C. Use available administrative resource charts to determine how slots are being used and how jobs are performing over time. Run a query on the INFORMATION_SCHEMA to review query performance.

A. Use slot reservations for your project to ensure that you have enough query processing capacity and are able to allocate available slots to the slower queries.

B. Use Cloud Monitoring to view BigQuery metrics and set up alerts that let you know when a certain percentage of slots were used.

D. Use Cloud Logging to determine if any users or downstream consumers are changing or deleting access grants on tagged resources.

You migrated a data backend for an application that serves 10 PB of historical product data for analytics. Only the last known state for a product, which is about 10 GB of data, needs to be served through an API to the other applications. You need to choose a cost-effective persistent storage solution that can accommodate the analytics requirements and the API performance of up to 1000 queries per second (QPS) with less than 1 second latency. What should you do?

1. Store the historical data in BigQuery for analytics. 2. In a Cloud SQL table, store the last state of the product after every product change. 3. Serve the last state data directly from Cloud SQL to the API.

1. Store the historical data in BigQuery for analytics. 2. Use a materialized view to precompute the last state of a product. 3. Serve the last state data directly from BigQuery to the API.

1. Store the product as a collection in Firestore with each product having a set of historical changes. 2. Use simple and compound queries for analytics. 3. Serve the last state data directly from Firestore to the API.

1. Store the historical data in Cloud SQL for analytics. 2. In a separate table, store the last state of the product after every product change. 3. Serve the last state data directly from Cloud SQL to the API.

You want to schedule a number of sequential load and transformation jobs. Data files will be added to a Cloud Storage bucket by an upstream process. There is no fixed schedule for when the new data arrives. Next, a Dataproc job is triggered to perform some transformations and write the data to BigQuery. You then need to run additional transformation jobs in BigQuery. The transformation jobs are different for every table. These jobs might take hours to complete. You need to determine the most efficient and maintainable workflow to process hundreds of tables and provide the freshest data to your end users. What should you do?

D. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators. 2. Create a separate DAG for each table that needs to go through the pipeline. 3. Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG. **Most Voted**

A. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc, and BigQuery operators. 2. Use a single shared DAG for all tables that need to go through the pipeline. 3. Schedule the DAG to run hourly.

B. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Cloud Storage, Dataproc, and BigQuery operators. 2. Create a separate DAG for each table that needs to go through the pipeline. 3. Schedule the DAGs to run hourly.

C. 1. Create an Apache Airflow directed acyclic graph (DAG) in Cloud Composer with sequential tasks by using the Dataproc and BigQuery operators. 2. Use a single shared DAG for all tables that need to go through the pipeline. 3. Use a Cloud Storage object trigger to launch a Cloud Function that triggers the DAG.

You are deploying a MySQL database workload onto Cloud SQL. The database must be able to scale up to support several readers from various geographic regions. The database must be highly available and meet low RTO and RPO requirements, even in the event of a regional outage. You need to ensure that interruptions to the readers are minimal during a database failover. What should you do?

C. Create a highly available Cloud SQL instance in region A. Scale up read workloads by creating read replicas in multiple regions. Promote one of the read replicas when region A is down.

A. Create a highly available Cloud SQL instance in region Create a highly available read replica in region

B. Scale up read workloads by creating cascading read replicas in multiple regions. Backup the Cloud SQL instances to a multi-regional Cloud Storage bucket. Restore the Cloud SQL backup to a new instance in another region when Region A is down.

D. Create a highly available Cloud SQL instance in region A. Create a highly available read replica in region B. Scale up read workloads by creating cascading read replicas in multiple regions. Promote the read replica in region B when region A is down.

E. Create a highly available Cloud SQL instance in region A. Scale up read workloads by creating read replicas in the same region. Failover to the standby Cloud SQL instance when the primary instance fails.

You are planning to load some of your existing on-premises data into BigQuery on Google Cloud. You want to either stream or batch-load data, depending on your use case. Additionally, you want to mask some sensitive data before loading into BigQuery. You need to do this in a programmatic way while keeping costs to a minimum. What should you do?

C. Create your pipeline with Dataflow through the Apache Beam SDK for Python, customizing separate options within your code for streaming, batch processing, and Cloud DLP. Select BigQuery as your data sink.

A. Use Cloud Data Fusion to design your pipeline, use the Cloud DLP plug-in to de-identify data within your pipeline, and then move the data into BigQuery.

