You need to connect multiple applications with dynamic public IP addresses to a Cloud SQL instance. You configured users with strong passwords and enforced the SSL connection to your Cloud SQL instance. You want to use Cloud SQL public IP and ensure that you have secured connections. What should you do?

Leave the Authorized Network empty. Use Cloud SQL Auth proxy on all applications.

Add CIDR 0.0.0.0/0 network to Authorized Network. Use Identity and Access Management (IAM) to add users.

Add all application networks to Authorized Network and regularly update them.

Add CIDR 0.0.0.0/0 network to Authorized Network. Use Cloud SQL Auth proxy on all applications.

Use Dataflow, write a pipeline that reads the data from Kafka, and writes the data to BigQuery.

Setup a Kafka Connect bridge between Kafka and Pub/Sub. Use a Google-provided Dataflow template to read the data from Pub/Sub, and write the data to BigQuery.

Use a proxy host in the VPC in Google Cloud connecting to Kafka. Write a Dataflow pipeline, read data from the proxy host, and write the data to BigQuery.

Setup a Kafka Connect bridge between Kafka and Pub/Sub. Write a Dataflow pipeline, read the data from Pub/Sub, and write the data to BigQuery.

You are designing the architecture to process your data from Cloud Storage to BigQuery by using Dataflow. The network team provided you with the Shared VPC network and subnetwork to be used by your pipelines. You need to enable the deployment of the pipeline on the Shared VPC network. What should you do?

Assign the compute.networkUser role to the Dataflow service agent.

Assign the compute.networkUser role to the service account that executes the Dataflow pipeline.

Assign the dataflow.admin role to the Dataflow service agent.

Assign the dataflow.admin role to the service account that executes the Dataflow pipeline.

Set the topic retention policy to 30 days.

Create a new topic, and publish the last 30 days of data each time a new subscriber connects to an existing topic.

Set the subscriber retention policy to 30 days.

Ask the source system to re-push the data to Pub/Sub, and subscribe to it.

You designed a data warehouse in BigQuery to analyze sales data. You want a self-serving, lowmaintenance, and cost- effective solution to share the sales dataset to other business units in your organization. What should you do?

Create an Analytics Hub private exchange, and publish the sales dataset.

Enable the other business units’ projects to access the authorized views of the sales dataset.

Create and share views with the users in the other business units.

Use the BigQuery Data Transfer Service to create a schedule that copies the sales dataset to the other business units’ projects.

How should you configure the Pub/Sub subscription?

Use exponential backoff as the subscription retry policy, and configure dead lettering to a different topic with maximum delivery attempts set to 10.

Increase the acknowledgement deadline to 10 minutes.

Use immediate redelivery as the subscription retry policy, and configure dead lettering to a different topic with maximum delivery attempts set to 10.

Use exponential backoff as the subscription retry policy, and configure dead lettering to the same source topic with maximum delivery attempts set to 10.

Use Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job.

Use Dataflow SQL to create a job that normalizes the data, and that after the first run of the job, schedule the pipeline to execute recurrently.

Create a Spark job and submit it to Dataproc Serverless.

Use BigQuery and GoogleSQL to normalize the data, and schedule recurring queries in BigQuery.

How should you build the solution?

Use Dataplex to manage data, track data lineage, and perform data quality validation.

Use BigLake to convert the current solution into a data lake architecture.

Build a new data discovery tool on Google Kubernetes Engine that helps with new source onboarding and data lineage tracking.

Use BigQuery to track data lineage, and use Dataprep to manage data and perform data quality validation.

Create a BigQuery reservation with a baseline of 500 slots with no autoscaling for the marketing team, and bill them back accordingly

Create a BigQuery Enterprise reservation with a baseline of 250 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly.

Establish a BigQuery quota for the marketing team, and limit the maximum number of bytes scanned each day.

Create a BigQuery Standard pay-as-you go reservation with a baseline of 0 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly.

Move your data to Cloud Storage. Run your jobs on Dataproc.

Move your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach.

Rewrite your jobs in Apache Beam. Run your jobs in Dataflow.

Copy your data to Compute Engine disks. Manage and run your jobs directly on those instances.

How should you store this data?

Store your data in Bigtable. Concatenate the sensor ID and timestamp and use it as the row key. Perform an export to BigQuery every day.

Store your data in BigQuery. Concatenate the sensor ID and timestamp, and use it as the primary key.

Store your data in Bigtable. Concatenate the sensor ID and metric, and use it as the row key. Perform an export to BigQuery every day.

Store your data in BigQuery. Use the metric as a primary key.

You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket, which contains a large number of files. You notice that queries against this table are slow. You want to improve the performance of these queries. What should you do?

Upgrade the external table to a BigLake table. Enable metadata caching for the table.

Change the storage class of the Hive partitioned data objects from Coldline to Standard.

Create an individual external table for each Hive partition by using a common table name prefix. Use wildcard table queries to reference the partitioned data.

Migrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.

Use a timestamp range filter in the query to fetch the customer's data for a specific range.

Set the expiring values of the column families to 29 days and keep the number of versions to 1.

