Professional Data Engineer Free Practice Questions — Page 9
Question 80
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?
A. Create a single Enterprise Edition reservation for both projects. Set a baseline of 300 slots. Enable autoscaling up to 700 slots.
B. 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.
C. 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.
D. 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.
Show Answer
Correct Answer: B
Explanation: The production workload requires guaranteed capacity with a 300-slot baseline and the ability to burst by an additional 500 slots using Enterprise Edition autoscaling. In BigQuery Editions, autoscaling capacity is added on top of the baseline, allowing up to 800 total slots when needed. The ad-hoc project explicitly needs billing based on data scanned rather than reserved slot capacity, which is achieved with on-demand billing rather than a reservation.
Question 81
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?
A. Use Dataprep to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.
B. Use Dataprep to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.
C. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.
D. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.
Show Answer
Correct Answer: A
Explanation: Dataprep provides a graphical interface for non-technical users to visually clean and prepare data. Writing the transformed data to BigQuery is a common workflow. Connected Sheets allows business users to analyze BigQuery data directly from Google Sheets, matching the requirement to work in a spreadsheet. Dataflow is code-oriented rather than GUI-focused, and Looker Studio is a BI dashboard tool rather than a spreadsheet interface.
Question 82
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?
A. Use Storage Transfer Service to move files into Cloud Storage.
B. Use Cloud Data Fusion to move files into Cloud Storage.
C. Use Dataflow to move files into Cloud Storage.
D. Use BigQuery Data Transfer Service to move files into BigQuery.
Show Answer
Correct Answer: B
Explanation: Cloud Data Fusion is the GUI-based, no-code data integration service designed to connect file-based sources (such as CSV and Parquet) to sinks including Cloud Storage. It supports customer-managed encryption keys (CMEK) for deployments and integrates with Cloud Storage buckets protected by CMEK. Storage Transfer Service is GUI-based for transfers but is not the intended object-sink/data-pipeline solution here and does not itself provide CMEK encryption on transferred data. Dataflow is code-based, and BigQuery Data Transfer Service targets BigQuery rather than Cloud Storage.
Question 83
You are planning to use Cloud Storage as part of your data lake solution. The Cloud Storage bucket will contain objects ingested from external systems. Each object will be ingested once, and the access patterns of individual objects will be random. You want to minimize the cost of storing and retrieving these objects. You want to ensure that any cost optimization efforts are transparent to the users and applications. What should you do?
A. Create a Cloud Storage bucket with Autoclass enabled.
B. 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.
C. 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.
D. 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.
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Correct Answer: A
Explanation: Autoclass is designed to automatically optimize storage class based on object access patterns across Standard, Nearline, Coldline, and Archive while keeping the bucket interface unchanged for users and applications. This minimizes storage costs transparently without requiring manual lifecycle thresholds or moving objects between buckets. Lifecycle rules based only on age are less adaptive to random access patterns, and using two buckets adds operational complexity and is not transparent.
Question 84
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?
A. 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.
B. 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.
C. 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.
D. 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.
Show Answer
Correct Answer: A
Explanation: For a Cloud Composer 2 environment without Internet access, the recommended reactive pattern is to use Cloud Storage notifications to Pub/Sub and have Airflow consume/trigger work from Pub/Sub rather than invoking the private Airflow web server. Options B, C, and D rely on calling the Airflow web server/REST API, which is problematic for private environments as described in Cloud Composer guidance. Enabling Private Google Access allows private resources to access Google APIs such as Pub/Sub.
Question 85
You have an Oracle database deployed in a VM as part of a Virtual Private Cloud (VPC) network. You want to replicate and continuously synchronize 50 tables to BigQuery. You want to minimize the need to manage infrastructure. What should you do?
A. 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.
B. 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.
C. Deploy Apache Kafka in the same VPC network, use Kafka Connect Oracle change data capture (CDC), and the Kafka Connect Google BigQuery Sink Connector.
D. Create a Datastream service from Oracle to BigQuery, use a private connectivity configuration to the same VPC network, and a connection profile to BigQuery.
Show Answer
Correct Answer: D
Explanation: Datastream is Google Cloud's fully managed, serverless change data capture (CDC) service that supports continuous replication from Oracle to BigQuery. Using private connectivity within the VPC minimizes infrastructure management while providing ongoing synchronization of the Oracle tables.
