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Google Professional-Data-Engineer Google Professional Data Engineer Exam Exam Practice Test

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Total 330 questions

Google Professional Data Engineer Exam Questions and Answers

Question 1

You are configuring networking for a Dataflow job. The data pipeline uses custom container images with the libraries that are required for the transformation logic preinstalled. The data pipeline reads the data from Cloud Storage and writes the data to BigQuery. You need to ensure cost-effective and secure communication between the pipeline and Google APIs and services. What should you do?

Options:

A.

Leave external IP addresses assigned to worker VMs while enforcing firewall rules.

B.

Disable external IP addresses and establish a Private Service Connect endpoint IP address.

C.

Disable external IP addresses from worker VMs and enable Private Google Access.

D.

Enable Cloud NAT to provide outbound internet connectivity while enforcing firewall rules.

Question 2

You are using BigQuery and Data Studio to design a customer-facing dashboard that displays large quantities of aggregated data. You expect a high volume of concurrent users. You need to optimize tie dashboard to provide quick visualizations with minimal latency. What should you do?

Options:

A.

Use BigQuery BI Engine with materialized views

B.

Use BigQuery BI Engine with streaming data.

C.

Use BigQuery Bl Engine with authorized views

D.

Use BigQuery Bl Engine with logical reviews

Question 3

You need (o give new website users a globally unique identifier (GUID) using a service that takes in data points and returns a GUID This data is sourced from both internal and external systems via HTTP calls that you will make via microservices within your pipeline There will be tens of thousands of messages per second and that can be multithreaded, and you worry about the backpressure on the system How should you design your pipeline to minimize that backpressure?

Options:

A.

Call out to the service via HTTP

B.

Create the pipeline statically in the class definition

C.

Create a new object in the startBundle method of DoFn

D.

Batch the job into ten-second increments

Question 4

You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

Options:

A.

Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.

B.

Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.

C.

Change the data pipeline to use BigQuery for storing stock trades, and update your application.

D.

Use Cloud Dataflow to write summary of each day’s stock trades to an Avro file on Cloud Storage. Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.

Question 5

You are designing a data mesh on Google Cloud with multiple distinct data engineering teams building data products. The typical data curation design pattern consists of landing files in Cloud Storage, transforming raw data in Cloud Storage and BigQuery datasets. and storing the final curated data product in BigQuery datasets You need to configure Dataplex to ensure that each team can access only the assets needed to build their data products. You also need to ensure that teams can easily share the curated data product. What should you do?

Options:

A.

1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data.

2 Provide each data engineering team access to the virtual lake.

B.

1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data. 2 Build separate assets for each data product within the zone.

3. Assign permissions to the data engineering teams at the zone level.

C.

1 Create a Dataplex virtual lake for each data product, and create a single zone to contain landing, raw, and curated data.

2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.

D.

1 Create a Dataplex virtual lake for each data product, and create multiple zones for landing, raw. and curated data.

2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.

Question 6

You are developing an Apache Beam pipeline to extract data from a Cloud SQL instance by using JdbclO. You have two projects running in Google Cloud. The pipeline will be deployed and executed on Dataflow in Project A. The Cloud SQL instance is running jn Project B and does not have a public IP address. After deploying the pipeline, you noticed that the pipeline failed to extract data from the Cloud SQL instance due to connection failure. You verified that VPC Service Controls and shared VPC are not in use in these projects. You want to resolve this error while ensuring that the data does not go through the public internet. What should you do?

Options:

A.

Set up VPC Network Peering between Project A and Project B. Add a firewall rule to allow the peered subnet range to access all instances on the network.

B.

Turn off the external IP addresses on the Dataflow worker. Enable Cloud NAT in Project A.

C.

Set up VPC Network Peering between Project A and Project B. Create a Compute Engine instance without external IP address in Project B on the peered subnet to serve as a proxy server to the Cloud SQL database.

D.

Add the external IP addresses of the Dataflow worker as authorized networks in the Cloud SOL instance.

Question 7

You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

Options:

A.

Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

B.

Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

C.

Set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.

D.

Set the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

Question 8

You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?

Options:

A.

Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

B.

Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.

C.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below 4000, send an alert.

D.

Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below 4000, send an alert.

