Google Cloud Platform Business Professional Accreditation – GCP Big Data and Machine Learning assessment answers

Here you’ll find all possible real exam questions & answers for the latest GCP Big Data and Machine Learning assessment which is a part of Google Cloud Platform Business Professional Accreditation available in Google Skillshop.

Link to the official certification exam page:  Google Cloud Platform Business Professional Accreditation.

Which of these statements best describes the kinds of transforms a Cloud Dataflow pipeline can do?

 

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

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BigQuery can bring in other Google products because within the common big data processing model, BigQuery is found in the ____________ phases.

 

Ingest and Storage
Apps and Devices
Storage and Analyze
Ingest and Process

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What is the business value that Cloud Pub/Sub can provide?

 

Availability, throughput, and latency
Sources, sinks, and transforms
VMs, network, and Non-SQL
Queries, machine learning, and compute

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Which challenge is Cloud IoT designed to address?

 

Organizations find it difficult to stay ahead when they continuously have to accommodate new data sources and more data without sacrificing efficiency.
Accepting that most devices can theoretically be connected to a network, building and managing such networks in a global, secure way—and then getting data out of them for analysis—is complex and difficult for organizations.
Multiple data marts are inefficient: they are complex and costly, and they make data difficult to use.
Organizations that want to take advantage of machine learning need to centralize their data with a managed data store that can consolidate structured and semi-structured data.

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Likened to a connector, which description best aligns to Cloud Pub/Sub’s role in GCP?

 

Cloud Pub/Sub takes the existing data processing pipeline and processes it alongside an incoming stream of input data, performs transforms on that data to gain useful or actionable insights, and produces resulting output data.
Cloud Pub/Sub helps capture data and rapidly pass massive numbers of messages securely between other Google Cloud Platform big data tools and other software applications.
Cloud Pub/Sub offers a solution for analyzing big data and can open the door for other Google Cloud Platform big data tools.
Cloud Pub/Sub allows organizations to access their data anywhere, anytime as an innovative storage solution in the cloud, acting as a repository of data collected by other Google Cloud Platform big data tools.

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BigQuery has the ability to scale seamlessly; what is another benefit when it comes to infrastructure?

 

Almost NoOps, with downtime-free upgrades and maintenance
No queries
Empty space storage
ZeroOps, with governance or maintenance required

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Within the big data processing model, which description defines where Cloud Pub/Sub falls and the role it plays?

 

Cloud Pub/Sub ingests event streams from anywhere, at any scale, for simple, reliable, real-time stream analytics.
Cloud Pub/Sub stores data and ensures accessibility without compromising security.
Cloud Pub/Sub processes queries, running them against a database of data to produce tables of the results.
Cloud Pub/Sub analyzes data to capture insights to be used for more informed decision making.

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What is the value that Cloud Dataflow can 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.

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Which of these benefits correspond to business challenges that are addressed by Cloud IoT?

 

Cloud IoT provides compute power across the globe at nearline locations.
Cloud IoT scales with big data workloads so that organizations can collect more data from more devices.
Cloud IoT facilitates machine learning with actionable insights by processing and analyzing data in real time.
Cloud IoT enables growth through a better user experience that can increase usage and adoption of a product.

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What business value can BigQuery provide?

 

Customer 360 analysis and log analysis
Availability, security, and preferred locations
Machine learning APIs and Tensorflow
Massively parallel databases and multiple data marts

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For organizations that want a large-scale machine learning service, select the value ML provides.

 

Cloud Machine Learning Engine makes it easy to build sophisticated, large-scale machine learning models across a broad set of scenarios.
Cloud Job Discovery provides a highly intuitive job search that anticipates what job seekers are looking for and surfaces targeted recommendations that help them discover new opportunities.
Cloud Translation API provides a simple programmatic interface for translating an arbitrary string into any supported language.
Cloud Video Intelligence API makes videos searchable and discoverable by extracting metadata, identifying key nouns, and annotating the content of the video.

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How is BigQuery ideal for organizations that run a data warehouse?

 

BigQuery isolates data for machine learning.
BigQuery improves analytics, lowers warehousing costs, and includes connectivity to other GCP products.
BigQuery lets data analysts run data processsing pipelines to do transforms on incoming streaming data.
BigQuery connects globally distributed industrial devices into a single network that can be managed efficiently.

