Professional Data Engineer Free Practice Questions — Page 3
Question 20
You need to orchestrate a pipeline with several Google Cloud services: a batch Dataflow job, then a BigQuery query job followed by a Vertex AI batch prediction. The logic is sequential. You want a lightweight, serverless orchestration solution with minimal operational overhead. What service should you use?
A. Select Cloud Composer.
B. Select Compute Engine with cron.
C. Select Dataproc with Apache Oozie.
D. Select Cloud Workflows.
Show Answer
Correct Answer: D
Explanation: Cloud Workflows is the best fit for lightweight, serverless orchestration of sequential Google Cloud service calls such as launching a Dataflow batch job, waiting for completion, running a BigQuery job, and then invoking a Vertex AI batch prediction. It is designed for workflow orchestration with minimal operational overhead. Cloud Composer (Airflow) is more powerful but heavier to operate, while Compute Engine with cron and Dataproc/Oozie require unnecessary infrastructure or are not appropriate for this use case.
Question 21
Your company uses Looker Studio connected to BigQuery for reporting. Users are experiencing slow dashboard load times due to complex queries on a large table. The queries involve aggregations and filtering on several columns. You need to optimize query performance to decrease the dashboard load times. What should you do?
A. Configure Looker Studio to use a shorter data refresh interval to ensure fresh data is always displayed.
B. Create a materialized view in BigQuery that pre-calculates the aggregations and filters used in the Looker Studio dashboards.
C. Implement row-level security in BigQuery to restrict data access and reduce the amount of data processed by the queries.
D. Use BigQuery BI Engine to accelerate query performance by caching frequently accessed data.
Show Answer
Correct Answer: D
Explanation: BigQuery BI Engine is specifically designed to accelerate interactive analytics for BI tools such as Looker Studio by using an in-memory cache and optimized execution, reducing dashboard latency without changing report logic. Materialized views can help if queries exactly match the precomputed aggregations, but for Looker Studio dashboards the recommended acceleration feature is BI Engine.
Question 22
Your data science team needs to perform interactive SQL queries on large datasets stored in Apache Parquet format within a Cloud Storage bucket. The team is familiar with Apache Hive and wants to leverage existing HiveQL queries. You need to provide an environment for the team to run their interactive HiveQL queries directly against the data in Cloud Storage. You want to keep operational overhead to a minimum. What should you do?
A. Install and configure an Apache Hadoop and Hive cluster manually on a group of Compute Engine instances.
B. Load the Parquet data into a BigQuery native table and use the BigQuery Connector for Hive to run the queries.
C. Configure BigQuery with an external table definition pointing to the Parquet files.
D. Deploy a Dataproc cluster with Hive services enabled.
Show Answer
Correct Answer: D
Explanation: The requirement is to run interactive HiveQL queries directly against Parquet data stored in Cloud Storage while minimizing operational overhead. Dataproc is a managed Hadoop/Spark service that supports Hive out of the box, and Hive can query Parquet data stored in Cloud Storage. Manually managing Compute Engine clusters has higher overhead. BigQuery external tables support SQL, not interactive HiveQL execution, and loading data into BigQuery does not satisfy the requirement to leverage existing HiveQL directly.
Question 23
You have a data analyst team member who needs to analyze data by using BigQuery. The data analyst wants to create a data pipeline that would load 200 CSV files with an average size of 15MB from a Cloud Storage bucket into BigQuery daily. The data needs to be ingested and transformed before being accessed in BigQuery for analysis. You need to recommend a fully managed, no-code solution for the data analyst. What should you do?
A. Create a Cloud Run function and schedule it to run daily using Cloud Scheduler to load the data into BigQuery.
B. Use the BigQuery Data Transfer Service to load files from Cloud Storage to BigQuery, create a BigQuery job which transforms the data using BigQuery SQL and schedule it to run daily.
C. Build a custom Apache Beam pipeline and run it on Dataflow to load the file from Cloud Storage to BigQuery and schedule it to run daily using Cloud Composer.
D. Create a pipeline by using BigQuery pipelines and schedule it to load the data into BigQuery daily.
Show Answer
Correct Answer: D
Explanation: BigQuery Pipelines is the fully managed, serverless solution designed for building ingestion and transformation pipelines within BigQuery with minimal/no code. It can ingest data from Cloud Storage, perform SQL-based transformations, and schedule recurring runs, matching the requirements more directly than combining separate services or writing custom code.
Question 24
You are configuring networking for a Dataflow job. The data pipeline uses custom container images with the libraries that are required for the transformation logic preinstalled. The data pipeline reads the data from Cloud Storage and writes the data to BigQuery. You need to ensure cost-effective and secure communication between the pipeline and Google APIs and services. What should you do?
A. Disable external IP addresses from worker VMs and enable Private Google Access.
B. Leave external IP addresses assigned to worker VMs while enforcing firewall rules.
C. Disable external IP addresses and establish a Private Service Connect endpoint IP address.
D. Enable Cloud NAT to provide outbound internet connectivity while enforcing firewall rules.
Show Answer
Correct Answer: A
Explanation: Because the Dataflow job only needs to access Google APIs (Cloud Storage and BigQuery) and uses custom container images with dependencies already preinstalled, worker VMs do not require general internet access. Disabling external IP addresses improves security and avoids external networking costs, while enabling Private Google Access allows the VMs to privately reach Google APIs such as Cloud Storage and BigQuery. Private Service Connect is unnecessary for this scenario, Cloud NAT is intended for internet egress, and keeping external IPs is less secure.