B. Use the BigQuery Data Transfer Service to schedule your migration. After the data is populated in BigQuery, use the connection to the Cloud Data Loss Prevention (Cloud DLP) API to de-identify the necessary data.

D. Set up Datastream to replicate your on-premise data on BigQuery.

You want to encrypt the customer data stored in BigQuery. You need to implement per-user crypto-deletion on data stored in your tables. You want to adopt native features in Google Cloud to avoid custom solutions. What should you do?

Implement Authenticated Encryption with Associated Data (AEAD) BigQuery functions while storing your data in BigQuery.

Create a customer-managed encryption key (CMEK) in Cloud KMS. Associate the key to the table while creating the table.

Create a customer-managed encryption key (CMEK) in Cloud KMS. Use the key to encrypt data before storing in BigQuery.

Encrypt your data during ingestion by using a cryptographic library supported by your ETL pipeline.

The data analyst team at your company uses BigQuery for ad-hoc queries and scheduled SQL pipelines in a Google Cloud project with a slot reservation of 2000 slots. However, with the recent introduction of hundreds of new non-time-sensitive SQL pipelines, the team is encountering frequent quota errors. You examine the logs and notice that approximately 1500 queries are being triggered concurrently during peak time. You need to resolve the concurrency issue. What should you do?

Update SQL pipelines to run as a batch query, and run ad-hoc queries as interactive query jobs.

Increase the slot capacity of the project with baseline as 0 and maximum reservation size as 3000.

Increase the slot capacity of the project with baseline as 2000 and maximum reservation size as 3000.

Update SQL pipelines and ad-hoc queries to run as interactive query jobs.

You are designing a data mesh on Google Cloud by using Dataplex to manage data in BigQuery and Cloud Storage. You want to simplify data asset permissions. You are creating a customer virtual lake with two user groups: Data engineers, which require full data lake access; Analytic users, which require access to curated data. You need to assign access rights to these two groups. What should you do?

1. Grant the dataplex.dataOwner role to the data engineer group on the customer data lake. 2. Grant the dataplex.dataReader role to the analytic user group on the customer curated zone.

1. Grant the dataplex.dataReader role to the data engineer group on the customer data lake. 2. Grant the dataplex.dataOwner role to the analytic user group on the customer curated zone.

1. Grant the bigquery.dataOwner role on BigQuery datasets and the storage.objectCreator role on Cloud Storage buckets to data engineers. 2. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectViewer role on Cloud Storage buckets to analytic users.

1. Grant the bigquery.dataViewer role on BigQuery datasets and the storage.objectViewer role on Cloud Storage buckets to data engineers. 2. Grant the bigquery.dataOwner role on BigQuery datasets and the storage.objectEditor role on Cloud Storage buckets to analytic users.

You are designing the architecture of your application to store data in Cloud Storage. Your application consists of pipelines that read data from a Cloud Storage bucket that contains raw data, and write the data to a second bucket after processing. You want to design an architecture with Cloud Storage resources that are capable of being resilient if a Google Cloud regional failure occurs. You want to minimize the recovery point objective (RPO) if a failure occurs, with no impact on applications that use the stored data. What should you do?

Adopt a dual-region Cloud Storage bucket, and enable turbo replication in your architecture.

Adopt multi-regional Cloud Storage buckets in your architecture.

Adopt two regional Cloud Storage buckets, and update your application to write the output on both buckets.

Adopt two regional Cloud Storage buckets, and create a daily task to copy from one bucket to the other.

You have designed an Apache Beam processing pipeline that reads from a Pub/Sub topic. The topic has a message retention duration of one day, and writes to a Cloud Storage bucket. You need to select a bucket location and processing strategy to prevent data loss in case of a regional outage with an RPO of 15 minutes. What should you do?

1. Use a dual-region Cloud Storage bucket with turbo replication enabled. 2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs. 3. Seek the subscription back in time by 60 minutes to recover the acknowledged messages. 4. Start the Dataflow job in a secondary region.

1. Use a dual-region Cloud Storage bucket. 2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs. 3. Seek the subscription back in time by 15 minutes to recover the acknowledged messages. 4. Start the Dataflow job in a secondary region.

1. Use a multi-regional Cloud Storage bucket. 2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs. 3. Seek the subscription back in time by 60 minutes to recover the acknowledged messages. 4. Start the Dataflow job in a secondary region.

