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Ensuring solution quality practice questions

Practise Google Professional Data Engineer Ensuring solution quality practice questions — original exam-style scenarios with answer choices, explanations, and analysis of common mistakes.

Courseiva uses original exam-style practice questions designed for learning and revision. The goal is to understand the concepts, recognise exam patterns, and improve through explanations — not memorise copied exam dumps.

Reviewed byJohnson Ajibi· MSc IT Security

20 questionsDomain: Ensuring solution quality

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What the exam tests

What to know about Ensuring solution quality

Ensuring solution quality questions test whether you can apply the concept in context, not just recognise a definition.

How the topic appears in realistic exam-style scenarios.

Which detail in the question changes the correct answer.

How to eliminate plausible but wrong options.

How to connect the question back to the wider exam objective.

Watch out for

Common Ensuring solution quality exam traps

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Practice set

Ensuring solution quality questions

20 questions · select your answer, then reveal the explanation

Question 1easymultiple choice

Read the full Ensuring solution quality explanation →

A data pipeline ingests streaming data from Pub/Sub into BigQuery via Dataflow. Recently, the pipeline has been failing with 'deadline exceeded' errors. What is the most likely cause?

Trap 1: The BigQuery streaming quota is exceeded.

Quota exceeded errors would be 'quota exceeded', not 'deadline exceeded'.

Trap 2: Dataflow workers are underutilized due to batch size settings.

Underutilization would not cause deadline exceeded errors; it might cause slow processing.

Trap 3: Dataflow autoscaling is disabled.

Autoscaling being disabled might cause performance issues but not specifically deadline exceeded errors.

Study all Ensuring solution quality common traps →

A
The BigQuery streaming quota is exceeded.
Why wrong: Quota exceeded errors would be 'quota exceeded', not 'deadline exceeded'.
B
Dataflow workers are underutilized due to batch size settings.
Why wrong: Underutilization would not cause deadline exceeded errors; it might cause slow processing.
C
Dataflow autoscaling is disabled.
Why wrong: Autoscaling being disabled might cause performance issues but not specifically deadline exceeded errors.
D
The Pub/Sub subscription's acknowledgement deadline is too short for the processing time.
A short acknowledgment deadline causes messages to be redelivered, leading to repeated processing attempts and eventual deadline exceeded errors.

Full breakdown with real-world context →

Question 2mediummultiple choice

Read the full Ensuring solution quality explanation →

A team is designing a data lake on Google Cloud using Cloud Storage and BigQuery. They need to ensure that sensitive data (e.g., PII) is encrypted at rest and have the ability to audit access. Which approach meets these requirements?

Trap 1: Use Customer-Managed Encryption Keys (CMEK) and enable VPC Service…

VPC Service Controls reduce exfiltration risk but do not provide access auditing.

Trap 2: Use Default Encryption and enable Data Loss Prevention (DLP) API.

Default Encryption does not allow customer control over keys.

Trap 3: Use Customer-Supplied Encryption Keys (CSEK) and enable VPC Service…

CSEK requires the customer to supply the key material, which may not be desirable for all scenarios.

Study all Ensuring solution quality common traps →

A
Use Customer-Managed Encryption Keys (CMEK) and enable VPC Service Controls.
Why wrong: VPC Service Controls reduce exfiltration risk but do not provide access auditing.
B
Use Customer-Managed Encryption Keys (CMEK) and enable Cloud Audit Logs.
CMEK provides control over encryption keys, and Cloud Audit Logs record access to data.
C
Use Default Encryption and enable Data Loss Prevention (DLP) API.
Why wrong: Default Encryption does not allow customer control over keys.
D
Use Customer-Supplied Encryption Keys (CSEK) and enable VPC Service Controls.
Why wrong: CSEK requires the customer to supply the key material, which may not be desirable for all scenarios.

Full breakdown with real-world context →

Question 3hardmultiple choice

Read the full Ensuring solution quality explanation →

A company runs a batch processing job on Dataproc that uses Apache Spark to process 500 GB of data daily. The job completes successfully but takes 4 hours. The team wants to reduce the runtime to under 2 hours without increasing cost. What should they do?

Trap 1: Increase the master node's machine type to n2-standard-8.

Master node size has minimal impact on processing speed.

Trap 2: Increase the machine type of worker nodes to n2-highmem-8.

Higher CPU machines cost more, potentially increasing cost.

Trap 3: Migrate the job to Dataflow with autoscaling enabled.

