A retail company uses Cloud Data Fusion to build pipelines. They have a new requirement to ingest data from a SaaS API that requires OAuth authentication. The API has rate limits. They also need to ensure that the pipeline can handle failures and retries. What should they do?

Use a Dataproc cluster with a Spark job to call the API and write to BigQuery.

Use a Cloud Data Fusion plugin that supports OAuth and configure retries and backoff in the pipeline.

Use BigQuery Data Transfer Service to connect to the API.

Write a custom Cloud Function to call the API and load data to Cloud Storage, then use Data Fusion to process it.

A media company has an on-premises Hadoop cluster running Hive and Pig jobs. They are migrating to Google Cloud and want to minimize changes to their existing processing logic. The data is currently in HDFS and will be moved to Cloud Storage. What is the most cost-effective and least-effort migration path?

Load the data into BigQuery and rewrite the logic as SQL queries.

Rewrite all Hive and Pig jobs as Dataflow pipelines using Apache Beam.

Use Cloud Data Fusion to import the Hive and Pig jobs and run them on Dataproc.

Create a Dataproc cluster and run the existing Hive and Pig jobs directly, pointing them to data in Cloud Storage.

A company needs to import data from a Salesforce instance into BigQuery on a nightly basis. The data includes custom objects and standard objects. They want a solution that is fully managed and requires minimal coding. What should they use?

BigQuery Data Transfer Service for Salesforce.

Cloud Data Fusion with a Salesforce connector.

A Dataproc cluster running a Spark job to extract from Salesforce.

A Dataflow pipeline using the Salesforce IO connector.

A logistics company receives daily shipment files in CSV format. The data often contains duplicate shipment IDs, inconsistent date formats, and missing destination ZIP codes. The team wants to clean this data before loading it into BigQuery for reporting. They need a solution that is easy to maintain and allows non-engineers to visually inspect the transformation logic. What should they use?

A Dataflow pipeline using Apache Beam to apply custom transforms for deduplication, date parsing, and ZIP code lookup.

Cloud Data Fusion with Wrangler to deduplicate, standardize dates, and fill missing ZIP codes from a reference table.

A Dataproc cluster running Spark SQL to perform the cleansing operations and write the results to BigQuery.

BigQuery Data Transfer Service to load the CSV directly and use SQL views to handle duplicates and missing values.

Google CloudProfessional Data Engineer

Domain 2Objective 2

2.2 Building the Pipelines PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 4)

Part of the Ingesting and processing the data domain, which accounts for ~25% of the PROFESSIONAL-DATA-ENGINEER exam. Google Cloud does not publish an official question count, but from its 120-minute exam (~50–80 total, ~13–20 in this domain), expect 4–7 from this objective — we provide 38 practice questions to prepare you well beyond it. (estimate)

38questions here

8free pages

10concepts

~25%of the exam

Questions 16–20

16expert · hard
A retail company uses Cloud Data Fusion to build pipelines. They have a new requirement to ingest data from a SaaS API that requires OAuth authentication. The API has rate limits. They also need to ensure that the pipeline can handle failures and retries. What should they do?
Select an answer first
17application · medium
A media company has an on-premises Hadoop cluster running Hive and Pig jobs. They are migrating to Google Cloud and want to minimize changes to their existing processing logic. The data is currently in HDFS and will be moved to Cloud Storage. What is the most cost-effective and least-effort migration path?
Select an answer first
18application · medium
A company needs to import data from a Salesforce instance into BigQuery on a nightly basis. The data includes custom objects and standard objects. They want a solution that is fully managed and requires minimal coding. What should they use?
Select an answer first
19application · medium
A logistics company receives daily shipment files in CSV format. The data often contains duplicate shipment IDs, inconsistent date formats, and missing destination ZIP codes. The team wants to clean this data before loading it into BigQuery for reporting. They need a solution that is easy to maintain and allows non-engineers to visually inspect the transformation logic. What should they use?
Select an answer first
20foundation · easy
A data engineer wants to perform complex transformations on large datasets using SQL. Which Google Cloud service is best suited for this?
Select an answer first

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2.2 Building the Pipelines PROFESSIONAL-DATA-ENGINEER Practice Questions (Page 4)

Questions 16–20

A retail company uses Cloud Data Fusion to build pipelines. They have a new requirement to ingest data from a SaaS API that requires OAuth authentication. The API has rate limits. They also need to ensure that the pipeline can handle failures and retries. What should they do?

A company needs to import data from a Salesforce instance into BigQuery on a nightly basis. The data includes custom objects and standard objects. They want a solution that is fully managed and requires minimal coding. What should they use?

A data engineer wants to perform complex transformations on large datasets using SQL. Which Google Cloud service is best suited for this?

Keep studying — Ingesting and processing the data