Latest [Feb 06, 2022] Real Google Professional-Data-Engineer Exam Dumps Questions
Professional-Data-Engineer Dumps To Pass Google Cloud Certified Exam in One Day (Updated 253 Questions)
The Professional Data Engineer exam is the industry-standard exam that proves the candidate’s ability to do data-driven decision-making by assembling, transforming, and publishing data. If you are rooting for a career in data engineering, you should take this test. It will lead you to attain the Professional Data Engineer certification issued by Google.
Exam Overview
The Professional Data Engineer certification exam is a 2-hour test consisting of the multiple-choice and multiple-select questions. The students can take it in the English or Japanese languages. To register for and schedule the exam, you must pay the fee of $200. It is possible to sit for this test in an online proctored format at a remote location. You can also take it as an on-site proctored exam at a designated testing center.
NEW QUESTION 135
What are two methods that can be used to denormalize tables in BigQuery?
- A. 1) Split table into multiple tables; 2) Use a partitioned table
- B. 1) Use nested repeated fields; 2) Use a partitioned table
- C. 1) Use a partitioned table; 2) Join tables into one table
- D. 1) Join tables into one table; 2) Use nested repeated fields
Answer: D
NEW QUESTION 136
Case Study: 2 - MJTelco
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.
You need to compose visualization for operations teams with the following requirements:
Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute) The report must not be more than 3 hours delayed from live data. The actionable report should only show suboptimal links.
Most suboptimal links should be sorted to the top.
Suboptimal links can be grouped and filtered by regional geography. User response time to load the report must be <5 seconds. You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types.
You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?
- A. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
- B. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
- C. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
- D. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
Answer: B
NEW QUESTION 137
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?
- A. Hive
- B. Spark
- C. YARN
- D. Pig
Answer: A,B,D
Explanation:
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
Reference:
https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run
NEW QUESTION 138
You are building a report-only data warehouse where the data is streamed into BigQuery via the streaming API Following Google's best practices, you have both a staging and a production table for the data How should you design your data loading to ensure that there is only one master dataset without affecting performance on either the ingestion or reporting pieces?
- A. Have a staging table that moves the staged data over to the production table and deletes the contents of the staging table every three hours
- B. Have a staging table that is an append-only model, and then update the production table every three hours with the changes written to staging
- C. Have a staging table that is an append-only model, and then update the production table every ninety minutes with the changes written to staging
- D. Have a staging table that moves the staged data over to the production table and deletes the contents of the staging table every thirty minutes
Answer: D
NEW QUESTION 139
You are working on a sensitive project involving private user data. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?
- A. Grant the consultant the Viewer role on the project.
- B. Create an anonymized sample of the data for the consultant to work with in a different project.
- C. Grant the consultant the Cloud Dataflow Developer role on the project.
- D. Create a service account and allow the consultant to log on with it.
Answer: C
Explanation:
A service account is a special type of Google account intended to represent a non-human user that needs to authenticate and be authorized to access data in Google APIs.
https://cloud.google.com/iam/docs/understanding-service-accounts
NEW QUESTION 140
Your software uses a simple JSON format for all messages. These messages are published to Google Cloud Pub/Sub, then processed with Google Cloud Dataflow to create a real-time dashboard for the CFO. During testing, you notice that some messages are missing in the dashboard. You check the logs, and all messages are being published to Cloud Pub/Sub successfully. What should you do next?
- A. Check the dashboard application to see if it is not displaying correctly.
- B. Use Google Stackdriver Monitoring on Cloud Pub/Sub to find the missing messages.
- C. Switch Cloud Dataflow to pull messages from Cloud Pub/Sub instead of Cloud Pub/Sub pushing messages to Cloud Dataflow.
- D. Run a fixed dataset through the Cloud Dataflow pipeline and analyze the output.
Answer: D
NEW QUESTION 141
You work for a shipping company that has distribution centers where packages move on delivery lines to route them properly. The company wants to add cameras to the delivery lines to detect and track any visual damage to the packages in transit. You need to create a way to automate the detection of damaged packages and flag them for human review in real time while the packages are in transit. Which solution should you choose?
