WorksheetsPDE-5
Total questions: 54
Worksheet time: 28mins
A TensorFlow machine learning model on Compute Engine virtual machines (n2-standard-32) takes two days to complete training. The model's TensorFlow operations need to run partially on the CPU. You want to reduce the training time in a cost-effective manner. What are you supposed to do?
Change the VM type to n2-highmem-32.
Change the VM type to e2-standard-32.
Train the model using a VM with a GPU hardware accelerator.
Train the model using a VM with a TPU hardware accelerator.
You're creating a machine learning model using BigQuery ML and creating an endpoint to host the model using Vertex AI. This will allow multiple vendors to process continuously streaming data at very close real time. There may be invalid values in these data. What are you supposed to be doing?
Create a new BigQuery dataset and use streaming inserts to land the data from multiple vendors. Configure your BigQuery ML model to use the "ingestion" dataset as the framing data.
Use BigQuery streaming inserts to land the data from multiple vendors where your BigQuery dataset ML model is deployed.
Create a Pub/Sub topic and send all vendor data to it. Connect a Cloud Function to the topic to process the data and store it in BigQuery.
Create a Pub/Sub topic and send all vendor data to it. Use Dataflow to process and sanitize the Pub/Sub data and stream it to BigQuery.
You have a data processing application that runs on Google Kubernetes Engine (GKE). The latest configuration available from the container registry is required to start containers. GPUs, Local Solid State Drives and 8 Gbps of bandwidth are needed on your GKE nodes. You want to efficiently provision the data processing infrastructure and manage the deployment process. What should you do?
Use Compute Engine startup scripts to pull container images, and use gcloud commands to provision the infrastructure.
Use Cloud Build to schedule a job using Terraform build to provision the infrastructure and launch with the most current container images.
Use GKE to autoscale containers, and use gcloud commands to provision the infrastructure.
Use Dataflow to provision the data pipeline, and use Cloud Scheduler to run the job.
For AI models and historical data to be used for analysis, you need advertising data. There is a need to identify long tail and outlier data points. Before running it through AI models, you want to clean the data in near real time. What are you supposed to be doing?
Use Cloud Storage as a data warehouse, shell scripts for processing, and BigQuery to create views for desired datasets.
Use Dataflow to identify longtail and outlier data points programmatically, with BigQuery as a sink.
Use BigQuery to ingest, prepare, and then analyze the data, and then run queries to create views.
Use Cloud Composer to identify longtail and outlier data points, and then output a usable dataset to BigQuery.
A shipping company is sending live package tracking data to an Apache Kafka stream in real time. You will then have BigQuery loading this file. In order to analyse the geospatial trends in package lifecycle, analysts at your company would like to query BigQuery tracking data. The table was originally created with ingest-date partitioning. Over time, the query processing time has increased. You need to copy all the data to a new clustered table. What should you do?
Re-create the table using data partitioning on the package delivery date.
Implement clustering in BigQuery on the package-tracking ID column.
Implement clustering in BigQuery on the ingest date column.
Tier older data onto Cloud Storage files and create a BigQuery table using Cloud Storage as an external data source.
You're building a data mesh on Google Cloud, with multiple different Data Engineering teams that are developing the data products. The typical data curation The design pattern consists of landing files in Cloud Storage, transforming raw data in Cloud Storage and BigQuery Datasets, and storing the final data. 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?
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.
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.
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.
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.
You're using BigQuery with a multiregion dataset that includes the daily sales volume table. Multiple times per day, this table is updated. In the event of regional failure with a recovery point objective that is less than 24 hours, you need to protect your sales table in order to keep costs as low as possible. What are you supposed to be doing?
Schedule a daily export of the table to a Cloud Storage dual or multi-region bucket.
Schedule a daily copy of the dataset to a backup region.
Schedule a daily BigQuery snapshot of the table.
Modify ETL job to load the data into both the current and another backup region.
You're trying to fix the Data Flow pipeline that stores data from cloud storage into BigQuery. You've discovered that the Dataflow worker The nodes are unable to communicate with each other. Your networking team relies on Google Cloud network tags to define firewall rules. You need to identify the issue while following Google-recommended networking security practices. What should you do?
Determine whether your Dataflow pipeline has a custom network tag set.
Determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 for the Dataflow network tag.
Determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 on the subnet used by Dataflow workers.
Determine whether your Dataflow pipeline is deployed with the external IP address option enabled.
