NEWEST GOOGLE PROFESSIONAL
DATA ENGINEER EXAM | Q&A WITH
RATIONALES
1. You are designing a stateful data processing
pipeline that reads data from a Cloud Storage
bucket and writes transformed data to a
BigQuery table. The pipeline must be highly
available and resilient to zonal failures within
the `us-central1` region. You need to configure
a Dataflow pipeline ensuring minimal disruption
during a zonal outage. What should you do?
A) Launch the Dataflow job with the `--
region=us-central1` parameter.
B) Deploy the Dataflow job to a single zone
within `us-central1` and configure it to use a
regional persistent disk to store its state.
C) Deploy the Dataflow job to a single zone
within `us-central1` and use a multi-regional
Cloud Storage bucket to store its state.
D) Launch the Dataflow job with the `--zone=us-
central1-a` parameter.
,Correct answer: A
Rationale: To ensure high availability and
resilience against zonal failures, you should
deploy the Dataflow job regionally using the `--
region=us-central1` parameter. This allows
Dataflow to distribute the job's resources
across multiple zones within the region,
preventing a single zone failure from disrupting
the entire pipeline .
2. You are using Dataflow to build a streaming
data pipeline to analyze user website click
activity from Pub/Sub. You need to calculate the
number of clicks for each user site visit. A site
visit is defined as a period of activity followed by
30 minutes of inactivity for a specific user. What
should you do?
A) Use tumbling windows with a 30-minute
window.
B) Use hopping windows with a 30-minute
window, and a 1-minute period.
C) Use hopping windows with a 30-minute
window, and a 30-minute period.
,D) Use session windows with a 30-minute gap
duration.
Correct answer: D
Rationale: Session windows are designed to
group data elements that occur together over a
period of time, separating those groups by a
period of inactivity. The "gap duration" of 30
minutes defines how much inactivity is required
before the current session window is
considered complete, which perfectly matches
the requirement of defining a site visit .
3. A company wants to store large amounts of
unstructured data and run ad-hoc SQL queries
on it without managing infrastructure. Which
Google Cloud service should they use?
A) Cloud Spanner
B) BigQuery
C) Cloud Bigtable
D) Cloud Firestore
Correct answer: B
, Rationale: BigQuery is a serverless, highly
scalable, and cost-effective multi-cloud data
warehouse designed for large-scale data
analytics. It is ideal for running ad-hoc SQL
queries on large amounts of unstructured data
without infrastructure management .
4. You are designing a pipeline that ingests real-
time streaming data and needs to handle both
late-arriving and out-of-order events. Which
service is best suited for this?
A) Cloud Pub/Sub only
B) Cloud Dataflow with windowing
C) Cloud Composer
D) Cloud Dataproc
Correct answer: B
Rationale: Cloud Dataflow is a fully managed
service for stream and batch processing. Its
support for event-time processing, windowing,
and handling late-arriving data makes it the
ideal choice for building resilient streaming
pipelines with complex temporal requirements .
DATA ENGINEER EXAM | Q&A WITH
RATIONALES
1. You are designing a stateful data processing
pipeline that reads data from a Cloud Storage
bucket and writes transformed data to a
BigQuery table. The pipeline must be highly
available and resilient to zonal failures within
the `us-central1` region. You need to configure
a Dataflow pipeline ensuring minimal disruption
during a zonal outage. What should you do?
A) Launch the Dataflow job with the `--
region=us-central1` parameter.
B) Deploy the Dataflow job to a single zone
within `us-central1` and configure it to use a
regional persistent disk to store its state.
C) Deploy the Dataflow job to a single zone
within `us-central1` and use a multi-regional
Cloud Storage bucket to store its state.
D) Launch the Dataflow job with the `--zone=us-
central1-a` parameter.
,Correct answer: A
Rationale: To ensure high availability and
resilience against zonal failures, you should
deploy the Dataflow job regionally using the `--
region=us-central1` parameter. This allows
Dataflow to distribute the job's resources
across multiple zones within the region,
preventing a single zone failure from disrupting
the entire pipeline .
2. You are using Dataflow to build a streaming
data pipeline to analyze user website click
activity from Pub/Sub. You need to calculate the
number of clicks for each user site visit. A site
visit is defined as a period of activity followed by
30 minutes of inactivity for a specific user. What
should you do?
A) Use tumbling windows with a 30-minute
window.
B) Use hopping windows with a 30-minute
window, and a 1-minute period.
C) Use hopping windows with a 30-minute
window, and a 30-minute period.
,D) Use session windows with a 30-minute gap
duration.
Correct answer: D
Rationale: Session windows are designed to
group data elements that occur together over a
period of time, separating those groups by a
period of inactivity. The "gap duration" of 30
minutes defines how much inactivity is required
before the current session window is
considered complete, which perfectly matches
the requirement of defining a site visit .
3. A company wants to store large amounts of
unstructured data and run ad-hoc SQL queries
on it without managing infrastructure. Which
Google Cloud service should they use?
A) Cloud Spanner
B) BigQuery
C) Cloud Bigtable
D) Cloud Firestore
Correct answer: B
, Rationale: BigQuery is a serverless, highly
scalable, and cost-effective multi-cloud data
warehouse designed for large-scale data
analytics. It is ideal for running ad-hoc SQL
queries on large amounts of unstructured data
without infrastructure management .
4. You are designing a pipeline that ingests real-
time streaming data and needs to handle both
late-arriving and out-of-order events. Which
service is best suited for this?
A) Cloud Pub/Sub only
B) Cloud Dataflow with windowing
C) Cloud Composer
D) Cloud Dataproc
Correct answer: B
Rationale: Cloud Dataflow is a fully managed
service for stream and batch processing. Its
support for event-time processing, windowing,
and handling late-arriving data makes it the
ideal choice for building resilient streaming
pipelines with complex temporal requirements .
