- Cloud Dataﬂow takes a query and runs information from the database through it to produce tables of data organized according to the requirements of the original query.
- Cloud Dataﬂow relies on a large database to store and analyze data processing pipelines, performing transforms resulting in predictive analytics that can be leveraged to optimize business decisions.
- Cloud Dataﬂow relies on training data to enable machine learning that can then read multiple streams of data and perform transforms that produce resulting output data.
- Cloud Dataﬂow reads data in and can apply ﬁltering, grouping, comparing, joining, or aggregation
Other Google Cloud Platform Certification Question Answers
Which statement best describes where Cloud Dataproc falls on the big data processing model and the role it plays?
Publisher applications can receive messages from a topic. Subscriber applications can subscribe to a topic to receive the message when the subscriber is ready. Publisher applications can send messages to a subscriber. Subscriber applications can send messages on a topic directly to publisher applications.
Which of these statements about the Publisher-Subscriber pattern utilized by Cloud Pub/Sub is TRUE?
What is the value that Cloud Pub/Sub provides? Select the 2 correct answers.
An organization’s analysts use Spark Shell. However, their IT department is concerned about the increase in usage and how to scale their cluster, which is running in Standalone mode. How does Cloud Dataproc help?
Market datasets QA datasets Training datasets Predictive datasets
What dataset type is vital to machine learning?
The Cloud Dataproc approach allows organizations to use Hadoop/Spark/Hive/Pig when needed. It takes on average only 90 seconds between the moment resources are requested and a job can be submitted. What makes this possible?
Which of these statements best expresses what you can do with Cloud Dataﬂow?
A query is how you retrieve information from a database, so which of these paths demonstrates the journey of a query?
Cloud IoT is a set of fully managed and integrated services that allows organizations to easily and securely connect, manage, and collect data from devices across the globe at a large scale. Knowing this, what stage of big data processing does Cloud IoT belong in?
High PUC cores and GPUs Ease of use and speed Idle clusters and scaling inﬂexibility Integration and customization
What are some business challenges that Cloud Dataproc addresses?
What does Cloud Dataproc do to help organizations avoid expensive underutilized clusters?
Process queries written in structured query language (SQL). Perform the transformations in “extract, transform, and load (ETL).” Scale without downtime. Develop apps faster and easier with cloud backend services.
Cloud Dataflow is a tool for developing and executing a wide range of data processing patterns on very large datasets. Which of these examples aligns with what Cloud Dataﬂow can do?
Cloud Dataflow is a fully managed service for transforming and enriching data in stream (real time) and batch (historical) modes with equal reliability and expressiveness. Knowing this, where does Cloud Dataﬂow ﬁt in the big data processing model?
What business challenges does Cloud IoT address? Select the 3 correct answers.
How is BigQuery ideal for organizations that run a data warehouse?
For organizations that want a large-scale machine learning service, select the value ML provides.
What business value can BigQuery provide?
Eliminates the need to buy, build, and operate computing hardware. Getting queries answered rapidly over very large data sets. Accelerates development for batch and streaming data processing pipelines. Allows for fast SQL queries on structured data.
What is the value that Cloud Dataflow can provide?
Within the big data processing model, which description deﬁnes where Cloud Pub/Sub falls and the role it plays?
BigQuery has the ability to scale seamlessly; what is another beneﬁt when it comes to infrastructure?
Likened to a connector, which description best aligns to Cloud Pub/Sub’s role in GCP?
Which challenge is Cloud IoT designed to address?
What is the business value that Cloud Pub/Sub can provide?
BigQuery can bring in other Google products because within the common big data processing model, BigQuery is found in the ____________ phases.
Which of these statements best describes the kinds of transforms a Cloud Dataﬂow pipeline can do?
What is Cloud SQL?
What is a common use case for moving data to GCP? Select the 2 correct answers.
What business challenges does Cloud Bigtable address? Select the 2 correct answers.
Cloud Firestore is good for:
Which Cloud Storage class is meant for organizations with the lowest frequency access?
With Cloud Firestore, you no longer need to determine the number of nodes or add servers or storage because:
Cloud Storage is good for:
Which statement best defines where data transfers ﬁt within the GCP?
Which of the following describes Cloud Firestore’s business value?
Why is Cloud Bigtable great for IoT and user analytics?
What type of database is Cloud SQL?
Which of these statements about Cloud Datastore is FALSE?
“High availability and durability via replications is just there; you don’t need to think about it or manage it. No scheduled downtime for upgrades, resizing or conﬁguration changes.” Which of the following concerns is addressed by this Cloud Firestore value proposition?
Using your understanding of GCP options/products meant to address storage and database areas, which statement best aligns with where Cloud Bigtable ﬁts within GCP?
