Data Fusion for ETL in GCP - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ service_behavior

intermediate

How does Data Fusion handle schema changes during ETL?

In Google Cloud Data Fusion, when an ETL pipeline reads from a source and the source schema changes (e.g., a new column is added), what is the default behavior of the pipeline if no schema update is applied?

A The pipeline fails with a schema mismatch error and stops processing.

B The pipeline automatically updates the schema and processes the new column without errors.

C The pipeline ignores the new column and continues processing with the old schema.

D The pipeline duplicates the data for the new column causing data inconsistency.

💡 Hint

Think about how strict the schema enforcement is by default in Data Fusion pipelines.

By default, Data Fusion pipelines use the schema defined at design time. If the source schema changes, the pipeline ignores new columns unless the schema is updated manually or through schema registry integration.

❓ Architecture

intermediate

Choosing the right Data Fusion edition for complex ETL

You need to build an ETL pipeline in Google Cloud Data Fusion that requires custom plugins and advanced transformations. Which Data Fusion edition should you choose?

A Basic edition, because it supports all plugin types and transformations.

B Standard edition, because it supports only built-in plugins.

C Trial edition, because it has no limits on plugin usage.

D Enterprise edition, because it supports custom plugins and advanced transformations.

💡 Hint

Consider which edition supports extensibility and advanced features.

The Enterprise edition of Data Fusion supports custom plugins and advanced transformations, making it suitable for complex ETL scenarios.

❓ security

advanced

Securing sensitive data in Data Fusion pipelines

You have an ETL pipeline in Data Fusion that processes sensitive customer data. Which approach best protects this data during pipeline execution?

A Use Cloud KMS to encrypt data at rest and configure Data Fusion to use service accounts with least privilege.

B Store sensitive data in plain text and rely on network security only.

C Disable audit logs to prevent data exposure in logs.

D Use public service accounts with broad permissions for easy access.

💡 Hint

Think about encryption and access control best practices.

Encrypting data at rest with Cloud KMS and using least privilege service accounts ensures sensitive data is protected during processing and storage.

❓ Configuration

advanced

Configuring a Data Fusion pipeline to read from a Cloud Storage bucket

Which configuration correctly sets up a source plugin in Data Fusion to read CSV files from a Google Cloud Storage bucket named my-data-bucket?

A Set the source plugin type to 'BigQuery' and the table to 'my-data-bucket'.

B Set the source plugin type to 'GCS File' and the path to 'gs://my-data-bucket/*.csv'.

C Set the source plugin type to 'Database' and the connection string to 'gs://my-data-bucket'.

D Set the source plugin type to 'Kafka' and the topic to 'my-data-bucket'.

💡 Hint

Consider which plugin reads files from Cloud Storage.

The 'GCS File' source plugin reads files from Cloud Storage buckets using the 'gs://' URI format.

✅ Best Practice

expert

Optimizing Data Fusion pipelines for large-scale ETL workloads

Which practice best improves performance and scalability of a Data Fusion ETL pipeline processing terabytes of data?

A Use batch pipelines with partitioned sources and sinks, and enable pipeline parallelism.

B Use single-threaded pipelines to avoid concurrency issues.

C Store all intermediate data in local temporary files on the Data Fusion instance.

D Disable pipeline retries to reduce execution time.

💡 Hint

Think about how to handle large data volumes efficiently.

Partitioning data sources and sinks and enabling parallelism allows Data Fusion pipelines to process large data sets efficiently and scale horizontally.

Practice

1. What is the main purpose of Google Cloud Data Fusion in ETL processes?

easy

A. To store large amounts of raw data permanently

B. To write complex SQL queries for data analysis

C. To visually build and automate data pipelines without coding

D. To create virtual machines for data processing

Solution

1. Step 1: Understand Data Fusion's role

   Data Fusion is designed to help users build data pipelines visually, making ETL easier without needing to write code.

2. Step 2: Compare options

   Options A, B, and D describe other tools or services, not Data Fusion's main purpose.