1. Use a regional Cloud Storage bucket. 2. Monitor Dataflow metrics with Cloud Monitoring to determine when an outage occurs. 3. Seek the subscription back in time by one day to recover the acknowledged messages. 4. Start the Dataflow job in a secondary region and write in a bucket in the same region.

You are preparing data that your machine learning team will use to train a model using BigQueryML. They want to predict the price per square foot of real estate. The training data has a column for the price and a column for the number of square feet. Another column called 'feature1' contains null values due to missing data. You want to replace the nulls with zeros to keep more data points. Which query should you use?

SELECT * EXCEPT(feature1), IFNULL(feature1, 0) AS feature1_cleaned FROM training_data;

SELECT * EXCEPT(price, square_feet), price/square_feet AS price_per_sqft FROM training_data WHERE feature1 IS NOT NULL;

SELECT * EXCEPT(price, square_feet, feature1), price/square_feet AS price_per_sqft, IFNULL(feature1, 0) AS feature1_cleaned FROM training_data;

SELECT * FROM training_data WHERE feature1 IS NOT NULL;

Different teams in your organization store customer and performance data in BigQuery. Each team needs to keep full control of their collected data, be able to query data with their projects, and be able to exchange their data with other teams. You need to implement an organization-wide solution, while minimizing operational tasks and costs. What should you do?

Ask each team to publish their data in Analytics Hub. Direct the other teams to subscribe to them.

Ask each team to create authorized views of their data. Grant the bigquery.jobUser role to each team.

Create a BigQuery scheduled query to replicate all customer data into team projects.

Enable each team to create materialized views of the data they need to access in their projects.

You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your data. You want to continue through the model development lifecycle. What should you do next?

Delineate what data will be used for testing and what will be used for training the model.

Use your model to run predictions on fresh customer input data.

Monitor your model performance, and make any adjustments needed.

Test and evaluate your model on your curated data to determine how well the model performs.

You have one BigQuery dataset which includes customers’ street addresses. You want to retrieve all occurrences of street addresses from the dataset. What should you do?

Create a deep inspection job on each table in your dataset with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.

Write a SQL query in BigQuery by using REGEXP_CONTAINS on all tables in your dataset to find rows where the word “street” appears.

Create a discovery scan configuration on your organization with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.

Create a de-identification job in Cloud Data Loss Prevention and use the masking transformation.

Your company operates in three domains: airlines, hotels, and ride-hailing services. Each domain has two teams: analytics and data science, which create data assets in BigQuery with the help of a central data platform. However, as each domain is evolving rapidly, the central data platform team is becoming a bottleneck. This is causing delays in deriving insights from data, and resulting in stale data when pipelines are not kept up to date. You need to design a data mesh architecture by using Dataplex to eliminate the bottleneck. What should you do?

1. Create one lake for each domain. Inside each lake, create one zone for each team. 2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone. 3. Direct each domain to manage their own lake’s data assets.

1. Create one lake for each team. Inside each lake, create one zone for each domain. 2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone. 3. Have the central data platform team manage all zones’ data assets.

1. Create one lake for each team. Inside each lake, create one zone for each domain. 2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone. 3. Direct each domain to manage their own zone’s data assets.

1. Create one lake for each domain. Inside each lake, create one zone for each team. 2. Attach each of the BigQuery datasets created by the individual teams as assets to the respective zone. 3. Have the central data platform team manage all lakes’ data assets.

You have an inventory of VM data stored in the BigQuery table. You want to prepare the data for regular reporting in the most cost-effective way. You need to exclude VM rows with fewer than 8 vCPU in your report. What should you do?

Create a view with a filter to drop rows with fewer than 8 vCPU, and use the UNNEST operator.

Create a materialized view with a filter to drop rows with fewer than 8 vCPU, and use the WITH common table expression.

Create a view with a filter to drop rows with fewer than 8 vCPU, and use the WITH common table expression.

Use Dataflow to batch process and write the result to another BigQuery table.

Your team is building a data lake platform on Google Cloud. As a part of the data foundation design, you are planning to store all the raw data in Cloud Storage. You are expecting to ingest approximately 25 GB of data a day and your billing department is worried about the increasing cost of storing old data. The current business requirements are: - The old data can be deleted anytime. - There is no predefined access pattern of the old data. - The old data should be available instantly when accessed. - There should not be any charges for data retrieval. What should you do to optimize for cost?

Create the bucket with the Autoclass storage class feature.

Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearline, 90 days to coldline, and 365 days to archive storage class. Delete old data as needed.

Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to coldline, 90 days to nearline, and 365 days to archive storage class. Delete old data as needed.

Create an Object Lifecycle Management policy to modify the storage class for data older than 30 days to nearline, 45 days to coldline, and 60 days to archive storage class. Delete old data as needed.

Your company’s data platform ingests CSV file dumps of booking and user profile data from upstream sources into Cloud Storage. The data analyst team wants to join these datasets on the email field available in both the datasets to perform analysis. However, personally identifiable information (PII) should not be accessible to the analysts. You need to de-identify the email field in both the datasets before loading them into BigQuery for analysts. What should you do?

C. Create a pipeline to de-identify the email field by using recordTransformations in Cloud DLP with format-preserving encryption with FFX as the de-identification transformation type.

A. Create a pipeline to de-identify the email field by using recordTransformations in Cloud Data Loss Prevention (Cloud DLP) with masking as the de-identification transformations type.

B. Load the booking and user profile data into a BigQuery table.

D. Load the booking and user profile data into a BigQuery table.

You have important legal hold documents in a Cloud Storage bucket. You need to ensure that these documents are not deleted or modified. What should you do?

Set a retention policy. Lock the retention policy.

Set a retention policy. Set the default storage class to Archive for long-term digital preservation.

Enable the Object Versioning feature. Add a lifecycle rule.

Enable the Object Versioning feature. Create a copy in a bucket in a different region.

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?

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

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

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

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

You are deploying a batch pipeline in Dataflow. This pipeline reads data from Cloud Storage, transforms the data, and then writes the data into BigQuery. The security team has enabled an organizational constraint in Google Cloud, requiring all Compute Engine instances to use only internal IP addresses and no external IP addresses. What should you do?

D. Ensure that Private Google Access is enabled in the subnetwork. Use Dataflow with only internal IP addresses.

A. Ensure that your workers have network tags to access Cloud Storage and BigQuery. Use Dataflow with only internal IP addresses.

B. Ensure that the firewall rules allow access to Cloud Storage and BigQuery. Use Dataflow with only internal IPs.

C. Create a VPC Service Controls perimeter that contains the VPC network and add Dataflow, Cloud Storage, and BigQuery as allowed services in the perimeter. Use Dataflow with only internal IP addresses.

You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled. You have set the maximum number of workers to 1000. The input of your pipeline is Pub/Sub messages with notifications from Cloud Storage. One of the pipeline transforms reads CSV files and emits an element for every CSV line. The job performance is low, the pipeline is using only 10 workers, and you notice that the autoscaler is not spinning up additional workers. What should you do to improve performance?

Change the pipeline code, and introduce a Reshuffle step to prevent fusion.

Enable Vertical Autoscaling to let the pipeline use larger workers.

Update the job to increase the maximum number of workers.

Use Dataflow Prime, and enable Right Fitting to increase the worker resources.

You have an Oracle database deployed in a VM as part of a Virtual Private Cloud (VPC) network. You want to replicate and continuously synchronize 50 tables to BigQuery. You want to minimize the need to manage infrastructure. What should you do?

Create a Datastream service from Oracle to BigQuery, use a private connectivity configuration to the same VPC network, and a connection profile to BigQuery.

Deploy Apache Kafka in the same VPC network, use Kafka Connect Oracle Change Data Capture (CDC), and Dataflow to stream the Kafka topic to BigQuery.

Create a Pub/Sub subscription to write to BigQuery directly. Deploy the Debezium Oracle connector to capture changes in the Oracle database, and sink to the Pub/Sub topic.

Deploy Apache Kafka in the same VPC network, use Kafka Connect Oracle change data capture (CDC), and the Kafka Connect Google BigQuery Sink Connector.

You are deploying an Apache Airflow directed acyclic graph (DAG) in a Cloud Composer 2 instance. You have incoming files in a Cloud Storage bucket that the DAG processes, one file at a time. The Cloud Composer instance is deployed in a subnetwork with no Internet access. Instead of running the DAG based on a schedule, you want to run the DAG in a reactive way every time a new file is received. What should you do?

Enable the Cloud Composer API, and set up Cloud Storage notifications to trigger a Cloud Function.

Enable Private Google Access in the subnetwork, and set up Cloud Storage notifications to a Pub/Sub topic.

Create a push subscription that points to the web server URL.

