Professional Data Engineer Free Practice Questions — Page 15
Question 125
You have a Cloud SQL for PostgreSQL instance in Region’ with one read replica in Region2 and another read replica in Region3. An unexpected event in Region’ requires that you perform disaster recovery by promoting a read replica in Region2. You need to ensure that your application has the same database capacity available before you switch over the connections. What should you do?
A. Enable zonal high availability on the primary instance. Create a new read replica in a new region.
B. Create a cascading read replica from the existing read replica in Region3.
C. Create two new read replicas from the new primary instance, one in Region3 and one in a new region.
D. Create a new read replica in Region1, promote the new read replica to be the primary instance, and enable zonal high availability.
Show Answer
Correct Answer: C
Explanation: After promoting the Region2 read replica to become the new primary, the remaining replicas based on the old primary are no longer sufficient for restoring the original read capacity and replication topology. To regain equivalent capacity and resilience before directing application traffic to the new primary, create new read replicas from the promoted primary. Option C does this by creating two new replicas, restoring the number of read replicas available and establishing replication from the new primary.
Question 126
You have a BigQuery table that contains customer data, including sensitive information such as names and addresses. You need to share the customer data with your data analytics and consumer support teams securely. The data analytics team needs to access the data of all the customers, but must not be able to access the sensitive data. 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.)
A. Create two separate authorized datasets; one for the data analytics team and another for the consumer support team.
B. Ensure that the data analytics team members do not have the Data Catalog Fine-Grained Reader role for the policy tags.
C. Replace the authorized dataset with an authorized view. Use row-level security and apply filter_expression to limit data access.
D. Remove the bigquery.dataViewer role from the data analytics team on the authorized datasets.
E. Enforce access control in the policy tag taxonomy.
Show Answer
Correct Answer: B, E
Explanation: Column-level security with policy tags is enforced through the policy tag taxonomy. If users still see sensitive columns, verify that access control enforcement is enabled on the taxonomy and that those users have not been granted the Fine-Grained Reader access that allows access governed by the policy tags. Authorized datasets address dataset access, not column-level restrictions.
Question 127
You are developing an Apache Beam pipeline to extract data from a Cloud SQL instance by using JdbcIO. You have two projects running in Google Cloud. The pipeline will be deployed and executed on Dataflow in Project A. The Cloud SQL. instance is running 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?
A. Set up VPC Network Peering between Project A and Project B. Add a firewall rule to allow the peered subnet range to access all instances on the network.
B. Turn off the external IP addresses on the Dataflow worker. Enable Cloud NAT in Project A.
C. Add the external IP addresses of the Dataflow worker as authorized networks in the Cloud SQL instance.
D. 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.
Show Answer
Correct Answer: A
Explanation: The intended solution is to provide private network connectivity between the Dataflow workers in Project A and the Cloud SQL private IP network in Project B using VPC Network Peering and appropriate firewall rules. Disabling external IPs or using Cloud NAT does not create connectivity to a private-only Cloud SQL instance, and authorizing external IPs requires public IP access. Introducing a proxy VM is unnecessary for the exam's intended architecture and adds operational complexity.
Question 128
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 should you do?
A. Provide the partner organization a copy of your CMEKs to decrypt the data.
B. Export the tables to parquet files to a Cloud Storage bucket and grant the storageinsights.viewer role on the bucket to the partner organization.
C. Copy the tables you need to share to a dataset without CMEKs. Create an Analytics Hub listing for this dataset.
D. Create an authorized view that contains the CMEK to decrypt the data when accessed.
Show Answer
Correct Answer: C
Explanation: A is incorrect because CMEKs should never be shared with external organizations. B is incorrect because exporting to Cloud Storage and granting Storage Insights Viewer does not provide access to the data files and does not address CMEK access. D is incorrect because authorized views control table access but do not decrypt data for principals that lack access to the CMEK. Copying the required tables into a dataset encrypted with Google-managed keys (or otherwise not requiring the partner to access your CMEK) and sharing them through Analytics Hub is the appropriate approach for external sharing.
Question 129
You have a Standard Tier Memorystore for Redis instance deployed in a production environment. You need to simulate a Redis instance failover in the most accurate disaster recovery situation, and ensure that the failover has no impact on production data. What should you do?
A. Create a Standard Tier Memorystore for Redis instance in the development environment. Initiate a manual failover by using the limited-data-loss data protection mode.
B. Create a Standard Tier Memorystore for Redis instance in a development environment. Initiate a manual failover by using the force-data-loss data protection mode.
C. Increase one replica to Redis instance in production environment. Initiate a manual failover by using the force-data-loss data protection mode.
D. Initiate a manual failover by using the limited-data-loss data protection mode to the Memorystore for Redis instance in the production environment.
Show Answer
Correct Answer: B
Explanation: To avoid any impact on production data, the failover test should be performed on a separate Standard Tier Memorystore for Redis instance in a development environment. To simulate the most severe and realistic disaster recovery behavior, use the force-data-loss manual failover mode, which does not wait for replica synchronization and exercises the aggressive failover path. Running the test in production (C or D) contradicts the requirement to avoid impacting production data, while limited-data-loss mode (A) prioritizes minimizing data loss rather than simulating the most aggressive disaster recovery scenario.
Question 131
You are troubleshooting your Dataflow pipeline that processes data from Cloud Storage to BigQuery. You have discovered that the Dataflow worker nodes cannot communicate with one another. 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?