The order history of 10 million customers is stored on your company's customer_order table in BigQuery, with a total size of 10 PB. You need to create A dashboard to display order history for the support team. The dashboard has two filters, country_name and username. Both are string data types in the BigQuery table. When a filter is applied, the dashboard fetches the order history from the table and displays the query results. However, the dashboard is slow to show the results when applying the filters to the following query:
SELECT date, order, status FROM customer_order
WHERE country = '<country_name>' AND username = '<username>'
How should you redesign the BigQuery table to support faster access?
Cluster the table by country and username fields.
Cluster the table by country field, and partition by username field.
Partition the table by country and username fields.
Partition the table by _PARTITIONTIME.
You're using a standard Tier store memory instance for Redis in your production environment. In order to be sure that the failover does not affect production data, it is necessary to simulate a Redis instance failure during an accurate disaster recovery scenario. What are you supposed to do?
Create a Standard Tier Memorystore for Redis instance in the development environment. Initiate a manual failover by using the limited-dataloss data protection mode.
Create a Standard Tier Memorystore for Redis instance in a development environment. Initiate a manual failover by using the force-dataloss data protection mode
Increase one replica to Redis instance in production environment. Initiate a manual failover by using the force-data-loss data protection mode.
Initiate a manual failover by using the limited-data-loss data protection mode to the Memorystore for Redis instance in the production environment.
You are administering a BigQuery dataset that uses a customer-managed encryption key (CMEK).
You need to share the dataset with a partner organization that does not have access to your CMEK. What are you supposed to do?
Provide the partner organization a copy of your CMEKs to decrypt the data.
Export the tables to parquet files to a Cloud Storage bucket and grant the storageinsights.viewer role on the bucket to the partner organization.
Copy the tables you need to share to a dataset without CMEKs. Create an Analytics Hub listing for this dataset.
Create an authorized view that contains the CMEK to decrypt the data when accessed.
To extract data from a Cloud SQL instance using JdbcIO, you're developing an Apache Beam pipeline. There are two Google projects running. Cloud. In Project A, the pipeline will be used and performed on Dataflow. The Cloud SQL. Instance is running in 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?
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.
Turn off the external IP addresses on the Dataflow worker. Enable Cloud NAT in Project A.
Add the external IP addresses of the Dataflow worker as authorized networks in the Cloud SQL instance.
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.
You've got a customer table in BigQuery that contains sensitive data, for example names and addresses. Your Data Analytics and Consumer Support teams need to share customer data with you in a secure manner. All customers' data needs to be accessible by the Analytics team, but they must not have access to sensitive information. The consumer support team needs access to all data columns, but must not be able to access customers that no longer have active contracts. You enforced these requirements by using an authorized dataset and policy tags. After implementing these steps, the data analytics team reports that they still have access to the sensitive columns. You need to ensure that the data analytics team does not have access to restricted data. What should you do? (Choose two.)
Create two separate authorized datasets; one for the data analytics team and another for the consumer support team.
Ensure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags.
Replace the authorized dataset with an authorized view. Use row-level security and apply filter_expression to limit data access.
Remove the bigquery.dataViewer role from the data analytics team on the authorized datasets.
Enforce access control in the policy tag taxonomy.
You've got one read replica in Region2 and one read replica in Region3 in your Cloud SQL instance for PostgreSQL. An unexpected This event requires that you perform disaster recovery by promoting read replication in Region 2. You need to ensure that your application has the same database capacity available before you switch over the connections. What should you do?
Enable zonal high availability on the primary instance. Create a new read replica in a new region.
Create a cascading read replica from the existing read replica in Region3.
Create two new read replicas from the new primary instance, one in Region3 and one in a new region.
Create a new read replica in Region1, promote the new read replica to be the primary instance, and enable zonal high availability.
Using Cloud Composer you can synchronize ETL pipelines. One of the tasks in the Apache Airflow directed acyclic graph (DAG) relies on a third-party service. If the task fails, you would like to be notified. What are you supposed to do?
Assign a function with notification logic to the on_retry_callback parameter for the operator responsible for the task at risk.
Configure a Cloud Monitoring alert on the sla_missed metric associated with the task at risk to trigger a notification.
Assign a function with notification logic to the on_failure_callback parameter tor the operator responsible for the task at risk.
Assign a function with notification logic to the sla_miss_callback parameter for the operator responsible for the task at risk.
You're moving a data warehouse on your premises to BigQuery. The MySQL database contains one of the upstream data sources. database that runs in You have no public IP addresses in your onpremises data center. You want to ensure that the data ingestion into BigQuery is done securely and does not go through the public internet. What should you do?