Write a Cloud Function instance to call the DAG by using the Cloud Composer API and the web server URL.

Use VPC Services Access to reach the web server URL.

You are planning to use Cloud Storage as part of your data lake solution. The Cloud Storage bucket will contain objects ingested from external systems. Each object will be ingested once, and the access patterns of individual objects will be random. You want to minimize the cost of storing and retrieving these objects. You want to ensure that any cost optimization efforts are transparent to the users and applications. What should you do?

Create a Cloud Storage bucket with Autoclass enabled.

Create a Cloud Storage bucket with an Object Lifecycle Management policy to transition objects from Standard to Coldline storage class if an object age reaches 30 days.

Create a Cloud Storage bucket with an Object Lifecycle Management policy to transition objects from Standard to Coldline storage class if an object is not live.

Create two Cloud Storage buckets. Use the Standard storage class for the first bucket, and use the Coldline storage class for the second bucket. Migrate objects from the first bucket to the second bucket after 30 days.

You have several different file type data sources, such as Apache Parquet and CSV. You want to store the data in Cloud Storage. You need to set up an object sink for your data that allows you to use your own encryption keys. You want to use a GUI-based solution. What should you do?

Use Cloud Data Fusion to move files into Cloud Storage.

Use Storage Transfer Service to move files into Cloud Storage.

Use Dataflow to move files into Cloud Storage.

Use BigQuery Data Transfer Service to move files into BigQuery.

Your business users need a way to clean and prepare data before using the data for analysis. Your business users are less technically savvy and prefer to work with graphical user interfaces to define their transformations. After the data has been transformed, the business users want to perform their analysis directly in a spreadsheet. You need to recommend a solution that they can use. What should you do?

Use Dataprep to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.

Use Dataprep to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.

Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.

Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.

You need to ensure that both projects have the appropriate compute resources available. What should you do?

Create two reservations, one for each of the projects. For the SLA project, use an Enterprise Edition with a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, configure on-demand billing.

Create a single Enterprise Edition reservation for both projects. Set a baseline of 300 slots. Enable autoscaling up to 700 slots.

Create two Enterprise Edition reservations, one for each of the projects. For the SLA project, set a baseline of 300 slots and enable autoscaling up to 500 slots. For the ad-hoc project, set a reservation baseline of 0 slots and set the ignore idle slots flag to False.

Create two Enterprise Edition reservations, one for each of the projects. For the SLA project, set a baseline of 800 slots. For the ad-hoc project, enable autoscaling up to 200 slots.

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) build 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 on the data governance team and are implementing security requirements. You need to encrypt all your data in BigQuery by using an encryption key managed by your team. You must implement a mechanism to generate and store encryption material only on your on-premises hardware security module (HSM). You want to rely on Google managed solutions. What should you do?

B. 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.

A. 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.

C. 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.

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

You maintain ETL pipelines. You notice that a streaming pipeline running on Dataflow is taking a long time to process incoming data, which causes output delays. You also noticed that the pipeline graph was automatically optimized by Dataflow and merged into one step. You want to identify where the potential bottleneck is occurring. What should you do?

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.

You are running your BigQuery project in the on-demand billing model and are executing a change data capture (CDC) process that ingests data. The CDC process loads 1 GB of data every 10 minutes into a temporary table, and then performs a merge into a 10 TB target table. This process is very scan intensive and you want to explore options to enable a predictable cost model. You need to create a BigQuery reservation based on utilization information gathered from BigQuery Monitoring and apply the reservation to the CDC process. What should you do?

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.

You are designing a fault-tolerant architecture to store data in a regional BigQuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution. What should you do?

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.

You are building a streaming Dataflow pipeline that ingests noise level data from hundreds of sensors placed near construction sites across a city. The sensors measure noise level every ten seconds, and send that data to the pipeline when levels reach above 70 dBA. You need to detect the average noise level from a sensor when data is received for a duration of more than 30 minutes, but the window ends when no data has been received for 15 minutes. What should you do?

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.

You are creating a data model in BigQuery that will hold retail transaction data. Your two largest tables, sales_transaction_header and sales_transaction_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_transaction_header and sales_transaction_line tables to improve the performance of data analytics queries. What should you do?

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.

You created a new version of a Dataflow streaming data ingestion pipeline that reads from Pub/Sub and writes to BigQuery. The previous version of the pipeline that runs in production uses a 5-minute window for processing. You need to deploy the new version of the pipeline without losing any data, creating inconsistencies, or increasing the processing latency by more than 10 minutes. What should you do?