A. Determine whether your Dataflow pipeline has a custom network tag set.
B. Determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 for the Dataflow network tag.
C. Determine whether there is a firewall rule set to allow traffic on TCP ports 12345 and 12346 on the subnet used by Dataflow workers.
D. Determine whether your Dataflow pipeline is deployed with the external IP address option enabled.
Show Answer
Correct Answer: B
Explanation: Dataflow workers must communicate with each other over TCP ports 12345 and 12346 (or 12346 only for Streaming Engine in some configurations). Google recommends controlling this access with network tags rather than broad subnet-based firewall rules. If workers cannot communicate, verify that an ingress firewall rule allows TCP 12345 and 12346 for the Dataflow worker network tag. Checking only for a custom tag (A) does not identify the connectivity issue, subnet-based rules (C) are not the recommended practice, and external IPs (D) are unrelated to intra-worker communication.
Question 132
You are using BigQuery with a multi-region dataset that includes a table with the daily sales volumes. This table is updated multiple times per day. You need to protect your sales table in case of regional failures with a recovery point objective (RPO) of less than 24 hours, while keeping costs to a minimum. What should you do?
A. Schedule a daily export of the table to a Cloud Storage dual or multi-region bucket.
B. Schedule a daily copy of the dataset to a backup region.
C. Schedule a daily BigQuery snapshot of the table.
D. Modify ETL job to load the data into both the current and another backup region.
Show Answer
Correct Answer: A
Explanation: BigQuery table snapshots are stored in the same region as the source table and do not protect against a regional failure. BigQuery multi-region locations also do not provide cross-region disaster recovery. To achieve an RPO of less than 24 hours at minimal cost, schedule a daily export to a Cloud Storage dual-region or multi-region bucket in a geographically distinct location. This provides off-site backup that can be restored into BigQuery after a regional outage. Copying datasets or dual-writing to another region incurs higher ongoing costs.
Question 133
You are designing a data mesh on Google Cloud with multiple distinct data engineering teams building data products. The typical data curation design pattern consists of landing files in Cloud Storage, transforming raw data in Cloud Storage and BigQuery datasets, and storing the final curated data product in BigQuery datasets. You need to configure Dataplex to ensure that each team can access only the assets needed to build their data products. You also need to ensure that teams can easily share the curated data product. What should you do?
A. 1. Create a single Dataplex virtual lake and create a single zone to contain landing, raw, and curated data. 2. Provide each data engineering team access to the virtual lake.
B. 1. Create a single Dataplex virtual lake and create a single zone to contain landing, raw, and curated data. 2. Build separate assets for each data product within the zone. 3. Assign permissions to the data engineering teams at the zone level.
C. 1. Create a Dataplex virtual lake for each data product, and create a single zone to contain landing, raw, and curated data. 2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.
D. 1. Create a Dataplex virtual lake for each data product, and create multiple zones for landing, raw, and curated data. 2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.
Show Answer
Correct Answer: D
Explanation: Dataplex best practices for a domain-oriented data mesh are to model each data domain/data product as its own lake and organize data lifecycle stages (landing/raw/curated) into separate zones. This provides isolation and governance for each team's assets while allowing curated data products to be shared appropriately. Options A and B incorrectly place all stages into a single zone, and C omits the recommended separation into multiple zones.
Question 134
A shipping company has live package-tracking data that is sent to an Apache Kafka stream in real time. This is then loaded into BigQuery. Analysts in your company want to query the tracking data in BigQuery to analyze geospatial trends in the lifecycle of a package. 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?
A. Re-create the table using data partitioning on the package delivery date.
B. Implement clustering in BigQuery on the package-tracking ID column.
C. Implement clustering in BigQuery on the ingest date column.
D. Tier older data onto Cloud Storage files and create a BigQuery table using Cloud Storage as an external data source.
Show Answer
Correct Answer: B
Explanation: Clustering improves query performance by physically organizing related rows within partitions based on the clustered column. Since analysts analyze the lifecycle of individual packages, clustering on the package-tracking ID co-locates records for the same package and speeds those queries. Clustering on the ingest date provides little benefit because the table is already partitioned by ingest date, changing partitioning to delivery date does not match the stated need to create a clustered table, and moving older data to external tables would generally reduce query performance rather than improve it.
Question 137
You need ads data to serve AI models and historical data for analytics. Longtail and outlier data points need to be identified. You want to cleanse the data in near-real time before running it through AI models. What should you do?
A. Use Cloud Storage as a data warehouse, shell scripts for processing, and BigQuery to create views for desired datasets.
B. Use Dataflow to identify longtail and outlier data points programmatically, with BigQuery as a sink.
C. Use BigQuery to ingest, prepare, and then analyze the data, and then run queries to create views.
D. Use Cloud Composer to identify longtail and outlier data points, and then output a usable dataset to BigQuery.
Show Answer
Correct Answer: B
Explanation: Dataflow is the appropriate service for near-real-time stream processing and data cleansing. It can programmatically detect long-tail and outlier data points as data arrives and write the cleansed results into BigQuery, which is well suited for historical analytics and serving downstream AI workloads. Cloud Storage is not a data warehouse, BigQuery alone is not intended for this kind of streaming transformation pipeline, and Cloud Composer orchestrates workflows rather than performing the data processing itself.
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