Update your existing on-premises ETL tool to write to BigQuery by using the BigQuery Open Database Connectivity (ODBC) driver. Set up the proxy parameter in the simba.googlebigqueryodbc.ini file to point to your data center’s NAT gateway.
Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Set up Cloud Interconnect between your onpremises data center and Google Cloud. Use Private connectivity as the connectivity method and allocate an IP address range within your VPC network to the Datastream connectivity configuration. Use Server-only as the encryption type when setting up the connection profile in Datastream.
Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Use Forward-SSH tunnel as the connectivity method to establish a secure tunnel between Datastream and your on-premises MySQL database through a tunnel server in your on-premises data center. Use None as the encryption type when setting up the connection profile in Datastream.
Use Datastream to replicate data from your on-premises MySQL database to BigQuery. Gather Datastream public IP addresses of the Google Cloud region that will be used to set up the stream. Add those IP addresses to the firewall allowlist of your on-premises data center. Use IP Allowlisting as the connectivity method and Server-only as the encryption type when setting up the connection profile in Datastream.
With all data residing in the U.S. regions, you are storing and analysing your Relational Databases from BigQuery on Google Cloud. There's also a lot of things you have. There are also stores in the U.S. region of Microsoft Azure and Amazon Web Services. You want to query all your data in BigQuery daily with as little movement of data as possible. What should you do?
Use BigQuery Data Transfer Service to load files from Azure and AWS into BigQuery.
Create a Dataflow pipeline to ingest files from Azure and AWS to BigQuery.
Load files from AWS and Azure to Cloud Storage with Cloud Shell gsutil rsync arguments.
Use the BigQuery Omni functionality and BigLake tables to query files in Azure and AWS.
You've got a lot of files in Cloud Storage that your Data Science team would like to use on models. Users do not have a method at this time The data stored in cloud storage is explored, cleaned and verified. You are looking for a low code solution that can be used by your data science team to quickly cleanse and explore data within Cloud Storage. What should you do?
Provide the data science team access to Dataflow to create a pipeline to prepare and validate the raw data and load data into BigQuery for data exploration.
Create an external table in BigQuery and use SQL to transform the data as necessary. Provide the data science team access to the external tables to explore the raw data.
Load the data into BigQuery and use SQL to transform the data as necessary. Provide the data science team access to staging tables to explore the raw data.
Provide the data science team access to Dataprep to prepare, validate, and explore the data within Cloud Storage.
By using Dataform, you can build an ELT solution in BigQuery. In your final tables, you must perform unique and null value checks. What How can you effectively integrate those checks into your pipeline?
Build BigQuery user-defined functions (UDFs).
Create Dataplex data quality tasks.
Build Dataform assertions into your code.
Write a Spark-based stored procedure.
Click events are sent to the PubSub topic by a Web server as messages. The eventTimestamp attribute in the message, which indicates when a click occurred, is included on the web server. You're running a Dataflow streaming job that reads from this Pubsub topic through a subscription, applies some transformations, and writes the result to another Pubsub topic for use by the advertising department. The advertising department needs to receive each message within 30 seconds of the corresponding click occurrence, but they report receiving the messages late. Your Dataflow job's system lag is about 5 seconds, and the data freshness is about 40 seconds. Inspecting a few messages show no more than 1 second lag between their eventTimestamp and publishTime. What is the problem and what should you do?
The advertising department is causing delays when consuming the messages. Work with the advertising department to fix this.
Messages in your Dataflow job are taking more than 30 seconds to process. Optimize your job or increase the number of workers to fix this.
Messages in your Dataflow job are processed in less than 30 seconds, but your job cannot keep up with the backlog in the Pub/Sub subscription. Optimize your job or increase the number of workers to fix this.
The web server is not pushing messages fast enough to Pub/Sub. Work with the web server team to fix this.
In Apache Parquet format, your organization stores customer data in an on premises Apache Hadoop cluster. The data are processed on a daily basis by Apache Spark jobs that are running in a cluster. You are migrating the Spark jobs and Parquet data to Google Cloud. BigQuery will be used on future transformation pipelines so you need to ensure that your data is available in BigQuery. You want to use managed services, while minimizing ETL data processing changes and overhead costs. What should you do?
Migrate your data to Cloud Storage and migrate the metadata to Dataproc Metastore (DPMS). Refactor Spark pipelines to write and read data on Cloud Storage, and run them on Dataproc Serverless.
Migrate your data to Cloud Storage and register the bucket as a Dataplex asset. Refactor Spark pipelines to write and read data on Cloud Storage, and run them on Dataproc Serverless.