- A. Use BigQuery machine learning to be able to train the model at scale, so you can analyze the packages in batches.
- B. Train an AutoML model on your corpus of images, and build an API around that model to integrate with the package tracking applications.
- C. Use the Cloud Vision API to detect for damage, and raise an alert through Cloud Functions. Integrate the package tracking applications with this function.
- D. Use TensorFlow to create a model that is trained on your corpus of images. Create a Python notebook in Cloud Datalab that uses this model so you can analyze for damaged packages.
Answer: A
NEW QUESTION 142
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?
- A. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
- B. Re-write the application to load accumulated data every 2 minutes.
- C. Convert the streaming insert code to batch load for individual messages.
- D. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
Answer: D
Explanation:
Explanation
The data is first comes to buffer and then written to Storage. If we are running queries in buffer we will face above mentioned issues. If we wait for the bigquery to write the data to storage then we won't face the issue.
So We need to wait till it's written tio storage
NEW QUESTION 143
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?
- A. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
- B. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/ allocated_for_project
- C. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
- D. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
Answer: B
Explanation:
https://cloud.google.com/bigquery/docs/monitoring
NEW QUESTION 144
You set up a streaming data insert into a Redis cluster via a Kafka cluster. Both clusters are running on Compute Engine instances. You need to encrypt data at rest with encryption keys that you can create, rotate, and destroy as needed. What should you do?
- A. Create encryption keys in Cloud Key Management Service. Reference those keys in your API service calls when accessing the data in your Compute Engine cluster instances.
- B. Create encryption keys locally. Upload your encryption keys to Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
- C. Create encryption keys in Cloud Key Management Service. Use those keys to encrypt your data in all of the Compute Engine cluster instances.
- D. Create a dedicated service account, and use encryption at rest to reference your data stored in your Compute Engine cluster instances as part of your API service calls.
Answer: B
NEW QUESTION 145
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.
You need to compose visualization for operations teams with the following requirements:
* Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)
* The report must not be more than 3 hours delayed from live data.
* The actionable report should only show suboptimal links.
* Most suboptimal links should be sorted to the top.
* Suboptimal links can be grouped and filtered by regional geography.
* User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?
- A. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
- B. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
- C. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
- D. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.
Answer: B
NEW QUESTION 146
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?
- A. A non-sequential numeric ID
- B. A timestamp followed by a stock symbol
- C. A stock symbol followed by a timestamp
- D. A sequential numeric ID
Answer: B,D
Explanation:
Explanation
using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes.
[https://cloud.google.com/bigtable/docs/schema-design]
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti
NEW QUESTION 147
Government regulations in your industry mandate that you have to maintain an auditable record of access
to certain types of data. Assuming that all expiring logs will be archived correctly, where should you store
data that is subject to that mandate?
- A. In a bucket on Cloud Storage that is accessible only by an AppEngine service that collects user
information and logs the access before providing a link to the bucket. - B. In Cloud SQL, with separate database user names to each user. The Cloud SQL Admin activity logs
will be used to provide the auditability. - C. Encrypted on Cloud Storage with user-supplied encryption keys. A separate decryption key will be
given to each authorized user. - D. In a BigQuery dataset that is viewable only by authorized personnel, with the Data Access log used to
provide the auditability.
Answer: D
NEW QUESTION 148
You are developing an application on Google Cloud that will automatically generate subject labels for users' blog posts. You are under competitive pressure to add this feature quickly, and you have no additional developer resources. No one on your team has experience with machine learning. What should you do?
- A. Call the Cloud Natural Language API from your application. Process the generated Entity Analysis as labels.
- B. Build and train a text classification model using TensorFlow. Deploy the model using Cloud Machine Learning Engine. Call the model from your application and process the results as labels.
- C. Call the Cloud Natural Language API from your application. Process the generated Sentiment Analysis as labels.