Question 9

Your United States-based company has created an application for assessing and responding to user actions. The primary table’s data volume grows by 250,000 records per second. Many third parties use your application’s APIs to build the functionality into their own frontend applications. Your application’s APIs should comply with the following requirements:

  • Single global endpoint
  • ANSI SQL support
  • Consistent access to the most up-to-date data

What should you do?

Options:

A.

Implement BigQuery with no region selected for storage or processing.

B.

Implement Cloud Spanner with the leader in North America and read-only replicas in Asia and Europe.

C.

Implement Cloud SQL for PostgreSQL with the master in Norht America and read replicas in Asia and Europe.

D.

Implement Cloud Bigtable with the primary cluster in North America and secondary clusters in Asia and Europe.

Question 10

You launched a new gaming app almost three years ago. You have been uploading log files from the previous day to a separate Google BigQuery table with the table name format LOGS_yyyymmdd. You have been using table wildcard functions to generate daily and monthly reports for all time ranges. Recently, you discovered that some queries that cover long date ranges are exceeding the limit of 1,000 tables and failing. How can you resolve this issue?

Options:

A.

Convert all daily log tables into date-partitioned tables

B.

Convert the sharded tables into a single partitioned table

C.

Enable query caching so you can cache data from previous months

D.

Create separate views to cover each month, and query from these views

Question 11

Your company is selecting a system to centralize data ingestion and delivery. You are considering messaging and data integration systems to address the requirements. The key requirements are:

  • The ability to seek to a particular offset in a topic, possibly back to the start of all data ever captured
  • Support for publish/subscribe semantics on hundreds of topics
  • Retain per-key ordering

Which system should you choose?

Options:

A.

Apache Kafka

B.

Cloud Storage

C.

Cloud Pub/Sub

D.

Firebase Cloud Messaging

Question 12

You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

Options:

A.

Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.

B.

Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.

C.

Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.

D.

Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

Question 13

You are designing a real-time system for a ride hailing app that identifies areas with high demand for rides to effectively reroute available drivers to meet the demand. The system ingests data from multiple sources to Pub/Sub. processes the data, and stores the results for visualization and analysis in real-time dashboards. The data sources include driver location updates every 5 seconds and app-based booking events from riders. The data processing involves real-time aggregation of supply and demand data for the last 30 seconds, every 2 seconds, and storing the results in a low-latency system for visualization. What should you do?

Options:

A.

Group the data by using a tumbling window in a Dataflow pipeline, and write the aggregated data to Memorystore

B.

Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to Memorystore

C.

Group the data by using a session window in a Dataflow pipeline, and write the aggregated data to BigQuery.

D.

Group the data by using a hopping window in a Dataflow pipeline, and write the aggregated data to BigQuery.

Question 14

You are designing a data warehouse in BigQuery to analyze sales data for a telecommunication service provider. You need to create a data model for customers, products, and subscriptions All customers, products, and subscriptions can be updated monthly, but you must maintain a historical record of all data. You plan to use the visualization layer for current and historical reporting. You need to ensure that the data model is simple, easy-to-use. and cost-effective. What should you do?

Options:

A.

Create a normalized model with tables for each entity. Use snapshots before updates to track historical data

B.

Create a normalized model with tables for each entity. Keep all input files in a Cloud Storage bucket to track historical data

C.

Create a denormalized model with nested and repeated fields Update the table and use snapshots to track historical data

D.

Create a denormalized, append-only model with nested and repeated fields Use the ingestion timestamp to track historical data.

Question 15

You are using Google BigQuery as your data warehouse. Your users report that the following simple query is running very slowly, no matter when they run the query:

SELECT country, state, city FROM [myproject:mydataset.mytable] GROUP BY country

You check the query plan for the query and see the following output in the Read section of Stage:1:

Question # 15

What is the most likely cause of the delay for this query?

Options:

A.

Users are running too many concurrent queries in the system

B.

The [myproject:mydataset.mytable] table has too many partitions

C.

Either the state or the city columns in the [myproject:mydataset.mytable] table have too many

NULL values

D.

Most rows in the [myproject:mydataset.mytable] table have the same value in the country column, causing data skew

Question 16

You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

Options:

A.

Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.

B.

Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery

permanent linked tables for query.