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What business challenges does Cloud IoT address? Select the 3 correct answers.

 

Growth
Massively parallel databases
Optimizes cost
Reduces risk

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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?

 

Storage
Analyze
Ingest
Process

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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?

 

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.

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Google Cloud’s AI provides modern machine learning services, with ________ models and a service to generate your own __________ models.

 

virtual machine, dataset
scalable, batch
storage, process
pre-trained, tailored

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What does Cloud Dataproc do to help organizations avoid expensive underutilized clusters?

 

Cloud Dataproc runs clusters ephemerally; in other words, only when needed.
Cloud Dataproc charges at a per-minute rate for each cluster, reducing costs.
Cloud Dataproc attaches storage or hard drives to each node of the cluster.
Cloud Dataproc runs clusters indefinitely, cutting down on wasted time typically spent on spinning up resources.

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What are some business challenges that Cloud Dataproc addresses?

 

High PUC cores and GPUs
Ease of use and speed
Idle clusters and scaling inflexibility
Integration and customization

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How does Cloud IoT help organizations unlock business insights in real time from data across globally dispersed devices?

 

It has a deep interoperability with business intelligence (BI) tools, allowing it to connect multiple devices around the globe through the tools themselves.
It connects globally distributed industrial devices into a single network that can be managed efficiently and serves as a new data source for an organization’s analytic systems to support improved operational efficiency.
It uses the Spark Machine Learning Libraries (MLlib) to run classification algorithms on very large datasets, relying on cloud-based machines where Spark can be installed and customized.
It is fully managed, with downtime-free upgrades and maintenance and seamless scaling, and it provides the benefits of operating on almost NoOps.

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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?

 

Storage
Analyze
Process
Ingest

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A query is how you retrieve information from a database, so which of these paths demonstrates the journey of a query?

 

Table with Data > Query > Database
Query > Table with Data > Database
Table with Data > Database > Query
Query > Database > Table with Data

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Which of these statements best expresses what you can do with Cloud Dataflow?

 

“I need access to near real-time reports, even if the data is speculative or sampled.”
“I wish I could run queries to organize my batch data.”
“I need to know what customers are doing right now, and I need to find out using my existing Hadoop tools.”
“I need to transfer my data from my on-premises solutions to the cloud.”

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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?

 

The separation of storage and compute.
The use of queries and containers.
The configuration of jobs and workflows.
The absence of management and maintenance.

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What dataset type is vital to machine learning?

 

Market datasets
QA datasets
Training datasets
Predictive datasets

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Machine learning is a branch of computer science that:

 

Is a tool for developing and executing a wide range of data processing patterns on very large datasets.
Is how you retrieve information from a database.
Is focused on enabling computers to recognize patterns in data—without humans telling the computer how to recognize the patterns.
Is a service to help capture data and rapidly pass massive numbers of messages between other big data tools.

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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?

 

Cloud Dataproc consolidates data marts into datasets and provides the ability to simply manage all datasets.
Cloud Dataproc supports Spark and can create clusters that scale for speed and mitigate any single point of failure.
Cloud Dataproc can act as a landing zone for log data at a low cost.
Cloud Dataproc enables you to convert audio to text by applying neural network models in an easy-to-use API.

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What is the value that Cloud Pub/Sub provides? Select the 2 correct answers.

 

Integration
Ease of use and implementation
Customization
Reduces OPEX

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Which of these statements about the Publisher-Subscriber pattern utilized by Cloud Pub/Sub is TRUE?

 

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.

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Which of the following is one of Google’s machine-learning-as-a-service offerings?

 

TensorFlow
Cloud Pub/Sub
Data Warehouse
Cloud Engine

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Which statement best describes where Cloud Dataproc falls on the big data processing model and the role it plays?

 

Cloud Dataproc allows organizations to scale data storage and ensures accessibility without compromising security.
Cloud Dataproc allows organizations to easily use MapReduce, Pig, Hive, and Spark to process data before storing it, and it helps organizations interactively analyze data with Spark and Hive.
Cloud Dataproc allows organizations to transform and enrich data in stream and batch modes.
Cloud Dataproc allows organizations to ingest event streams from anywhere, at any scale, for simple, reliable, real-time stream analytics.

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