Professional Data Engineer Free Practice Questions — Page 11
Question 83
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?
A. Set the constraints/gcp.resourceLocations organization policy constraint to in:europe-west3-locations.
B. 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.
C. Set the constraints/gcp.resourceLocations organization policy constraint to in:eu-locations.
D. Create a Cloud Function to monitor all resources created and automatically destroy the ones created outside the europe-west3 region.
Show Answer
Correct Answer: A
Explanation: Google-recommended practice for enforcing location restrictions is to use Organization Policy. The constraints/gcp.resourceLocations constraint centrally enforces where resources can be created. Setting it to in:europe-west3-locations restricts all supported resources to only the europe-west3 region. Other options rely on tooling, monitoring, or broader regions and do not provide preventive, organization-wide enforcement.
Question 84
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?
A. Use Bigtable for your large workloads, with connections to Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.
B. Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Orchestrate your pipelines with Cloud Composer.
C. Use Dataproc to migrate Hadoop clusters to Google Cloud, and Cloud Storage to handle any HDFS use cases. Convert your ETL pipelines to Dataflow.
D. 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.
Show Answer
Correct Answer: B
Explanation: The requirement is to modernize Hadoop workloads with minimal changes. Dataproc is the managed Google Cloud service that natively supports Apache Hadoop and Spark, allowing lift-and-shift of existing clusters. Cloud Storage is the recommended replacement for HDFS, providing scalable, durable storage integrated with Dataproc. Since the organization already uses Apache Airflow, Cloud Composer (managed Airflow) allows reusing existing orchestration logic with minimal modification. Other options require significant redesign (Bigtable, Dataflow, or Cloud Data Fusion).
Question 85
You are using a Dataflow streaming job to read messages from a message bus that does not support exactly-once delivery. Your job then applies some transformations, and loads the result into BigQuery. You want to ensure that your data is being streamed into BigQuery with exactly-once delivery semantics. You expect your ingestion throughput into BigQuery to be about 1.5 GB per second. What should you do?
A. Use the BigQuery Storage Write API and ensure that your target BigQuery table is regional.
B. Use the BigQuery Storage Write API and ensure that your target BigQuery table is multiregional.
C. Use the BigQuery Streaming API and ensure that your target BigQuery table is regional.
D. Use the BigQuery Streaming API and ensure that your target BigQuery table is multiregional.
Show Answer
Correct Answer: B
Explanation: To achieve exactly-once delivery semantics when ingesting from a source without exactly-once guarantees, you must use the BigQuery Storage Write API, which supports exactly-once writes via stream offsets. Given the expected ingestion rate of ~1.5 GB/s, a multiregional BigQuery table is required because the Storage Write API throughput limit is much higher for multiregional locations (up to ~3 GB/s) than for regional locations (~300 MB/s). The legacy BigQuery Streaming API does not provide exactly-once semantics.
Question 86
Your organization uses a multi-cloud data storage strategy, storing data in Cloud Storage, and data in Amazon Web Services’ (AWS) S3 storage buckets. All data resides in US regions. You want to query up-to-date data by using BigQuery, regardless of which cloud the data is stored in. You need to allow users to query the tables from BigQuery without giving direct access to the data in the storage buckets. What should you do?
A. Setup a BigQuery Omni connection to the AWS S3 bucket data. Create BigLake tables over the Cloud Storage and S3 data and query the data using BigQuery directly.
B. Set up a BigQuery Omni connection to the AWS S3 bucket data. Create external tables over the Cloud Storage and S3 data and query the data using BigQuery directly.
C. Use the Storage Transfer Service to copy data from the AWS S3 buckets to Cloud Storage buckets. Create BigLake tables over the Cloud Storage data and query the data using BigQuery directly.
D. Use the Storage Transfer Service to copy data from the AWS S3 buckets to Cloud Storage buckets. Create external tables over the Cloud Storage data and query the data using BigQuery directly.
Show Answer
Correct Answer: A
Explanation: BigQuery Omni is required to query data that remains in AWS S3 without copying it to Google Cloud. When using BigQuery Omni, external data in S3 must be accessed through BigLake tables, not traditional external tables. BigLake provides a unified table abstraction and fine‑grained access control, allowing users to query up‑to‑date data in both Cloud Storage and S3 from BigQuery without granting direct access to the underlying storage buckets. Options that rely on external tables or data transfer either do not support S3 directly or violate the requirement to avoid data movement.
Question 87
You are architecting a data transformation solution for BigQuery. Your developers are proficient with SQL and want to use the ELT development technique. In addition, your developers need an intuitive coding environment and the ability to manage SQL as code. You need to identify a solution for your developers to build these pipelines. What should you do?
A. Use Dataform to build, manage, and schedule SQL pipelines.
B. Use Dataflow jobs to read data from Pub/Sub, transform the data, and load the data to BigQuery.
C. Use Data Fusion to build and execute ETL pipelines.
D. Use Cloud Composer to load data and run SQL pipelines by using the BigQuery job operators.
Show Answer
Correct Answer: A
Explanation: The requirements point to an ELT approach using SQL in BigQuery, SQL managed as code, and an intuitive developer experience. Dataform is purpose-built for BigQuery ELT, lets developers define transformations purely in SQL, supports version control and testing, and provides scheduling and orchestration. The other options focus on ETL, streaming, or orchestration rather than SQL-centric ELT development.