3. Final Answer:

   To visually build and automate data pipelines without coding -> Option C

4. Quick Check:

   Data Fusion = Visual ETL pipelines [OK]

Hint: Data Fusion is about visual pipelines, not coding or storage [OK]

Common Mistakes:

• Confusing Data Fusion with BigQuery for SQL queries
• Thinking Data Fusion stores data permanently
• Assuming Data Fusion manages virtual machines

2. Which of the following is the correct way to add a source plugin in a Data Fusion pipeline?

easy

A. Drag the source plugin from the palette and drop it onto the canvas

B. Write a SQL query to define the source

C. Create a virtual machine and install the source plugin manually

D. Use the command line to start the source plugin

Solution

1. Step 1: Recall Data Fusion UI usage

   Data Fusion uses a visual interface where you drag and drop plugins to build pipelines.

2. Step 2: Eliminate incorrect methods

   Options A, C, and D involve coding, manual installs, or command line, which are not how Data Fusion works for adding plugins.

3. Final Answer:

   Drag the source plugin from the palette and drop it onto the canvas -> Option A

4. Quick Check:

   Data Fusion = Drag & drop plugins [OK]

Hint: Data Fusion uses drag-and-drop for plugins, not code [OK]

Common Mistakes:

• Trying to write SQL to add plugins
• Thinking manual VM setup is needed
• Using command line instead of UI

3. Given a Data Fusion pipeline with a source plugin reading from Cloud Storage and a sink plugin writing to BigQuery, what happens when the pipeline runs successfully?

medium

A. The pipeline deletes data in BigQuery

B. Data is moved from Cloud Storage to BigQuery after any transformations

C. The pipeline creates a new Cloud Storage bucket

D. Data is copied from BigQuery to Cloud Storage

Solution

1. Step 1: Understand source and sink roles

   The source plugin reads data from Cloud Storage, and the sink plugin writes data to BigQuery.

2. Step 2: Identify pipeline behavior

   When the pipeline runs, data flows from source to sink, optionally transformed in between.

3. Final Answer:

   Data is moved from Cloud Storage to BigQuery after any transformations -> Option B

4. Quick Check:

   Source to sink = Cloud Storage to BigQuery [OK]

Hint: Source reads, sink writes; data flows forward [OK]

Common Mistakes:

• Confusing source and sink directions
• Thinking pipeline creates buckets automatically
• Assuming pipeline deletes data

4. You created a Data Fusion pipeline but it fails to start. Which of the following is the most likely cause?

medium

A. The pipeline has too many source plugins

B. The Cloud Storage bucket is empty

C. You did not write any SQL queries in the pipeline

D. The sink plugin is missing or not configured properly

Solution

1. Step 1: Check pipeline requirements

   Every Data Fusion pipeline needs a properly configured sink plugin to write data.

2. Step 2: Analyze failure reasons

   If the sink is missing or misconfigured, the pipeline cannot complete and will fail to start.

3. Final Answer:

   The sink plugin is missing or not configured properly -> Option D

4. Quick Check:

   Missing sink = pipeline start failure [OK]

Hint: Always configure sink plugin to start pipeline [OK]

Common Mistakes:

• Assuming empty bucket causes start failure
• Thinking SQL queries are mandatory
• Believing multiple sources cause failure

5. You want to build a Data Fusion pipeline that reads sales data from multiple Cloud Storage folders, cleans the data by removing duplicates, and writes the result to BigQuery daily. Which approach best fits this requirement?

hard

A. Use multiple source plugins for each folder, a Wrangler plugin to clean duplicates, and a BigQuery sink; schedule pipeline daily

B. Write a custom script outside Data Fusion to clean data, then use Data Fusion to load into BigQuery

C. Use a single source plugin pointing to one folder and manually merge data later

D. Create a pipeline with only a BigQuery source and sink without cleaning

Solution

1. Step 1: Identify data sources and cleaning needs

   Multiple folders require multiple source plugins or a pattern that reads all folders; cleaning duplicates needs a Wrangler or transform plugin.

2. Step 2: Design pipeline and scheduling

   Connect sources to cleaning plugin, then to BigQuery sink; schedule pipeline to run daily for automation.

3. Final Answer:

   Use multiple source plugins for each folder, a Wrangler plugin to clean duplicates, and a BigQuery sink; schedule pipeline daily -> Option A

4. Quick Check:

   Multiple sources + cleaning + sink + schedule = Use multiple source plugins for each folder, a Wrangler plugin to clean duplicates, and a BigQuery sink; schedule pipeline daily [OK]

Hint: Combine sources, cleaning plugin, sink, and schedule for ETL [OK]

Common Mistakes:

• Skipping cleaning step inside Data Fusion
• Using external scripts unnecessarily
• Ignoring scheduling for daily runs