Professional Data Engineer Free Practice Questions — Page 17
Question 143
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old. What should you do?
A. Disable caching by editing the report settings.
B. Disable caching in BigQuery by editing table details.
C. Refresh your browser tab showing the visualizations.
D. Clear your browser history for the past hour then reload the tab showing the virtualizations.
Show Answer
Correct Answer: A
Explanation: Google Data Studio (Looker Studio) caches data by default—typically for up to about an hour—to improve performance and reduce query load on sources like BigQuery. This caching can prevent very recent data from appearing in visualizations. Disabling caching in the report settings forces Data Studio to query BigQuery for fresh data, allowing data less than 1 hour old to appear. Browser refreshes or clearing history do not bypass Data Studio’s cache, and changing BigQuery table settings does not control Data Studio’s report-level caching.
Question 144
You are building a new data pipeline to share data between two different types of applications: jobs generators and job runners. Your solution must scale to accommodate increases in usage and must accommodate the addition of new applications without negatively affecting the performance of existing ones. What should you do?
A. Create an API using App Engine to receive and send messages to the applications
B. Use a Cloud Pub/Sub topic to publish jobs, and use subscriptions to execute them
C. Create a table on Cloud SQL, and insert and delete rows with the job information
D. Create a table on Cloud Spanner, and insert and delete rows with the job information
Show Answer
Correct Answer: B
Explanation: Cloud Pub/Sub is designed for scalable, decoupled messaging between producers and consumers. Job generators can publish jobs to a topic, while job runners consume them via subscriptions. Pub/Sub automatically scales with traffic, supports multiple independent subscribers, and allows new applications to be added without impacting existing ones. The other options introduce tighter coupling, scalability limits, or unnecessary complexity for this use case.
Question 145
You have several Spark jobs that run on a Cloud Dataproc cluster on a schedule. Some of the jobs run in sequence, and some of the jobs run concurrently. You need to automate this process. What should you do?
A. Create a Cloud Dataproc Workflow Template
B. Create an initialization action to execute the jobs
C. Create a Directed Acyclic Graph in Cloud Composer
D. Create a Bash script that uses the Cloud SDK to create a cluster, execute jobs, and then tear down the cluster
Show Answer
Correct Answer: C
Explanation: The requirement is to automate scheduled Spark jobs with both sequential and concurrent execution. Cloud Composer (managed Airflow) is designed for workflow orchestration: it natively supports DAGs with task dependencies, parallelism, retries, monitoring, and built-in scheduling. Dataproc Workflow Templates can define job dependencies but do not provide native scheduling, so an external scheduler/orchestrator would still be required. Initialization actions and bash scripts are not suitable for orchestrating complex, scheduled workflows. Therefore, Cloud Composer is the best fit.
Question 146
You have a data pipeline with a Dataflow job that aggregates and writes time series metrics to Bigtable. You notice that data is slow to update in Bigtable. This data feeds a dashboard used by thousands of users across the organization. You need to support additional concurrent users and reduce the amount of time required to write the data. Which two actions should you take? (Choose two.)
A. Configure your Dataflow pipeline to use local execution
B. Increase the maximum number of Dataflow workers by setting maxNumWorkers in PipelineOptions
C. Increase the number of nodes in the Bigtable cluster
D. Modify your Dataflow pipeline to use the Flatten transform before writing to Bigtable
E. Modify your Dataflow pipeline to use the CoGroupByKey transform before writing to Bigtable
Show Answer
Correct Answer: B, C
Explanation: The slowdown is due to insufficient parallelism in the Dataflow job and limited write throughput in Bigtable. Increasing maxNumWorkers allows Dataflow to process and write data in parallel, reducing pipeline latency. Increasing the number of Bigtable nodes increases write and read throughput, enabling faster ingestion and supporting more concurrent dashboard users. Other options do not address write performance or scalability.
Question 147
Your company is in the process of migrating its on-premises data warehousing solutions to BigQuery. The existing data warehouse uses trigger-based change data capture (CDC) to apply updates from multiple transactional database sources on a daily basis. With BigQuery, your company hopes to improve its handling of
CDC so that changes to the source systems are available to query in BigQuery in near-real time using log-based CDC streams, while also optimizing for the performance of applying changes to the data warehouse. Which two steps should they take to ensure that changes are available in the BigQuery reporting table with minimal latency while reducing compute overhead? (Choose two.)
A. Perform a DML INSERT, UPDATE, or DELETE to replicate each individual CDC record in real time directly on the reporting table.
B. Insert each new CDC record and corresponding operation type to a staging table in real time.
C. Periodically DELETE outdated records from the reporting table.
D. Periodically use a DML MERGE to perform several DML INSERT, UPDATE, and DELETE operations at the same time on the reporting table.
E. Insert each new CDC record and corresponding operation type in real time to the reporting table, and use a materialized view to expose only the newest version of each unique record.
Show Answer
Correct Answer: B, D
Explanation: To achieve near–real-time availability of CDC data while minimizing compute overhead in BigQuery, the recommended pattern is to decouple ingestion from heavy mutations. Ingesting each CDC event in real time into a staging (delta) table allows low-latency availability without expensive per-row updates. Then, periodically applying those accumulated changes to the reporting table using a single DML MERGE efficiently batches INSERT, UPDATE, and DELETE operations, significantly reducing compute cost compared to row-by-row DML. This aligns with Google’s cost-optimized CDC architecture for BigQuery.
Question 148
Your platform on your on-premises environment generates 100 GB of data daily, composed of millions of structured JSON text files. Your on-premises environment cannot be accessed from the public internet. You want to use Google Cloud products to query and explore the platform data. What should you do?
