Google Cloud Certified Real Exam Questions and Answers FREE Professional-Data-Engineer Updated on Jul 31, 2021 [Q93-Q113]

Google Cloud Certified Professional-Data-Engineer Real Exam Questions and Answers FREE Updated on Jul 31, 2021

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NEW QUESTION 93
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
* Decoupling producer from consumer
* Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
* Near real-time SQL query
* Maintain at least 2 years of historical data, which will be queried with SQL Which pipeline should you use to meet these requirements?

• A. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
• B. Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
• C. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.
• D. Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.

Answer: D

 

NEW QUESTION 94
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

• A. Add ", UNNEST(city)" before the WHERE clause.
• B. Change "person" to "person.city".
• C. Change "person" to "city.person".
• D. Add ", UNNEST(person)" before the WHERE clause.

Answer: D

Explanation:
Explanation
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_resu

 

NEW QUESTION 95
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error.
SELECT person FROM `project1.example.table1` WHERE city = "London"
How would you correct the error?

• A. Add ", UNNEST(city)" before the WHERE clause.
• B. Change "person" to "person.city".
• C. Change "person" to "city.person".
• D. Add ", UNNEST(person)" before the WHERE clause.

Answer: D

Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql#nested_repeated_results

 

NEW QUESTION 96
Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use Hadoop jobs they have already created and minimize the management of the cluster as much as possible. They also want to be able to persist data beyond the life of the cluster. What should you do?

• A. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
• B. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
• C. Create a Google Cloud Dataflow job to process the data.
• D. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.
• E. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.

Answer: C

 

NEW QUESTION 97
You have a data pipeline that writes data to Cloud Bigtable using well-designed row keys. You want to monitor your pipeline to determine when to increase the size of you Cloud Bigtable cluster. Which two actions can you take to accomplish this? (Choose two.)

• A. Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Read pressure index is above 100.
• B. Monitor latency of read operations. Increase the size of the Cloud Bigtable cluster of read operations take longer than 100 ms.
• C. Monitor the latency of write operations. Increase the size of the Cloud Bigtable cluster when there is a sustained increase in write latency.
• D. Review Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Write pressure index is above 100.
• E. Monitor storage utilization. Increase the size of the Cloud Bigtable cluster when utilization increases above
  70% of max capacity.

Answer: A,C

 

NEW QUESTION 98
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DTstores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRINGtype. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

• A. Delete the table CLICK_STREAM, and then re-create it such that the column DTis of the TIMESTAMP type. Reload the data.
• B. Add two columns to the table CLICK STREAM: TSof the TIMESTAMPtype and IS_NEWof the BOOLEANtype. Reload all data in append mode. For each appended row, set the value of IS_NEWto true. For future queries, reference the column TSinstead of the column DT, with the WHEREclause ensuring that the value of IS_NEWmust be true.
• C. Create a view CLICK_STREAM_V, where strings from the column DTare cast into TIMESTAMPvalues.
  Reference the view CLICK_STREAM_Vinstead of the table CLICK_STREAMfrom now on.
• D. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DTinto TIMESTAMPvalues. Run the query into a destination table NEW_CLICK_STREAM, in which the column TSis the TIMESTAMPtype. Reference the table NEW_CLICK_STREAMinstead of the table CLICK_STREAMfrom now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.
• E. Add a column TSof the TIMESTAMPtype to the table CLICK_STREAM, and populate the numeric values from the column TSfor each row. Reference the column TSinstead of the column DTfrom now on.

Answer: B

 

NEW QUESTION 99
You are selecting services to write and transform JSON messages from Cloud Pub/Sub to BigQuery for a data pipeline on Google Cloud. You want to minimize service costs. You also want to monitor and accommodate input data volume that will vary in size with minimal manual intervention. What should you do?

• A. Use Cloud Dataflow to run your transformations. Monitor the total execution time for a sampling of jobs.
  Configure the job to use non-default Compute Engine machine types when needed.
• B. Use Cloud Dataproc to run your transformations. Use the diagnosecommand to generate an operational output archive. Locate the bottleneck and adjust cluster resources.
• C. Use Cloud Dataflow to run your transformations. Monitor the job system lag with Stackdriver. Use the default autoscaling setting for worker instances.
• D. Use Cloud Dataproc to run your transformations. Monitor CPU utilization for the cluster. Resize the number of worker nodes in your cluster via the command line.