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.

Your organization's data assets are stored in BigQuery, Pub/Sub, and a PostgreSQL instance running on Compute Engine. Because there are multiple domains and diverse teams using the data, teams in your organization are unable to discover existing data assets. You need to design a solution to improve data discoverability while keeping development and configuration efforts to a minimum. What should you do?

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

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

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

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

You need to create a SQL pipeline. The pipeline runs an aggregate SQL transformation on a BigQuery table every two hours and appends the result to another existing BigQuery table. You need to configure the pipeline to retry if errors occur. You want the pipeline to send an email notification after three consecutive failures. What should you do?

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

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

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

D. 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.

You are monitoring your organization's data lake hosted on BigQuery. The ingestion pipelines read data from Pub/Sub and write the data into tables on BigQuery. After a new version of the ingestion pipelines is deployed, the daily stored data increased by 50%. The volumes of data in Pub/Sub remained the same and only some tables had their daily partition data size doubled. You need to investigate and fix the cause of the data increase. What should you do?

C. 1. Check for duplicate rows in the BigQuery tables that have the daily partition data size doubled. 2. Check the BigQuery Audit logs to find job IDs. 3. Use Cloud Monitoring to determine when the identified Dataflow jobs started and the pipeline code version. 4. When more than one pipeline ingests data into a table, stop all versions except the latest one.

A. 1. Check for duplicate rows in the BigQuery tables that have the daily partition data size doubled. 2. Schedule daily SQL jobs to deduplicate the affected tables. 3. Share the deduplication script with the other operational teams to reuse if this occurs to other tables.

B. 1. Check for code errors in the deployed pipelines. 2. Check for multiple writing to pipeline BigQuery sink. 3. Check for errors in Cloud Logging during the day of the release of the new pipelines. 4. If no errors, restore the BigQuery tables to their content before the last release by using time travel.

D. 1. Roll back the latest deployment. 2. Restore the BigQuery tables to their content before the last release by using time travel. 3. Restart the Dataflow jobs and replay the messages by seeking the subscription to the timestamp of the release.

You have a BigQuery dataset named “customers”. All tables will be tagged by using a Data Catalog tag template named “gdpr”. The template contains one mandatory field, “has_sensitive_data”, with a boolean value. All employees must be able to do a simple search and find tables in the dataset that have either true or false in the “has_sensitive_data” field. However, only the Human Resources (HR) group should be able to see the data inside the tables for which “has_sensitive data” is true. You give all the employees group the bigquery.metadataViewer and bigquery.connectionUser roles on the dataset. You want to minimize configuration overhead. What should you do next?

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

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

B. 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.

D. 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.

You are executing the CI/CD cycle for the code of the directed acyclic graphs (DAGs) running in Cloud Composer. Your team has two Cloud Composer instances: one instance for development and another instance for production. Your team is using a Git repository to maintain and develop the code of the DAGs. You want to deploy the DAGs automatically to Cloud Composer when a certain tag is pushed to the Git repository. What should you do?

1. Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing. 2. If the tests pass, use Cloud Build to copy the code to the bucket of the production instance.

1. Use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the code to the Google Kubernetes Engine (GKE) cluster of the development instance for testing. 2. If the tests pass, use the KubernetesPodOperator to deploy the container to the GKE cluster of the production instance.

1. Use Cloud Build to build a container and the KubernetesPodOperator to deploy the code of the DAG to the Google Kubernetes Engine (GKE) cluster of the development instance for testing. 2. If the tests pass, copy the code to the Cloud Storage bucket of the production instance.

1. Use Cloud Build to copy the code of the DAG to the Cloud Storage bucket of the development instance for DAG testing. 2. If the tests pass, use Cloud Build to build a container with the code of the DAG and the KubernetesPodOperator to deploy the container to the Google Kubernetes Engine (GKE) cluster of the production instance.

You have a BigQuery table that ingests data directly from a Pub/Sub subscription. The ingested data is encrypted with a Google-managed encryption key. You need to meet a new organization policy that requires you to use keys from a centralized Cloud Key Management Service (Cloud KMS) project to encrypt data at rest. What should you do?