Question 25
You are administering a BigQuery on-demand environment. Your business intelligence tool is submitting hundreds of queries each day that aggregate a large (50 TB) sales history fact table at the day and month levels. These queries have a slow response time and are exceeding cost expectations. You need to decrease response time, lower query costs, and minimize maintenance. What should you do?
A. Build authorized views on top of the sales table to aggregate data at the day and month level.
B. Enable BI Engine and add your sales table as a preferred table.
C. Build materialized views on top of the sales table to aggregate data at the day and month level.
D. Create a scheduled query to build sales day and sales month aggregate tables on an hourly basis.
Show Answer
Correct Answer: C
Explanation: Materialized views are designed for repeated aggregate queries over large base tables. They precompute and incrementally maintain aggregated results, reducing data scanned, improving query latency, and minimizing maintenance compared with manually scheduled aggregate tables. Authorized views do not improve performance, BI Engine is primarily an in-memory acceleration layer with capacity limitations and is not appropriate for a 50 TB fact table, and scheduled aggregate tables require ongoing maintenance.
Question 26
You are using Workflows to call an API that returns a 1KB JSON response, apply some complex business logic on this response, wait for the logic to complete, and then perform a load from a Cloud Storage file to BigQuery. The Workflows standard library does not have sufficient capabilities to perform your complex logic, and you want to use Python's standard library instead. You want to optimize your workflow for simplicity and speed of execution. What should you do?
A. Create a Cloud Composer environment and run the logic in Cloud Composer.
B. Create a Dataproc cluster, and use PySpark to apply the logic on your JSON file.
C. Invoke a Cloud Function instance that uses Python to apply the logic on your JSON file.
D. Invoke a subworkflow in Workflows to apply the logic on your JSON file.
Show Answer
Correct Answer: C
Explanation: The best choice is to invoke a Cloud Function (or its modern equivalent, Cloud Run functions) from Workflows to execute the complex Python business logic. Workflows is designed to orchestrate services rather than perform complex computation. A Cloud Function provides serverless, low-latency execution for small payloads like a 1 KB JSON response and integrates directly with Workflows. Cloud Composer is unnecessarily complex for this use case, Dataproc is excessive for such a small task, and a subworkflow would still be limited by the Workflows language and standard library, which the question states is insufficient.
Question 27
You are migrating your on-premises data warehouse to BigQuery. As part of the migration, you want to facilitate cross-team collaboration to get the most value out of the organization’s data. You need to design an architecture that would allow teams within the organization to securely publish, discover, and subscribe to read-only data in a self-service manner. You need to minimize costs while also maximizing data freshness. What should you do?
A. Use Analytics Hub to facilitate data sharing.
B. Create authorized datasets to publish shared data in the subscribing team's project.
C. Create a new dataset for sharing in each individual team’s project. Grant the subscribing team the bigquery.dataViewer role on the dataset.
D. Use BigQuery Data Transfer Service to copy datasets to a centralized BigQuery project for sharing.
Show Answer
Correct Answer: A
Explanation: Analytics Hub is purpose-built for secure, self-service data sharing in BigQuery. It enables teams to publish, discover, and subscribe to read-only datasets without copying data, minimizing storage costs while providing access to the freshest underlying data. Authorized datasets and separate shared datasets require more manual administration, and BigQuery Data Transfer Service copies data, increasing costs and reducing freshness.
Question 28
You have several different unstructured data sources, within your on-premises data center as well as in the cloud. The data is in various formats, such as Apache Parquet and CSV. You want to centralize this 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 BigQuery Data Transfer Service to move files into BigQuery.
B. Use Storage Transfer Service to move files into Cloud Storage
C. Use Dataflow to move files into Cloud Storage
D. Use Cloud Data Fusion to move files into Cloud Storage.
Show Answer
Correct Answer: D
Explanation: Cloud Data Fusion is the GUI-based data integration service designed to ingest data from diverse on-premises and cloud sources into Cloud Storage. It supports configuring Cloud Storage sinks and can use customer-managed encryption keys (CMEK). Storage Transfer Service has a GUI but is primarily for transfer workflows and does not satisfy the encryption-key requirement in the way described. Dataflow is code-based rather than GUI-based, and BigQuery Data Transfer Service targets BigQuery rather than Cloud Storage.
Question 29
You want to migrate an Apache Spark 3 batch job from on-premises to Google Cloud. You need to minimally change the job so that the job reads from Cloud Storage and writes the result to BigQuery. Your job is optimized for Spark, where each executor has 8 vCPU and 16 GB memory, and you want to be able to choose similar settings. You want to minimize installation and management effort to run your job. What should you do?
A. Execute the job as part of a deployment in a new Google Kubernetes Engine cluster.
B. Execute the job from a new Compute Engine VM.
C. Execute the job in a new Dataproc cluster.
D. Execute as a Dataproc Serverless job.
Show Answer
Correct Answer: D
Explanation: Dataproc Serverless is the best fit because it minimizes operational overhead by eliminating cluster provisioning and management while still supporting Apache Spark 3 and allowing configuration of Spark executor resources (such as cores and memory) to approximate the existing executor sizing. It integrates with Cloud Storage and the Spark BigQuery connector for reading and writing data with minimal code changes.
Sources:
https://cloud.google.com/dataproc-serverless/docs/guides/bigquery-connector-spark-example
https://cloud.google.com/blog/products/data-analytics/serverless-spark-on-google-cloud-interactive-tutorial
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