Question 86
You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled. You have set the maximum number of workers to 1000. The input of your pipeline is Pub/Sub messages with notifications from Cloud Storage. One of the pipeline transforms reads CSV files and emits an element for every CSV line. The job performance is low, the pipeline is using only 10 workers, and you notice that the autoscaler is not spinning up additional workers. What should you do to improve performance?
A. Enable Vertical Autoscaling to let the pipeline use larger workers.
B. Change the pipeline code, and introduce a Reshuffle step to prevent fusion.
C. Update the job to increase the maximum number of workers.
D. Use Dataflow Prime, and enable Right Fitting to increase the worker resources.
Show Answer
Correct Answer: B
Explanation: The most likely cause is Dataflow fusion limiting parallelism. Reading a CSV file and emitting one element per line can keep downstream work fused with the file read, preventing sufficient parallelization and giving the autoscaler little reason to add workers. Introducing a Reshuffle step breaks fusion, allowing work to be redistributed across more workers and improving throughput. Increasing the maximum workers is irrelevant because it is already set very high, and larger workers or Right Fitting do not address the lack of parallelism in this streaming pipeline.
Question 87
You are deploying a batch pipeline in Dataflow. This pipeline reads data from Cloud Storage, transforms the data, and then writes the data into BigQuery. The security team has enabled an organizational constraint in Google Cloud, requiring all Compute Engine instances to use only internal IP addresses and no external IP addresses. What should you do?
A. Ensure that your workers have network tags to access Cloud Storage and BigQuery. Use Dataflow with only internal IP addresses.
B. Ensure that the firewall rules allow access to Cloud Storage and BigQuery. Use Dataflow with only internal IPs.
C. Create a VPC Service Controls perimeter that contains the VPC network and add Dataflow, Cloud Storage, and BigQuery as allowed services in the perimeter. Use Dataflow with only internal IP addresses.
D. Ensure that Private Google Access is enabled in the subnetwork. Use Dataflow with only internal IP addresses.
Show Answer
Correct Answer: D
Explanation: Dataflow workers running without external IP addresses must still reach Google APIs such as Cloud Storage and BigQuery. The required configuration is to enable Private Google Access on the subnetwork and configure Dataflow to use only internal IP addresses. Network tags and firewall rules do not provide access to Google APIs without external IPs, and VPC Service Controls are for defining service perimeters, not for enabling connectivity from private-only VMs.
Question 88
You are designing a data warehouse in BigQuery to analyze sales data for a telecommunication service provider. You need to create a data model for customers, products, and subscriptions. All customers, products, and subscriptions can be updated monthly, but you must maintain a historical record of all data. You plan to use the visualization layer for current and historical reporting. You need to ensure that the data model is simple, easy-to-use, and cost-effective. What should you do?
A. Create a normalized model with tables for each entity. Use snapshots before updates to track historical data.
B. Create a normalized model with tables for each entity. Keep all input files in a Cloud Storage bucket to track historical data.
C. Create a denormalized model with nested and repeated fields. Update the table and use snapshots to track historical data.
D. Create a denormalized, append-only model with nested and repeated fields. Use the ingestion timestamp to track historical data.
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Correct Answer: D
Explanation: A denormalized, append-only model with nested and repeated fields aligns with BigQuery best practices by minimizing joins and simplifying analytics. Appending new versions instead of updating rows preserves full history without snapshot management, and an ingestion timestamp (or version timestamp) enables both current and historical reporting efficiently. Snapshots add operational complexity and storage overhead for regularly changing data, while storing raw files alone does not provide a usable historical analytical model.
Question 89
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?
A. Set a retention policy. Lock the retention policy.
B. Set a retention policy. Set the default storage class to Archive for long-term digital preservation.
C. Enable the Object Versioning feature. Add a lifecycle rule.
D. Enable the Object Versioning feature. Create a copy in a bucket in a different region.
Show Answer
Correct Answer: A
Explanation: A bucket retention policy prevents objects from being deleted or replaced until the retention period expires. Locking the retention policy (Bucket Lock) makes the policy immutable, ensuring the retention settings cannot be reduced or removed, which is appropriate for legal hold/compliance requirements. Archive storage class affects cost, not immutability, and Object Versioning alone does not prevent deletion or modification.
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