C.

Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.

D.

Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into

Cloud Bigtable for query.

Question 17

You are creating a data model in BigQuery that will hold retail transaction data. Your two largest tables, sales_transation_header and sales_transation_line. have a tightly coupled immutable relationship. These tables are rarely modified after load and are frequently joined when queried. You need to model the sales_transation_header and sales_transation_line tables to improve the performance of data analytics queries. What should you do?

Options:

A.

Create a sal es_transaction table that Stores the sales_tran3action_header and sales_transaction_line data as a JSON data type.

B.

Create a sale3_transaction table that holds the sales_transaction_header information as rows and the

sales_transaction_line rows as nested and repeated fields.

C.

Create a sale_transaction table that holds the sales_transaction_header and sales_transaction_line information as rows, duplicating the sales_transaction_header data for each line.

D.

Create separate sales_transation_header and sales_transation_line tables and. when querying, specify the sales transition line first in the WHERE clause.

Question 18

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?

Options:

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.

Question 19

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?

Options:

A.

Create a table called tracking_table and include a DATE column.

B.

Create a partitioned table called tracking_table and include a TIMESTAMP column.

C.

Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.

D.

Create a table called tracking_table with a TIMESTAMP column to represent the day.

Question 20

MJTelco is building a custom interface to share data. They have these requirements:

  • They need to do aggregations over their petabyte-scale datasets.
  • They need to scan specific time range rows with a very fast response time (milliseconds).

Which combination of Google Cloud Platform products should you recommend?

Options:

A.

Cloud Datastore and Cloud Bigtable

B.

Cloud Bigtable and Cloud SQL

C.

BigQuery and Cloud Bigtable

D.

BigQuery and Cloud Storage

Question 21

You need to compose visualizations for operations teams with the following requirements:

Which approach meets the requirements?

Options:

A.

Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.

B.

Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.

C.

Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.

D.

Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.

Question 22

MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

Options:

A.

Rowkey: date#device_idColumn data: data_point

B.

Rowkey: dateColumn data: device_id, data_point

C.

Rowkey: device_idColumn data: date, data_point

D.

Rowkey: data_pointColumn data: device_id, date

E.

Rowkey: date#data_pointColumn data: device_id

Question 23

You create a new report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. It is company policy to ensure employees can view only the data associated with their region, so you create and populate a table for each region. You need to enforce the regional access policy to the data.

Which two actions should you take? (Choose two.)

Options:

A.

Ensure all the tables are included in global dataset.

B.

Ensure each table is included in a dataset for a region.

C.

Adjust the settings for each table to allow a related region-based security group view access.

D.

Adjust the settings for each view to allow a related region-based security group view access.

E.

Adjust the settings for each dataset to allow a related region-based security group view access.

Question 24

MJTelco’s Google Cloud Dataflow pipeline is now ready to start receiving data from the 50,000 installations. You want to allow Cloud Dataflow to scale its compute power up as required. Which Cloud Dataflow pipeline configuration setting should you update?

Options:

A.

The zone

B.

The number of workers

C.

The disk size per worker

D.

The maximum number of workers

Question 25

Your company is loading comma-separated values (CSV) files into Google BigQuery. The data is fully imported successfully; however, the imported data is not matching byte-to-byte to the source file. What is the most likely cause of this problem?

Options:

A.

The CSV data loaded in BigQuery is not flagged as CSV.

B.

The CSV data has invalid rows that were skipped on import.

C.

The CSV data loaded in BigQuery is not using BigQuery’s default encoding.

D.

The CSV data has not gone through an ETL phase before loading into BigQuery.

Question 26

Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by 10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.

You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)

Options:

A.

Introduce data compression for each file to increase the rate file of file transfer.

B.

Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

C.

Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.

D.

Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.

E.

Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.

Question 27

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= ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

Question # 27

Question # 27

Options:

A.

Option A

B.

Option B.

C.

Option C

D.

Option D

Question 28

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

Options:

A.

Load the data every 30 minutes into a new partitioned table in BigQuery.

B.

Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery

C.

Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore

D.

Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Question 29

You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.

You need to analyze the data by querying against individual fields. Which three databases meet your requirements? (Choose three.)

Options:

A.

Redis

B.

HBase

C.