Answer: B

Explanation:
Explanation

 

NEW QUESTION 100
Case Study 1 - Flowlogistic
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market.
Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
* Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
* Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
* Databases
8 physical servers in 2 clusters
- SQL Server - user data, inventory, static data
3 physical servers
- Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
* Application servers - customer front end, middleware for order/customs
60 virtual machines across 20 physical servers
- Tomcat - Java services
- Nginx - static content
- Batch servers
* Storage appliances
- iSCSI for virtual machine (VM) hosts
- Fibre Channel storage area network (FC SAN) - SQL server storage
- Network-attached storage (NAS) image storage, logs, backups
* 10 Apache Hadoop /Spark servers
- Core Data Lake
- Data analysis workloads
* 20 miscellaneous servers
- Jenkins, monitoring, bastion hosts,
Business Requirements
* Build a reliable and reproducible environment with scaled panty of production.
* Aggregate data in a centralized Data Lake for analysis
* Use historical data to perform predictive analytics on future shipments
* Accurately track every shipment worldwide using proprietary technology
* Improve business agility and speed of innovation through rapid provisioning of new resources
* Analyze and optimize architecture for performance in the cloud
* Migrate fully to the cloud if all other requirements are met
Technical Requirements
* Handle both streaming and batch data
* Migrate existing Hadoop workloads
* Ensure architecture is scalable and elastic to meet the changing demands of the company.
* Use managed services whenever possible
* Encrypt data flight and at rest
* Connect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.
Flowlogistic is rolling out their real-time inventory tracking system. The tracking devices will all send package-tracking messages, which will now go to a single Google Cloud Pub/Sub topic instead of the Apache Kafka cluster. A subscriber application will then process the messages for real-time reporting and store them in Google BigQuery for historical analysis. You want to ensure the package data can be analyzed over time.
Which approach should you take?

• A. Attach the timestamp on each message in the Cloud Pub/Sub subscriber application as they are received.
• B. Attach the timestamp and Package ID on the outbound message from each publisher device as they are sent to Clod Pub/Sub.
• C. Use the NOW () function in BigQuery to record the event's time.
• D. Use the automatically generated timestamp from Cloud Pub/Sub to order the data.

Answer: B

 

NEW QUESTION 101
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 stock symbol followed by a timestamp
• B. A timestamp followed by a stock symbol
• C. A sequential numeric ID
• D. A non-sequential numeric ID

Answer: B,C

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_hotspotting

 

NEW QUESTION 102
Business owners at your company have given you a database of bank transactions. Each row contains the user ID, transaction type, transaction location, and transaction amount. They ask you to investigate what type of machine learning can be applied to the dat

• A. Unsupervised learning to predict the location of a transaction.
• B. Supervised learning to determine which transactions are most likely to be fraudulent.
• C. Unsupervised learning to determine which transactions are most likely to be fraudulent.
• D. Clustering to divide the transactions into N categories based on feature similarity.
• E. Which three machine learning applications can you use? (Choose three.)
• F. Reinforcement learning to predict the location of a transaction.
• G. Supervised learning to predict the location of a transaction.

Answer: B,C,G

 

NEW QUESTION 103
You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

• A. Create a cron schedule in Cloud Dataprep.
• B. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.
• C. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.
• D. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

Answer: C

 

NEW QUESTION 104
You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average
200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
What should you do?

• A. Increase the size of your parquet files to ensure them to be 1 GB minimum.
• B. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.
• C. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.
• D. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.

Answer: C

 

NEW QUESTION 105
You are training a spam classifier. You notice that you are overfitting the training data. Which three actions can you take to resolve this problem? (Choose three.)

• A. Use a smaller set of features
• B. Decrease the regularization parameters
• C. Reduce the number of training examples
• D. Get more training examples
• E. Increase the regularization parameters
• F. Use a larger set of features

Answer: B,D,F

Explanation:
Explanation/Reference:

 

NEW QUESTION 106
The marketing team at your organization provides regular updates of a segment of your customer dataset.
The marketing team has given you a CSV with 1 million records that must be updated in BigQuery. When you use the UPDATE statement in BigQuery, you receive a quotaExceeded error. What should you do?

• A. Increase the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.
• B. Reduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.
• C. Import the new records from the CSV file into a new BigQuery table. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.
• D. Split the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.

Answer: C

Explanation:
https://cloud.google.com/blog/products/gcp/performing-large-scale-mutations-in-bigquery

 

NEW QUESTION 107
Your company's customer and order databases are often under heavy load. This makes performing analytics against them difficult without harming operations. The databases are in a MySQL cluster, with nightly backups taken using mysqldump. You want to perform analytics with minimal impact on operations. What should you do?

• A. Add a node to the MySQL cluster and build an OLAP cube there.
• B. Mount the backups to Google Cloud SQL, and then process the data using Google Cloud Dataproc.
• C. Use an ETL tool to load the data from MySQL into Google BigQuery.
• D. Connect an on-premises Apache Hadoop cluster to MySQL and perform ETL.