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

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

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

D. 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.

You created an analytics environment on Google Cloud so that your data scientist team can explore data without impacting the on-premises Apache Hadoop solution. The data in the on-premises Hadoop Distributed File System (HDFS) cluster is in Optimized Row Columnar (ORC) formatted files with multiple columns of Hive partitioning. The data scientist team needs to be able to explore the data in a similar way as they used on the on-premises HDFS cluster with SQL on the Hive query engine. You need to choose the most cost-effective storage and processing solution. What should you do?

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.

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 Exchanger trigger to resubmit the job in case of zonal failure when submitting the job.

Cloud Project and Data Management Questions

11th Grade

•

62 Qs

Similar activities

Identify parts of Grammar French

8th - 12th Grade

•

66 Qs

Bien Dit 1 Chapter 7.2 Vocabulary Quiz

9th - 12th Grade

•

58 Qs

sp1_Uruguay & LIMP (Intro - cap. 4)

9th - 12th Grade

•

62 Qs

Regular Preterite + ir, ser, ver, dar, hacer

9th - 12th Grade

•

65 Qs

Unit 4 Irregular YO forms and IR

12th Grade

•

60 Qs

Vārdšķiru pareizrakstība

9th - 11th Grade

•

60 Qs

Imagina 4 Vocabulario

9th Grade - University

•

63 Qs

Spanish Level 2: Unit 4 La juventud Pre assessment

9th - 12th Grade

•

63 Qs

Cloud Project and Data Management Questions

Quiz

•

World Languages

•

11th Grade

•

Practice Problem

•

Hard

Nadia Charcap

FREE Resource

62 questions

Show all answers

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

Your organization has two Google Cloud projects, project A and project B. In project A, you have a Pub/Sub topic that receives data from confidential sources. Only the resources in project A should be able to access the data in that topic. You want to ensure that project B and any future project cannot access data in the project A topic. What should you do?

Add firewall rules in project A so only traffic from the VPC in project A is permitted.

Configure VPC Service Controls in the organization with a perimeter around project A.

Use Identity and Access Management conditions to ensure that only users and service accounts in project A can access resources in project A.

Configure VPC Service Controls in the organization with a perimeter around the VPC of project A.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

You stream order data by using a Dataflow pipeline, and write the aggregated result to Memorystore. You provisioned a Memorystore for Redis instance with Basic Tier, 4 GB capacity, which is used by 40 clients for read-only access. You are expecting the number of read-only clients to increase significantly to a few hundred and you need to be able to support the demand. You want to ensure that read and write access availability is not impacted, and any changes you make can be deployed quickly. What should you do?

Create a new Memorystore for Redis instance with Standard Tier. Set capacity to 4 GB and read replica to No read replicas (high availability only). Delete the old instance.

Create a new Memorystore for Redis instance with Standard Tier. Set capacity to 5 GB and create multiple read replicas. Delete the old instance.

Create a new Memorystore for Memcached instance. Set a minimum of three nodes, and memory per node to 4 GB. Modify the Dataflow pipeline and all clients to use the Memcached instance. Delete the old instance.

Create multiple new Memorystore for Redis instances with Basic Tier (4 GB capacity). Modify the Dataflow pipeline and new clients to use all instances.

MULTIPLE SELECT QUESTION

30 sec • 1 pt

You have a streaming pipeline that ingests data from Pub/Sub in production. You need to update this streaming pipeline with improved business logic. You need to ensure that the updated pipeline reprocesses the previous two days of delivered Pub/Sub messages. What should you do? (Choose two.)

A. Use the Pub/Sub subscription clean-retry-policy flag

B. Use Pub/Sub Snapshot capture two days before the deployment.

C. Create a new Pub/Sub subscription two days before the deployment.

D. Use the Pub/Sub subscription retain-acked-messages flag.

E. Use Pub/Sub Seek with a timestamp.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

You currently use a SQL-based tool to visualize your data stored in BigQuery. The data visualizations require the use of outer joins and analytic functions. Visualizations must be based on data that is no less than 4 hours old. Business users are complaining that the visualizations are too slow to generate. You want to improve the performance of the visualization queries while minimizing the maintenance overhead of the data preparation pipeline. What should you do?

Create materialized views with the allow_non_incremental_definition option set to true for the visualization queries. Specify the max_staleness parameter to 4 hours and the enable_refresh parameter to true. Reference the materialized views in the data visualization tool.

Create views for the visualization queries. Reference the views in the data visualization tool.