MySQL

D.

MongoDB

E.

Cassandra

F.

HDFS with Hive

Question 30

You are designing the database schema for a machine learning-based food ordering service that will predict what users want to eat. Here is some of the information you need to store:

  • The user profile: What the user likes and doesn’t like to eat
  • The user account information: Name, address, preferred meal times
  • The order information: When orders are made, from where, to whom

The database will be used to store all the transactional data of the product. You want to optimize the data schema. Which Google Cloud Platform product should you use?

Options:

A.

BigQuery

B.

Cloud SQL

C.

Cloud Bigtable

D.

Cloud Datastore

Question 31

You work for a manufacturing plant that batches application log files together into a single log file once a day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make sure the log file in processed once per day as inexpensively as possible. What should you do?

Options:

A.

Change the processing job to use Google Cloud Dataproc instead.

B.

Manually start the Cloud Dataflow job each morning when you get into the office.

C.

Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.

D.

Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.

Question 32

You work for a large fast food restaurant chain with over 400,000 employees. You store employee information in Google BigQuery in a Users table consisting of a FirstName field and a LastName field. A member of IT is building an application and asks you to modify the schema and data in BigQuery so the application can query a FullName field consisting of the value of the FirstName field concatenated with a space, followed by the value of the LastName field for each employee. How can you make that data available while minimizing cost?

Options:

A.

Create a view in BigQuery that concatenates the FirstName and LastName field values to produce the FullName.

B.

Add a new column called FullName to the Users table. Run an UPDATE statement that updates the FullName column for each user with the concatenation of the FirstName and LastName values.

C.

Create a Google Cloud Dataflow job that queries BigQuery for the entire Users table, concatenates the FirstName value and LastName value for each user, and loads the proper values for FirstName, LastName, and FullName into a new table in BigQuery.

D.

Use BigQuery to export the data for the table to a CSV file. Create a Google Cloud Dataproc job to process the CSV file and output a new CSV file containing the proper values for FirstName, LastName and FullName. Run a BigQuery load job to load the new CSV file into BigQuery.

Question 33

Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs. What should you recommend they do?

Options:

A.

Rewrite the job in Pig.

B.

Rewrite the job in Apache Spark.

C.

Increase the size of the Hadoop cluster.

D.

Decrease the size of the Hadoop cluster but also rewrite the job in Hive.

Question 34

You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?

Options:

A.

Disable caching by editing the report settings.

B.

Disable caching in BigQuery by editing table details.

C.

Refresh your browser tab showing the visualizations.

D.

Clear your browser history for the past hour then reload the tab showing the virtualizations.

Question 35

Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

Options:

A.

Field promotion

B.

Randomization

C.

Salting

D.

Hashing

Question 36

Which of the following is NOT a valid use case to select HDD (hard disk drives) as the storage for Google Cloud Bigtable?

Options:

A.

You expect to store at least 10 TB of data.

B.

You will mostly run batch workloads with scans and writes, rather than frequently executing random reads of a small number of rows.

C.

You need to integrate with Google BigQuery.

D.

You will not use the data to back a user-facing or latency-sensitive application.

Question 37

Cloud Dataproc is a managed Apache Hadoop and Apache _____ service.

Options:

A.

Blaze

B.

Spark

C.

Fire

D.

Ignite

Question 38

When you store data in Cloud Bigtable, what is the recommended minimum amount of stored data?

Options:

A.

500 TB

B.

1 GB

C.

1 TB

D.

500 GB

Question 39

In order to securely transfer web traffic data from your computer's web browser to the Cloud Dataproc cluster you should use a(n) _____.

Options:

A.

VPN connection

B.

Special browser

C.

SSH tunnel

D.

FTP connection

Question 40

Which of these is NOT a way to customize the software on Dataproc cluster instances?

Options:

A.

Set initialization actions

B.

Modify configuration files using cluster properties

C.

Configure the cluster using Cloud Deployment Manager

D.

Log into the master node and make changes from there

Question 41

Which Java SDK class can you use to run your Dataflow programs locally?

Options:

A.

LocalRunner

B.

DirectPipelineRunner

C.

MachineRunner

D.

LocalPipelineRunner

Question 42

Cloud Bigtable is Google's ______ Big Data database service.