Schedule a job daily to scan the data in the table and delete data older than 30 days.

Set the expiring values of the column families to 30 days and set the number of versions to 2.

Use watermarks to define the expected data arrival window. Allow late data as it arrives.

Change your windowing function to tumbling windows to avoid overlapping window periods.

Change your windowing function to session windows to define your windows based on certain activity.

Expand your hopping window so that the late data has more time to arrive within the grouping

Give analysts the BigQuery Data Viewer role on the shared dataset. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the dataset level for their assigned dataset.

Give analysts the BigQuery Data Viewer role at the project level. Create one other dataset, and give the analysts the BigQuery Data Editor role on that dataset.

Give analysts the BigQuery Data Viewer role at the project level. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the project level.

Give analysts the BigQuery Data Viewer role on the shared dataset. Create one other dataset and give the analysts the BigQuery Data Editor role on that dataset.

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

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

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

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

You are designing a Dataflow pipeline for a batch processing job. You want to mitigate multiple zonal failures at job submission time. What should you do?

Specify a worker region by using the --region flag.

Submit duplicate pipelines in two different zones by using the --zone flag.

Set the pipeline staging location as a regional Cloud Storage bucket.

Create an Eventarc trigger to resubmit the job in case of zonal failure when submitting the job.

Copy the ORC files on Cloud Storage, then create external BigQuery tables for the data scientist team.

Import the ORC files to Bigtable tables for the data scientist team.

Import the ORC files to BigQuery tables for the data scientist team.

Copy the ORC files on Cloud Storage, then deploy a Dataproc cluster for the data scientist team.

Create a new BigQuery table and Pub/Sub topic by using customer-managed encryption keys (CMEK), and migrate the data from the old BigQuery table.

Use Cloud KMS encryption key with Dataflow to ingest the existing Pub/Sub subscription to the existing BigQuery table.

Create a new BigQuery table by using customer-managed encryption keys (CMEK), and migrate the data from the old BigQuery table.

Create a new Pub/Sub topic with CMEK and use the existing BigQuery table by using Googlemanaged encryption key.

What should you do next?

Create the “gdpr” tag template with public visibility. Assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.

Create the “gdpr” tag template with private visibility. Assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.

Create the “gdpr” tag template with private visibility. Assign the datacatalog.tagTemplateViewer role on this tag to the all employees group, and assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.

Create the “gdpr” tag template with public visibility. Assign the datacatalog.tagTemplateViewer role on this tag to the all employees group, and assign the bigquery.dataViewer role to the HR group on the tables that contain sensitive data.

Use the BigQueryInsertJobOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to true.

Use the BigQueryUpsertTableOperator in Cloud Composer, set the retry parameter to three, and set the email_on_failure parameter to true.

Create a BigQuery scheduled query to run the SQL transformation with schedule options that repeats every two hours, and enable email notifications.

Create a BigQuery scheduled query to run the SQL transformation with schedule options that repeats every two hours, and enable notification to Pub/Sub topic. Use Pub/Sub and Cloud Functions to send an email after three failed executions.

Use Data Catalog to automatically catalog BigQuery datasets and Pub/Sub topics. Use Data Catalog APIs to manually catalog PostgreSQL tables.

Use Data Catalog to automatically catalog BigQuery datasets. Use Data Catalog APIs to manually catalog Pub/Sub topics and PostgreSQL tables.

Use Data Catalog to automatically catalog BigQuery datasets and Pub/Sub topics. Use custom connectors to manually catalog PostgreSQL tables.

Use customer connectors to manually catalog BigQuery datasets, Pub/Sub topics, and PostgreSQL tables.

Drain the old pipeline, then start the new pipeline.

Update the old pipeline with the new pipeline code.

Snapshot the old pipeline, stop the old pipeline, and then start the new pipeline from the snapshot.

Cancel the old pipeline, then start the new pipeline.

Create a sales_transaction table that holds the sales_transaction_header information as rows and the sales_transaction_line rows as nested and repeated fields.

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

Create a sales_transaction table that stores the sales_transaction_header and sales_transaction_line data as a JSON data type.

Create separate sales_transaction_header and sales_transaction_line tables and, when querying, specify the sales_transaction_line first in the WHERE clause.

Use session windows with a 15-minute gap duration.

Use session windows with a 30-minute gap duration.

Use hopping windows with a 15-minute window, and a thirty-minute period.

Use tumbling windows with a 15-minute window and a fifteen-minute .withAllowedLateness operator.

Access historical data by using time travel in BigQuery.

Export the data from BigQuery into a new table that excludes the corrupted data.

Create a BigQuery table snapshot on a daily basis.

Migrate your data to multi-region BigQuery buckets.

Create a BigQuery reservation for the project.

Create a BigQuery reservation for the dataset.

Create a BigQuery reservation for the job.

Create a BigQuery reservation for the service account running the job.

Insert a Reshuffle operation after each processing step, and monitor the execution details in the Dataflow console.

Insert output sinks after each key processing step, and observe the writing throughput of each block.

Log debug information in each ParDo function, and analyze the logs at execution time.