Create a Cloud Function instance to export the visualization query results as parquet files to a Cloud Storage bucket. Use Cloud Scheduler to trigger the Cloud Function every 4 hours. Reference the parquet files in the data visualization tool.

Create materialized views for the visualization queries. Use the incremental updates capability of BigQuery materialized views to handle changed data automatically. Reference the materialized views in the data visualization tool.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

You need to modernize your existing on-premises data strategy. Your organization currently uses: - Apache Hadoop clusters for processing multiple large data sets, including on-premises Hadoop Distributed File System (HDFS) for data replication. - Apache Airflow to orchestrate hundreds of ETL pipelines with thousands of job steps. You need to set up a new architecture in Google Cloud that can handle your Hadoop workloads and requires minimal changes to your existing orchestration processes. What should you do?

Use Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.

Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.

Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Convert your ETL pipelines to Dataflow.

Use Dataproc to migrate your Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Use Cloud Data Fusion to visually design and deploy your ETL pipelines.

MULTIPLE SELECT QUESTION

30 sec • 1 pt

You recently deployed several data processing jobs into your Cloud Composer 2 environment. You notice that some tasks are failing in Apache Airflow. On the monitoring dashboard, you see an increase in the total workers memory usage, and there were worker pod evictions. You need to resolve these errors. What should you do? (Choose two.)

A. Increase the directed acyclic graph (DAG) file parsing interval.

B. Increase the Cloud Composer 2 environment size from medium to large.

C. Increase the maximum number of workers and reduce worker concurrency.

D. Increase the memory available to the Airflow workers.

E. Increase the memory available to the Airflow trigger.

MULTIPLE CHOICE QUESTION

30 sec • 1 pt

You are on the data governance team and are implementing security requirements to deploy resources. You need to ensure that resources are limited to only the europe-west3 region. You want to follow Google-recommended practices. What should you do?

Set the constraints/gcp.resourceLocations organization policy constraint to in:europe-west3-locations.

Deploy resources with Terraform and implement a variable validation rule to ensure that the region is set to the europe-west3 region for all resources.

Set the constraints/gcp.resourceLocations organization policy constraint to in:eu-locations.

Create a Cloud Function to monitor all resources created and automatically destroy the ones created outside the europe-west3 region.

Access all questions and much more by creating a free account

Create resources

Host any resource

Get auto-graded reports

Continue with Google

Continue with Email

Continue with Classlink

Continue with Clever

or continue with

Microsoft

Apple

Others

Already have an account?

Similar Resources on Wayground

65 questions

Vocabulario 5.2

Quiz

•

4th - 12th Grade

60 questions

auténtico 1 ch 2B vocab and estar

Quiz

•

6th - 12th Grade

65 questions

Time Telling

Quiz

•

KG - University

65 questions

Por vs. Para

Quiz

•

11th Grade

65 questions

El Mundo de Trabajo y el futuro

Quiz

•

11th Grade

57 questions

Ser vs estar

Quiz

•

9th - 12th Grade

59 questions

Present Tense Shoe Verbs E to IE

Quiz

•

10th - 12th Grade

60 questions

Passe Compose versus Imparfait

Quiz

•

9th - 12th Grade

Popular Resources on Wayground

15 questions

Fractions on a Number Line

Quiz

•

3rd Grade

20 questions

Equivalent Fractions

Quiz

•

3rd Grade

25 questions

Multiplication Facts

Quiz

•

5th Grade

54 questions

Analyzing Line Graphs & Tables

Quiz

•

4th Grade

$fractions$

22 questions

fractions

Quiz

•

3rd Grade

20 questions

Main Idea and Details

Quiz

•

5th Grade

20 questions

Context Clues

Quiz

•

6th Grade

15 questions

Equivalent Fractions

Quiz

•

4th Grade

Discover more resources for World Languages

20 questions

-AR -ER -IR present tense

Quiz

•

10th - 12th Grade

28 questions

Ser vs estar

Quiz

•

9th - 12th Grade

20 questions

Preterito indefinido - Regulares / Irregulares

Quiz

•

9th - 12th Grade

19 questions

Gustar and similar verbs

Quiz

•

9th - 12th Grade

20 questions

verbos reflexivos en español

Quiz

•

9th - 12th Grade

25 questions

Preterito regular

Quiz

•

10th - 12th Grade

20 questions

Verbos regulares en el presente

Quiz

•

6th - 12th Grade

25 questions

Spanish Cognates

Quiz

•

7th - 12th Grade