Question 88
One of your encryption keys stored in Cloud Key Management Service (Cloud KMS) was exposed. You need to re- encrypt all of your CMEK-protected Cloud Storage data that used that key, and then delete the compromised key. You also want to reduce the risk of objects getting written without customer-managed encryption key (CMEK) protection in the future. What should you do?
A. Rotate the Cloud KMS key version. Continue to use the same Cloud Storage bucket.
B. Create a new Cloud KMS key. Set the default CMEK key on the existing Cloud Storage bucket to the new one.
C. Create a new Cloud KMS key. Create a new Cloud Storage bucket. Copy all objects from the old bucket to the new one bucket while specifying the new Cloud KMS key in the copy command.
D. Create a new Cloud KMS key. Create a new Cloud Storage bucket configured to use the new key as the default CMEK key. Copy all objects from the old bucket to the new bucket without specifying a key.
Show Answer
Correct Answer: D
Explanation: You must re-encrypt all existing objects with a new, uncompromised CMEK and also prevent future writes from accidentally using Google-managed encryption. Rotating the key does not re-encrypt existing data, so A is incorrect. Simply setting a new default CMEK on the existing bucket (B) does not re-encrypt existing objects. Copying objects while explicitly specifying a key (C) re-encrypts data but does not reduce the risk of future objects being written without CMEK if defaults change or are omitted. Creating a new bucket with the new key set as the default CMEK and copying objects without specifying a key ensures all copied data is re-encrypted and all future objects are automatically CMEK-protected, after which the compromised key can be deleted.
Question 89
Your infrastructure team has set up an interconnect link between Google Cloud and the on-premises network. You are designing a high-throughput streaming pipeline to ingest data in streaming from an Apache Kafka cluster hosted on- premises. You want to store the data in BigQuery, with as minimal latency as possible. What should you do?
A. Setup a Kafka Connect bridge between Kafka and Pub/Sub. Use a Google-provided Dataflow template to read the data from Pub/Sub, and write the data to BigQuery.
B. Use a proxy host in the VPC in Google Cloud connecting to Kafka. Write a Dataflow pipeline, read data from the proxy host, and write the data to BigQuery.
C. Use Dataflow, write a pipeline that reads the data from Kafka, and writes the data to BigQuery.
D. Setup a Kafka Connect bridge between Kafka and Pub/Sub. Write a Dataflow pipeline, read the data from Pub/Sub, and write the data to BigQuery.
Show Answer
Correct Answer: C
Explanation: The requirement is high-throughput streaming with minimal latency over an existing Interconnect. Reading directly from the on‑premises Kafka cluster using Dataflow avoids adding an extra hop and replication layer (Pub/Sub), which would increase end-to-end latency. Dataflow natively supports Kafka as a streaming source and can write directly to BigQuery, making this the lowest-latency architecture among the options.
Question 90
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?
A. Use BigQuery Data Transfer Service by using the Java Database Connectivity (JDBC) driver with FastExport connection.
B. Create a Teradata Parallel Transporter (TPT) export script to export the historical data, and import to BigQuery by using the bq command-line tool.
C. Use BigQuery Data Transfer Service with the Teradata Parallel Transporter (TPT) tbuild utility.
D. 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.
Show Answer
Correct Answer: A
Explanation: When local storage on the Teradata system is limited and you want the least amount of custom programming, the recommended approach is to use BigQuery Data Transfer Service with the Teradata JDBC driver using a FastExport connection. This method streams data directly from Teradata to BigQuery without staging large export files on local disk, and it avoids writing and maintaining custom TPT scripts. Google’s Teradata migration guidance explicitly recommends JDBC FastExport when local storage constraints exist.
Question 91
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 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?
A. Ask each team to create authorized views of their data. Grant the biquery.jobUser role to each team.
B. Create a BigQuery scheduled query to replicate all customer data into team projects.
C. Ask each team to publish their data in Analytics Hub. Direct the other teams to subscribe to them.
D. Enable each team to create materialized views of the data they need to access in their projects.
Show Answer
Correct Answer: C
Explanation: Analytics Hub is designed for scalable, organization-wide data sharing in BigQuery. Each team retains ownership and control of its datasets while publishing them as data products that other teams can subscribe to and query from their own projects. This avoids data duplication, minimizes operational overhead, supports cross-project access, and reduces storage and maintenance costs compared to replication, views management, or materialized views.
Question 92
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?
A. Use your model to run predictions on fresh customer input data.
B. Monitor your model performance, and make any adjustments needed.
C. Delineate what data will be used for testing and what will be used for training the model.
D. Test and evaluate your model on your curated data to determine how well the model performs.
Show Answer
Correct Answer: C
Explanation: After data processing (cleaning, transforming, and preparing features), the next step in the model development lifecycle is to split the dataset into training and testing (and possibly validation) sets. This must be done before training or evaluating a model. Options A, B, and D all assume that a model has already been trained, which has not yet occurred.
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