Verify that the Dataflow service accounts have appropriate permissions to write the processed data to the output sinks.

Create the encryption key in the on-premises HSM and link it to a Cloud External Key Manager (Cloud EKM) key. Associate the created Cloud KMS key while creating the BigQuery resources.

. Create the encryption key in the on-premises HSM, and import it into a Cloud Key Management Service (Cloud KMS) key. Associate the created Cloud KMS key while creating the BigQuery resources.

Create the encryption key in the on-premises HSM, and import it into Cloud Key Management Service (Cloud HSM) key. Associate the created Cloud HSM key while creating the BigQuery resources.

Create the encryption key in the on-premises HSM. Create BigQuery resources and encrypt data while ingesting them into BigQuery.

You want to migrate your existing Teradata data warehouse to BigQuery. You want to move the historical data to BigQuery by using the most efficient method that requires the least amount of programming, but local storage space on your existing data warehouse is limited. What should you do?

Use BigQuery Data Transfer Service by using the Java Database Connectivity (JDBC) driver with FastExport connection.

Create a Teradata Parallel Transporter (TPT) export script to export the historical data, and import to BigQuery by using the bq command-line tool.

Use BigQuery Data Transfer Service with the Teradata Parallel Transporter (TPT) tbuild utility.

Create a script to export the historical data, and upload in batches to Cloud Storage. Set up a BigQuery Data Transfer Service instance from Cloud Storage to BigQuery.

You are managing a Dataplex environment with raw and curated zones. A data engineering team is uploading JSON and CSV files to a bucket asset in the curated zone but the files are not being automatically discovered by Dataplex. What should you do to ensure that the files are discovered by Dataplex?

Move the JSON and CSV files to the raw zone.

Enable auto-discovery of files for the curated zone.

Use the bg command-line tool to load the JSON and CSV files into BigQuery tables.

Grant object level access to the CSV and JSON files in Cloud Storage.

Create streams in Datastream to replicate the required tables from both Cloud SQL databases to BigQuery for these queries.

Create BigQuery connections to both Cloud SQL databases. Use BigQuery federated queries on the two databases and the Google Analytics data on BigQuery to run these queries.

Create a job on Apache Spark with Dataproc Serverless to query both Cloud SQL databases and the Google Analytics data on BigQuery for these queries

Create a Dataproc cluster with Trino to establish connections to both Cloud SQL databases and BigQuery, to execute the queries

Data 50 preg (311-261) v1

12th Grade

•

50 Qs

Similar activities

Sec-D Javascript-1

12th Grade - University

•

52 Qs

STRUCTURED QUERY LANGUAGE

12th Grade

•

46 Qs

12A- KIỂM TRA GIỮA HỌC KỲ 1

12th Grade

•

50 Qs

SOALAN TMK

KG - University

•

46 Qs

ULANGAN HARIAN TEKNIK PENGOLAHAN AUDIO DAN VIDEO

1st - 12th Grade

•

50 Qs

Intro to Computing Midterms

12th Grade

•

50 Qs

UH ORIENTASI DASAR TEKNIK JARINGAN KOMPUTER DAN TELEKOMUNIKASI

10th Grade - University

•

50 Qs

PAS - PKK - Kelas XII TKJ

12th Grade

•

45 Qs

Data 50 preg (311-261) v1

Quiz

•

Computers

•

12th Grade

•

Practice Problem

•

Hard

Academia Google

Used 7+ times

FREE Resource

50 questions

Show all answers

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

You need to look at BigQuery data from a specific table multiple times a day. The underlying table you are querying is several petabytes in size, but you want to filter your data and provide simple aggregations to downstream users. You want to run queries faster and get up-to-date insights quicker. What should you do?

Run a scheduled query to pull the necessary data at specific intervals dally.

Use a cached query to accelerate time to results.

Limit the query columns being pulled in the final result.

Create a materialized view based off of the query being run.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

Your chemical company needs to manually check documentation for customer order. You use a pull subscription in Pub/Sub so that sales agents get details from the order. You must ensure that you do not process orders twice with different sales agents and that you do not add more complexity to this workflow. What should you do?

Use a Deduplicate PTransform in Dataflow before sending the messages to the sales agents.

Create a transactional database that monitors the pending messages.

Use Pub/Sub exactly-once delivery in your pull subscription.

Create a new Pub/Sub push subscription to monitor the orders processed in the agent's system.

MULTIPLE CHOICE QUESTION

2 mins • 1 pt

You work for an airline and you need to store weather data in a BigQuery table. Weather data will be used as input to a machine learning model. The model only uses the last 30 days of weather data. You want to avoid storing unnecessary data and minimize costs. What should you do?

Create a BigQuery table where each record has an ingestion timestamp. Run a scheduled query to delete all the rows with an ingestion timestamp older than 30 days.

Create a BigQuery table partitioned by datetime value of the weather date. Set up partition expiration to 30 days.

Create a BigQuery table partitioned by ingestion time. Set up partition expiration to 30 days.

Create a BigQuery table with a datetime column for the day the weather data refers to. Run a scheduled query to delete rows with a datetime value older than 30 days.