Migrate your data to BigQuery. Refactor Spark pipelines to write and read data on BigQuery, and run them on Dataproc Serverless.
Migrate your data to BigLake. Refactor Spark pipelines to write and read data on Cloud Storage, and run them on Dataproc on Compute Engine.
There are two Google Cloud projects in your organization, 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.
Using the Dataflow pipeline, you're sending order data and writing an aggregate result to memory stores. You've created a memory store for Redis. For instance, four GB of Basic Tier capacity for read only access that 40 clients are using. 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.
You have a streaming pipeline that ingests data from Pub/Sub in production. This streaming pipeline needs to be updated with more business logic. Ensure that the last two days of delivered PubSub messages are processed by the updated pipeline. What are you supposed to be doing? (Choose two)
Use the Pub/Sub subscription clear-retry-policy flag
Use Pub/Sub Snapshot capture two days before the deployment.
Create a new Pub/Sub subscription two days before the deployment.
Use the Pub/Sub subscription retain-acked-messages flag.
Use Pub/Sub Seek with a timestamp.
You're using a SQL based tool to view your BigQuery data at this time. External joins and analytical functions are required to display the data. Data that is not less than 4 hours old shall be used for the projections. The business user complains that it's too slow to create the visualizations. 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.
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.
In your Cloud Composer 2 environment, several data processing jobs have just been installed. You're noticing that some of the Apache tasks don't work. Airflow. You can see an increase in the total worker memory usage on the monitoring dashboard, as well as the eviction of the worker pod. These errors need to be resolved. What are you supposed to do? (Choose two)
Increase the directed acyclic graph (DAG) file parsing interval.
Increase the Cloud Composer 2 environment size from medium to large.
Increase the maximum number of workers and reduce worker concurrency.
Increase the memory available to the Airflow workers.
Increase the memory available to the Airflow triggerer.
You're part of 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'd like to follow the 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.
You're an administrator of BigQuery, which supports a team of data consumers running ad hoc queries and downstream reporting using tools such as Looker. All A single organisational project consolidates data and users. You recently noticed some slowness in query results and want to troubleshoot where the slowdowns are occurring. You think that there might be some job queuing or slot contention occurring as users run jobs, which slows down access to results. You need to investigate the query job information and determine where performance is being affected. What should you do?
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.
Use Cloud Monitoring to view BigQuery metrics and set up alerts that let you know when a certain percentage of slots were used.
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.
Use Cloud Logging to determine if any users or downstream consumers are changing or deleting access grants on tagged resources.
For an application that provides 10 PB of historical product data for Analytics, you moved the database backend. Only the last available state of a product, For the other applications, approximately 10 GB of data is required to be handled by an API. 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. 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 products 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.
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.
A number of sequential load and transformation jobs need to be scheduled. The upstream process will add data files to a cloud storage bucket. The arrival of the new data does not have a fixed schedule. 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?
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.
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.
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.
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.
The workload of the MySQL database is being deployed to Cloud SQL. Support for several readers from different authors will need to be enabled in the database. 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?
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.
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.
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.
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're planning to transfer some of your existing onpremises data to BigQuery on Google Cloud. You're going to stream or batchload data, Depending on how you use it. 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?
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.
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.
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.
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. To avoid custom solutions, you must use the native features of Google Cloud. What are you supposed to 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?
Increase the slot capacity of the project with baseline as 0 and maximum reservation size as 3000.
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 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 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.
To store data in cloud storage, you are designing the architecture of your application. The pipelines that read data are part of your application. 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 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 a dual-region Cloud Storage bucket, and enable turbo replication in your architecture.
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 duration of the message retention for this topic is 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.
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.
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.
Customer and performance data are stored in BigQuery by a number of teams within your organization. Each team must retain complete control of their collected items data, be able to query data within 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 create authorized views of their data. Grant the biquery.jobUser role to each team.
Create a BigQuery scheduled query to replicate all customer data into team projects.
Ask each team to publish their data in Analytics Hub. Direct the other teams to subscribe to them.
Enable each team to create materialized views of the data they need to access in their projects.
To determine the factors that lead to your customers' sales conversion, you are creating a model. The processing of your data has been completed. You're going to continue with the model development lifecycle. What should you do next?
Use your model to run predictions on fresh customer input data.
Monitor your model performance, and make any adjustments needed.
Delineate what data will be used for testing and what will be used for training the model.
Test and evaluate your model on your curated data to determine how well the model performs.
You've got a set of BigQuery data sets, which include client's street addresses. From the dataset, you want to retrieve all occurrences of street addresses. What are you supposed to do?