Options:

A.

Relational

B.

mySQL

C.

NoSQL

D.

SQL Server

Question 43

Which of the following IAM roles does your Compute Engine account require to be able to run pipeline jobs?

Options:

A.

dataflow.worker

B.

dataflow.compute

C.

dataflow.developer

D.

dataflow.viewer

Question 44

What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

Options:

A.

Include multiple time series values within the row key

B.

Keep the row keep as an 8 bit integer

C.

Keep your row key reasonably short

D.

Keep your row key as long as the field permits

Question 45

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)?

Options:

A.

A sequential numeric ID

B.

A timestamp followed by a stock symbol

C.

A non-sequential numeric ID

D.

A stock symbol followed by a timestamp

Question 46

The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster ____.

Options:

A.

application node

B.

conditional node

C.

master node

D.

worker node

Question 47

When creating a new Cloud Dataproc cluster with the projects.regions.clusters.create operation, these four values are required: project, region, name, and ____.

Options:

A.

zone

B.

node

C.

label

D.

type

Question 48

You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name from the data source and then write to an output PCollection.

Tom,555 X street

Tim,553 Y street

Sam, 111 Z street

Which operation is best suited for the above data processing requirement?

Options:

A.

ParDo

B.

Sink API

C.

Source API

D.

Data extraction

Question 49

If you're running a performance test that depends upon Cloud Bigtable, all the choices except one below are recommended steps. Which is NOT a recommended step to follow?

Options:

A.

Do not use a production instance.

B.

Run your test for at least 10 minutes.

C.

Before you test, run a heavy pre-test for several minutes.

D.

Use at least 300 GB of data.

Question 50

To run a TensorFlow training job on your own computer using Cloud Machine Learning Engine, what would your command start with?

Options:

A.

gcloud ml-engine local train

B.

gcloud ml-engine jobs submit training

C.

gcloud ml-engine jobs submit training local

D.

You can't run a TensorFlow program on your own computer using Cloud ML Engine .

Question 51

What are two of the benefits of using denormalized data structures in BigQuery?

Options:

A.

Reduces the amount of data processed, reduces the amount of storage required

B.

Increases query speed, makes queries simpler

C.

Reduces the amount of storage required, increases query speed

D.

Reduces the amount of data processed, increases query speed

Question 52

Flowlogistic wants to use Google BigQuery as their primary analysis system, but they still have Apache Hadoop and Spark workloads that they cannot move to BigQuery. Flowlogistic does not know how to store the data that is common to both workloads. What should they do?

Options:

A.

Store the common data in BigQuery as partitioned tables.

B.

Store the common data in BigQuery and expose authorized views.

C.

Store the common data encoded as Avro in Google Cloud Storage.

D.

Store he common data in the HDFS storage for a Google Cloud Dataproc cluster.

Question 53

Flowlogistic’s management has determined that the current Apache Kafka servers cannot handle the data volume for their real-time inventory tracking system. You need to build a new system on Google Cloud Platform (GCP) that will feed the proprietary tracking software. The system must be able to ingest data from a variety of global sources, process and query in real-time, and store the data reliably. Which combination of GCP products should you choose?

Options:

A.

Cloud Pub/Sub, Cloud Dataflow, and Cloud Storage

B.

Cloud Pub/Sub, Cloud Dataflow, and Local SSD

C.

Cloud Pub/Sub, Cloud SQL, and Cloud Storage

D.

Cloud Load Balancing, Cloud Dataflow, and Cloud Storage

Question 54

Flowlogistic’s CEO wants to gain rapid insight into their customer base so his sales team can be better informed in the field. This team is not very technical, so they’ve purchased a visualization tool to simplify the creation of BigQuery reports. However, they’ve been overwhelmed by all the data in the table, and are spending a lot of money on queries trying to find the data they need. You want to solve their problem in the most cost-effective way. What should you do?

Options:

A.

Export the data into a Google Sheet for virtualization.

B.

Create an additional table with only the necessary columns.

C.

Create a view on the table to present to the virtualization tool.

D.

Create identity and access management (IAM) roles on the appropriate columns, so only they appear in a query.

Question 55

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?

Options:

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.

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Total 330 questions