Answer: D

Explanation:
Topic 2, Flowlogistic Case Study
Company Overview
Flowlogistic is a leading logistics and supply chain provider. They help businesses throughout the world manage their resources and transport them to their final destination. The company has grown rapidly, expanding their offerings to include rail, truck, aircraft, and oceanic shipping.
Company Background
The company started as a regional trucking company, and then expanded into other logistics market. Because they have not updated their infrastructure, managing and tracking orders and shipments has become a bottleneck. To improve operations, Flowlogistic developed proprietary technology for tracking shipments in real time at the parcel level. However, they are unable to deploy it because their technology stack, based on Apache Kafka, cannot support the processing volume. In addition, Flowlogistic wants to further analyze their orders and shipments to determine how best to deploy their resources.
Solution Concept
Flowlogistic wants to implement two concepts using the cloud:
* Use their proprietary technology in a real-time inventory-tracking system that indicates the location of their loads
* Perform analytics on all their orders and shipment logs, which contain both structured and unstructured data, to determine how best to deploy resources, which markets to expand info. They also want to use predictive analytics to learn earlier when a shipment will be delayed.
Existing Technical Environment
Flowlogistic architecture resides in a single data center:
* Databases
* 8 physical servers in 2 clusters
* SQL Server - user data, inventory, static data
* 3 physical servers
* Cassandra - metadata, tracking messages
10 Kafka servers - tracking message aggregation and batch insert
* Application servers - customer front end, middleware for order/customs
* 60 virtual machines across 20 physical servers
* Tomcat - Java services
* Nginx - static content
* Batch servers
Storage appliances
* iSCSI for virtual machine (VM) hosts
* Fibre Channel storage area network (FC SAN) - SQL server storage
* Network-attached storage (NAS) image storage, logs, backups
* Apache Hadoop /Spark servers
* Core Data Lake
* Data analysis workloads
* 20 miscellaneous servers
* Jenkins, monitoring, bastion hosts,
Business Requirements
* Build a reliable and reproducible environment with scaled panty of production.
* Aggregate data in a centralized Data Lake for analysis
* Use historical data to perform predictive analytics on future shipments
* Accurately track every shipment worldwide using proprietary technology
* Improve business agility and speed of innovation through rapid provisioning of new resources
* Analyze and optimize architecture for performance in the cloud
* Migrate fully to the cloud if all other requirements are met
Technical Requirements
* Handle both streaming and batch data
* Migrate existing Hadoop workloads
* Ensure architecture is scalable and elastic to meet the changing demands of the company.
* Use managed services whenever possible
* Encrypt data flight and at rest
* Connect a VPN between the production data center and cloud environment SEO Statement We have grown so quickly that our inability to upgrade our infrastructure is really hampering further growth and efficiency. We are efficient at moving shipments around the world, but we are inefficient at moving data around.
We need to organize our information so we can more easily understand where our customers are and what they are shipping.
CTO Statement
IT has never been a priority for us, so as our data has grown, we have not invested enough in our technology. I have a good staff to manage IT, but they are so busy managing our infrastructure that I cannot get them to do the things that really matter, such as organizing our data, building the analytics, and figuring out how to implement the CFO' s tracking technology.
CFO Statement
Part of our competitive advantage is that we penalize ourselves for late shipments and deliveries. Knowing where out shipments are at all times has a direct correlation to our bottom line and profitability. Additionally, I don't want to commit capital to building out a server environment.

 

NEW QUESTION 108
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
* Decoupling producer from consumer
* Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
* Near real-time SQL query
* Maintain at least 2 years of historical data, which will be queried with SQL Which pipeline should you use to meet these requirements?

• A. Create an application that provides an API. Write a tool to poll the API and write data to Cloud Storage as gzipped JSON files.
• B. Create an application that publishes events to Cloud Pub/Sub, and create a Cloud Dataflow pipeline that transforms the JSON event payloads to Avro, writing the data to Cloud Storage and BigQuery.
• C. Create an application that writes to a Cloud SQL database to store the data. Set up periodic exports of the database to write to Cloud Storage and load into BigQuery.
• D. Create an application that publishes events to Cloud Pub/Sub, and create Spark jobs on Cloud Dataproc to convert the JSON data to Avro format, stored on HDFS on Persistent Disk.

Answer: B

 

NEW QUESTION 109
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.
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 and include a DATE column.
• C. Create a table called tracking_table with a TIMESTAMP column to represent the day.
• D. Create a partitioned table called tracking_table and include a TIMESTAMP column.

Answer: D

 

NEW QUESTION 110
You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?

• A. Update the Cloud Dataflow pipeline inflight by passing the --update option with the --jobName set to the existing job name
• B. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code
• C. Update the Cloud Dataflow pipeline inflight by passing the --updateoption with the --jobNameset to a new unique job name
• D. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code

Answer: A

Explanation:
Explanation/Reference: https://cloud.google.com/dataflow/docs/guides/updating-a-pipeline

 

NEW QUESTION 111
Your company's on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage.
You want to minimize the storage cost of the migration. What should you do?

• A. Tune the Cloud Dataproc cluster so that there is just enough disk for all data.
• B. Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.
• C. Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.
• D. Put the data into Google Cloud Storage.

Answer: B

 

NEW QUESTION 112
Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.

• A. Fire
• B. Blaze
• C. Spark
• D. Ignite

Answer: C

Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/

 

NEW QUESTION 113
......

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