A. Use Cloud Scheduler to copy data daily from your on-premises environment to Cloud Storage. Use the BigQuery Data Transfer Service to import data into BigQuery.
B. Use a Transfer Appliance to copy data from your on-premises environment to Cloud Storage. Use the BigQuery Data Transfer Service to import data into BigQuery.
C. Use Transfer Service for on-premises data to copy data from your on-premises environment to Cloud Storage. Use the BigQuery Data Transfer Service to import data into BigQuery.
D. Use the BigQuery Data Transfer Service dataset copy to transfer all data into BigQuery.
Show Answer
Correct Answer: C
Explanation: The data is generated daily (100 GB/day) and resides on-premises with no public internet access. Transfer Service for on‑premises data is designed for continuous, recurring transfers from on‑premises environments to Cloud Storage using an agent-based approach and works with private connectivity. Once the data is in Cloud Storage, BigQuery Data Transfer Service (or external table ingestion) can load it into BigQuery for querying. Transfer Appliance is intended for one‑time or infrequent bulk migrations, Cloud Scheduler cannot move data, and BigQuery Data Transfer Service cannot pull directly from on‑premises systems.
Question 149
You need to migrate a Redis database from an on-premises data center to a Memorystore for Redis instance. You want to follow Google-recommended practices and perform the migration for minimal cost, time and effort. What should you do?
A. Make an RDB backup of the Redis database, use the gsutil utility to copy the RDB file into a Cloud Storage bucket, and then import the RDB file into the Memorystore for Redis instance.
B. Make a secondary instance of the Redis database on a Compute Engine instance and then perform a live cutover.
C. Create a Dataflow job to read the Redis database from the on-premises data center and write the data to a Memorystore for Redis instance.
D. Write a shell script to migrate the Redis data and create a new Memorystore for Redis instance.
Show Answer
Correct Answer: A
Explanation: Google-recommended practice for migrating Redis data into Memorystore is to use the native Redis RDB snapshot import/export feature. Creating an RDB backup on-premises, copying it to Cloud Storage, and importing it into Memorystore minimizes cost, time, and operational effort, avoids custom tooling, and follows official guidance. Other options add unnecessary complexity and are not recommended.
Question 150
Government regulations in the banking industry mandate the protection of clients' personally identifiable information (PII). Your company requires PII to be access controlled, encrypted, and compliant with major data protection standards. In addition to using Cloud Data Loss Prevention (Cloud DLP), you want to follow
Google-recommended practices and use service accounts to control access to PII. What should you do?
A. Assign the required Identity and Access Management (IAM) roles to every employee, and create a single service account to access project resources.
B. Use one service account to access a Cloud SQL database, and use separate service accounts for each human user.
C. Use Cloud Storage to comply with major data protection standards. Use one service account shared by all users.
D. Use Cloud Storage to comply with major data protection standards. Use multiple service accounts attached to IAM groups to grant the appropriate access to each group.
Show Answer
Correct Answer: D
Explanation: The requirement is to protect PII using Google‑recommended practices, with access control via service accounts and compliance with major data protection standards. Options that use a single shared service account (A, C) violate least‑privilege and auditability best practices. Option B incorrectly uses service accounts for human users, which Google discourages. Option D best matches the intent of Google’s guidance: use a compliant storage service with encryption and access controls, and use multiple, purpose‑specific service accounts to provide granular access aligned with least privilege, managed at scale via IAM groups. Despite wording imperfections, D is the closest to Google‑recommended practice among the choices.
Question 151
You are using BigQuery and Data Studio to design a customer-facing dashboard that displays large quantities of aggregated data. You expect a high volume of concurrent users. You need to optimize the dashboard to provide quick visualizations with minimal latency. What should you do?
A. Use BigQuery BI Engine with materialized views.
B. Use BigQuery BI Engine with logical views.
C. Use BigQuery BI Engine with streaming data.
D. Use BigQuery BI Engine with authorized views.
Show Answer
Correct Answer: A
Explanation: For a customer-facing dashboard with large aggregated datasets and many concurrent users, the main requirement is low-latency query performance at scale. BigQuery BI Engine accelerates queries by caching data in memory, and materialized views further optimize performance by precomputing and storing aggregated results. This combination minimizes query execution time and reduces load on the underlying tables, making it ideal for fast, consistent visualizations in Data Studio. Logical views, authorized views, or streaming data do not provide the same level of precomputed, in-memory aggregation performance for this use case.
Question 152
You are implementing workflow pipeline scheduling using open source-based tools and Google Kubernetes Engine (GKE). You want to use a Google managed service to simplify and automate the task. You also want to accommodate Shared VPC networking considerations. What should you do?
A. Use Dataflow for your workflow pipelines. Use Cloud Run triggers for scheduling.
B. Use Dataflow for your workflow pipelines. Use shell scripts to schedule workflows.
C. Use Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the host project.
D. Use Cloud Composer in a Shared VPC configuration. Place the Cloud Composer resources in the service project.
Show Answer
Correct Answer: D
Explanation: You want a Google‑managed service to orchestrate and schedule workflows on GKE using open‑source tools while supporting Shared VPC. Cloud Composer (managed Apache Airflow) is designed for workflow scheduling and integrates well with GKE. In a Shared VPC setup, Google’s documented and recommended model is to place the Cloud Composer environment in the service project while attaching it to the host project’s network. This simplifies automation, respects Shared VPC boundaries, and follows Google best practices.
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