“Cloud Datastore enables you to hand off the responsibility of running a highly scalable document-oriented database to Google. From 10 users to 100s of millions of users, you will no longer need to spend time optimizing, conﬁguring the database, updating it, or monitoring system health.” Which of the following concerns is addressed by this Cloud Datastore value proposition?
Cloud Storage ____________ object (or BLOB) data. Organizations can store an unlimited number of objects, up to 5 ___________ in size each.
What is Cloud Bigtable NOT good for?
How does BigQuery Data Transfer Service work?
How does Cloud Storage help organizations get content to their users faster?
How does Cloud Storage deliver simplicity?
What is an example of a time-consuming task required to set up and run a database that organizations can hand off to Google when they choose to use Cloud SQL?
What aspect of Cloud Datastore allows organizations to worry less about making changes to their underlying data structure as their application evolves?
Non-traditional transforms and standard tools like Hadoop. Standard connectors and standard tools like MySQL workbench. Standard containers and standard tools like BigQuery. Non-traditional connectors and standard tools like PostgreSQL BETA.
An industry-leading local SDD that integrates with the other products in the Google Cloud Platform suite. An enterprise-grade database service built for the cloud speciﬁcally to combine the beneﬁts of relational database structure with relational horizontal scale. A ﬂexible, scalable NoSQL cloud database to store and sync data for client- and server-side development. A tool for developing and executing a wide range of data processing patterns on very large datasets.
Cloud Firestore is:
What are some common challenges that can be addressed by data transfer options?
What is a data transfer option offered by Google to help move data?
Cloud Spanner captures up to a petabyte of data on one Transfer Appliance without impacting the outbound network. Cloud Spanner stores and serves an unlimited number of objects and up to 5 terabytes of object data for each. Cloud Spanner automates loading data into BigQuery from YouTube, AdWords, and DoubleClick. Cloud Spanner uses synchronous replication within and across regions to achieve greater availability.
With Cloud Spanner, if one region goes offline, data can still be served from another region. How does Cloud Spanner make this possible?
Considering where it fits in the GCP, Cloud Datastore is good for:
Cloud Spanner is:
How does Cloud SQL address the challenge of compatibility?
Cloud Spanner is good for conﬁguring replications and applying updates. Cloud Spanner is good for object database management at scale that requires the capacity to process millions of objects in near real time. Cloud Spanner is good for binary data such as images, media serving, and backups. Cloud Spanner is good for relational database management systems at scale that require high availability and HTAP.
Which statement best aligns with the role Cloud Spanner plays in GCP?
Organizations considering Cloud Spanner often ﬁnd ACID transactions valuable. What are ACID transactions?
Firebase is GCP’s mobile extension. Several of Firebase’s products are built on top of GCP products, providing mobile-optimized SDKs and capabilities. Which of these products can be amplified via Firebase?
Build their own infrastructure. Make sure it’s available night and day. Control over the virtual-machine infrastructure. Grow the app as their computing needs increase.
During the app building process, which of these challenges are relieved by App Engine that organizations would have previously had to do on their own? Select the 3 correct answers.
How does Compute Engine improve quality and time to market?
What service do organizations receive with App Engine that meets key developer needs?
Of the four GCP options that are aligned with where the code needs to run, App Engine is:
What is the definition of a “network” in relation to GCP?
Which of the following is a benefit provided by Cloud Functions?
What are some challenges that Firebase addresses? Select the 2 correct answers.
What is Kubernetes Engine?
Compared to Compute Engine and App Engine, what type of solution is Kubernetes Engine?
Which of these is an example of a challenge addressed by Cloud Functions?
How does Compute Engine provide the same computing power that Google uses for its infrastructure?
Which product included in Google Cloud networking allows organizations to connect GCP resources in a separate network or domain and isolate them from each other for security and compliance?
Firebase helps mobile application development teams:
What business value can Kubernetes Engine provide?
Mobile app teams can integrate their mobile apps with one of the available __________ and manage the integrated Firebase services via _____________ which includes tools from Google for developing apps, engaging with users, and earning more through mobile ads.
What business value can Compute Engine provide?
Which of the following best defines Compute Engine?
Which of these statements best expresses what you can do with Cloud Functions?
Which statement best describes where Google Cloud networking fits within GCP?
What is a software container?
How does Cloud Functions connect and extend cloud services?
What challenges are addressed by Google Cloud networking?
Organizations can build apps that run on Kubernetes Engine that: (Select the 2 correct answers)
What business value can Firebase provide?
Compute Engine is ideal for organizations that:
Which product included in Google Cloud networking serves content to end users with high availability and high performance?
As a computing platform provided as a service, how does App Engine provide value?
Which of these key terms indicate a business-related challenge that App Engine addresses?