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 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.
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.
There are three areas in which your company operates: airlines, hotels, and ridehailing services. Analytics and Data Science, which create data assets in BigQuery with the help of a central data platform team, are two teams in each area. But the central data platform team is becoming a bottleneck due to rapid changes in each area. 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 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. Direct each domain to manage their own lake’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.
The Google Cloud Data Lake Platform is being developed by your team. You plan to store all the raw data as part of your Data Foundation design. 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.
CSV file dumps of booking and user profile data from upstream sources are imported into cloud storage by your company's data platform. The data to perform analysis, the analyst team would like to join these data sets in an email field that is available on both datasets. 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?
1. 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.
2. Load the booking and user profile data into a BigQuery table.
1. 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.
2. Load the booking and user profile data into a BigQuery table.
. 1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the email mask as the data masking rule.
3. Assign the policy to the email field in both tables.
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts.
1. Load the CSV files from Cloud Storage into a BigQuery table, and enable dynamic data masking.
2. Create a policy tag with the default masking value as the data masking rule.
3. Assign the policy to the email field in both tables.
4. Assign the Identity and Access Management bigquerydatapolicy.maskedReader role for the BigQuery tables to the analysts
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.
To analyze sales data from a telecommunications service provider, you're designing the BigQuery Data Warehouse. For customers, products and subscriptions a data model has to be created. You can update all customers, products and subscriptions on a monthly basis, but you need to keep an historical record of each data set. 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 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.
Create a denormalized, append-only model with nested and repeated fields. Use the ingestion timestamp to track historical data.
In Dataflow, you're installing the batch pipeline. This pipeline is reading data from cloud storage, transforming it, and then writing it to the database 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?
Ensure that your workers have network tags to access Cloud Storage and BigQuery. Use Dataflow with only internal IP addresses.
Ensure that the firewall rules allow access to Cloud Storage and BigQuery. Use Dataflow with only internal IPs.
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.
Ensure that Private Google Access is enabled in the subnetwork. Use Dataflow with only internal IP addresses.
With streaming engine and horizontal autoscaling enabled, you are running a Dataflow streaming pipeline. You've set a maximum number 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?
Enable Vertical Autoscaling to let the pipeline use larger workers.
Change the pipeline code, and introduce a Reshuffle step to prevent fusion.
Update the job to increase the maximum number of workers.
Use Dataflow Prime, and enable Right Fitting to increase the worker resources.
You're running an Oracle database on a virtual machine that is part of the VCC network. You're trying to replicate and continue to replicate. Sync 50 tables to BigQuery. You want to minimize the need to manage infrastructure. What should you do?
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.
Create a Datastream service from Oracle to BigQuery, use a private connectivity configuration to the same VPC network, and a connection profile to BigQuery.
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?
1. Enable Private Google Access in the subnetwork, and set up Cloud Storage notifications to a Pub/Sub topic. 2. Create a push subscription that points to the web server URL.
1. Enable the Cloud Composer API, and set up Cloud Storage notifications to trigger a Cloud Function. 2. Write a Cloud Function instance to call the DAG by using the Cloud Composer API and the web server URL. 3. Use VPC Serverless Access to reach the web server URL.
1. Enable the Airflow REST API, and set up Cloud Storage notifications to trigger a Cloud Function instance. 2. Create a Private Service Connect (PSC) endpoint. 3. Write a Cloud Function that connects to the Cloud Composer cluster through the PSC endpoint
1. Enable the Airflow REST API, and set up Cloud Storage notifications to trigger a Cloud Function instance. 2. Write a Cloud Function instance to call the DAG by using the Airflow REST API and the web server URL. 3. Use VPC Serverless Access to reach the web server URL.
As part of your data lake solution, you're planning to use cloud storage. Objects that have been ingested from outside will be stored in the cloud storage bucket. 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 Storage Transfer Service to move files into Cloud Storage.
Use Cloud Data Fusion 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 have two projects where you run BigQuery jobs:
• One project runs production jobs that have strict completion time SLAs. These are high priority jobs that must have the required compute resources available when needed. These jobs generally never go below a 300 slot utilization, but occasionally spike up an additional 500 slots.
• The other project is for users to run ad-hoc analytical queries. This project generally never uses more than 200 slots at a time. You want these ad-hoc queries to be billed based on how much data users scan rather than by slot capacity. You need to ensure that both projects have the appropriate compute resources available. What should you do?
Create a single Enterprise Edition reservation for both projects. Set a baseline of 300 slots. Enable autoscaling up to 700 slots.
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 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.
