice that there seems to be a delay for many of the requests. It's your team's responsibility to identify the issue quickly within the strict timeline for fixes according to the contract, and then fix it. What should you do?
Explanation:
Cloud Trace is a built-in tool in the Operations suite to identify issues like latency.
-> Such fixes are unlikely to change core issues like the service itself being architected or written suboptimally.
Though changes like browser, networking, etc. are helpful, it would be the wrong approach to first recommend that the customer upgrade all their hardware and software.
-> Rewriting code and logging information is going to be time consuming. In general though, logging should always be included in code and it can give good insights. But tracing is way more specific and comprehensive for this requirement.
-> In certain cases, we might identify scaling as the issue. But we should first identify the core problem. So, start with tracing. We can also achieve scale in server-ful technologies.
Reference link- https://cloud.google.com/trace
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