- D. Build and train a text classification model using TensorFlow. Deploy the model using a Kubernetes Engine cluster. Call the model from your application and process the results as labels.
Answer: A
Explanation:
As time is less, use cloud NLP and entity is used to label general subjects, sentiment label for sentiment analysis.
NEW QUESTION 149
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this data. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? (Choose two.)
- A. Denormalize the data as must as possible.
- B. Develop a data pipeline where status updates are appended to BigQuery instead of updated.
- C. Use BigQuery UPDATE to further reduce the size of the dataset.
- D. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file. Use BigQuery's support for external data sources to query.
- E. Preserve the structure of the data as much as possible.
Answer: A,B
Explanation:
Denormalization will help in performance by reducing query time, update are not good with bigquery.
NEW QUESTION 150
You are building an application to share financial market data with consumers, who will receive data feeds.
Data is collected from the markets in real time. Consumers will receive the data in the following ways:
* Real-time event stream
* ANSI SQL access to real-time stream and historical data
* Batch historical exports
Which solution should you use?
- A. Cloud Dataproc, Cloud Dataflow, BigQuery
- B. Cloud Dataflow, Cloud SQL, Cloud Spanner
- C. Cloud Pub/Sub, Cloud Storage, BigQuery
- D. Cloud Pub/Sub, Cloud Dataproc, Cloud SQL
Answer: C
NEW QUESTION 151
If a dataset contains rows with individual people and columns for year of birth, country, and income, how many of the columns are continuous and how many are categorical?
- A. 2 continuous and 1 categorical
- B. 3 continuous
- C. 1 continuous and 2 categorical
- D. 3 categorical
Answer: A
Explanation:
The columns can be grouped into two types--categorical and continuous columns:
A column is called categorical if its value can only be one of the categories in a finite set. For example, the native country of a person (U.S., India, Japan, etc.) or the education level (high school, college, etc.) are categorical columns.
A column is called continuous if its value can be any numerical value in a continuous range. For example, the capital gain of a person (e.g. $14,084) is a continuous column. Year of birth and income are continuous columns. Country is a categorical column. You could use bucketization to turn year of birth and/or income into categorical features, but the raw columns are continuous.
Reference: https://www.tensorflow.org/tutorials/wide#reading_the_census_data
NEW QUESTION 152
You are deploying a new storage system for your mobile application, which is a media streaming service.
You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie'the property 'actors'and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname>ordered by date_releasedor all movies with tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?
C: Set the following in your entity options: exclude_from_indexes = 'actors, tags' D: Set the following in your entity options: exclude_from_indexes = 'date_published'
- A. Option B.
- B. Option D
- C. Option C
- D. Option A
Answer: D
NEW QUESTION 153
You are building a model to make clothing recommendations. You know a user's fashion preference is likely to change over time, so you build a data pipeline to stream new data back to the model as it becomes available.
How should you use this data to train the model?
- A. Continuously retrain the model on just the new data.
- B. Train on the existing data while using the new data as your test set.
- C. Train on the new data while using the existing data as your test set.
- D. Continuously retrain the model on a combination of existing data and the new data.
Answer: D
NEW QUESTION 154
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.
Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day's events. They also want to use streaming ingestion. What should you do?
- A. Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.
- B. Create a table called tracking_table with a TIMESTAMP column to represent the day.
- C. Create a partitioned table called tracking_table and include a TIMESTAMP column.
- D. Create a table called tracking_table and include a DATE column.
Answer: C
NEW QUESTION 155
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time. Which two methods can accomplish this? (Choose two.)
- A. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
- B. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.
- C. Use managed export, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
- D. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
- E. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
Answer: D,E
NEW QUESTION 156
You work for a mid-sized enterprise that needs to move its operational system transaction data from an on- premises database to GCP. The database is about 20 TB in size. Which database should you choose?
- A. Cloud Datastore
- B. Cloud Bigtable
- C. Cloud Spanner
- D. Cloud SQL
Answer: D
Explanation:
Explanation/Reference:
NEW QUESTION 157
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