Migration may require code changes and Dataflow costs may not be lower.

Study all Ensuring solution quality common traps →

A
Use preemptible VMs for worker nodes and increase the number of workers.
Preemptible VMs are cheaper, allowing more workers for the same cost, reducing runtime.
B
Increase the master node's machine type to n2-standard-8.
Why wrong: Master node size has minimal impact on processing speed.
C
Increase the machine type of worker nodes to n2-highmem-8.
Why wrong: Higher CPU machines cost more, potentially increasing cost.
D
Migrate the job to Dataflow with autoscaling enabled.
Why wrong: Migration may require code changes and Dataflow costs may not be lower.

Full breakdown with real-world context →

Question 4mediummulti select

Read the full Ensuring solution quality explanation →

Which TWO actions are recommended to improve the reliability of a Cloud Dataflow streaming pipeline that processes event data from Pub/Sub?

Trap 1: Use a pull subscription with a 10-second acknowledgment deadline.

Short ack deadlines can cause duplicates and processing failures.

Trap 2: Disable autoscaling to prevent worker churn.

Autoscaling helps handle load spikes; disabling it can cause failures under load.

Trap 3: Use micro-batch processing with a small batch size.

Small batches increase overhead and may not improve reliability.

Study all Ensuring solution quality common traps →

A
Use a pull subscription with a 10-second acknowledgment deadline.
Why wrong: Short ack deadlines can cause duplicates and processing failures.
B
Enable Dataflow Streaming Engine.
Streaming Engine offloads state management to the backend, improving reliability.
C
Enable exactly-once processing sinks (e.g., BigQuery with guaranteed row-level insertion).
Exactly-once processing prevents duplicate data.
D
Disable autoscaling to prevent worker churn.
Why wrong: Autoscaling helps handle load spikes; disabling it can cause failures under load.
E
Use micro-batch processing with a small batch size.
Why wrong: Small batches increase overhead and may not improve reliability.

Full breakdown with real-world context →

Question 5hardmultiple choice

Read the full Ensuring solution quality explanation →

A data analyst runs a complex SQL query in BigQuery that joins multiple large tables and receives the above error. Which action is most likely to resolve the issue?

Exhibit

Refer to the exhibit.

BigQuery job error log:
{
  "jobId": "job_12345",
  "errorResult": {
    "reason": "resourcesExceeded",
    "message": "Resources exceeded during query execution: The query could not be executed in the allocated memory. High memory usage caused the query to fail."
  },
  "statistics": {
    "query": {
      "statementType": "SELECT",
      "totalSlotMs": 12000000,
      "totalBytesProcessed": 5000000000000
    }
  }
}

Trap 1: Use a larger number of workers in the query execution.

BigQuery does not expose worker count; it manages resources internally.

Trap 2: Use smaller tables by sampling data.

Sampling would reduce accuracy and may not be acceptable.

Trap 3: Add clustering on join columns.

Clustering can help but may not resolve memory exhaustion if the query is complex.

Study all Ensuring solution quality common traps →

A
Use a larger number of workers in the query execution.
Why wrong: BigQuery does not expose worker count; it manages resources internally.
B
Use smaller tables by sampling data.
Why wrong: Sampling would reduce accuracy and may not be acceptable.
C
Add clustering on join columns.
Why wrong: Clustering can help but may not resolve memory exhaustion if the query is complex.
D
Increase the number of slots allocated to the project.
More slots provide more memory and CPU, reducing resource exceeded errors.

Full breakdown with real-world context →

Question 6mediummultiple choice

Read the full Ensuring solution quality explanation →

A company runs a real-time anomaly detection system on Google Cloud. Streaming data from IoT devices is ingested via Pub/Sub, processed by Dataflow (Apache Beam), and results are written to Bigtable for low-latency serving. Recently, the system has been experiencing increased latency and occasional data loss. The Dataflow pipeline shows high system lag and backlog in Pub/Sub. The Bigtable cluster has 3 nodes and is reporting high CPU utilization (over 90%). The team suspects the issue is with the pipeline configuration. They have already verified that there are no errors in the pipeline code and no network issues. Which action should they take to resolve the issue?

Trap 1: Change the Dataflow worker machine type to n2-standard-8.

Faster workers would also increase write throughput, not solving the bottleneck.

Trap 2: Decrease the batch size in the Dataflow pipeline to reduce latency.

Smaller batches increase the number of write requests, putting more load on Bigtable.

Trap 3: Increase the number of Dataflow workers to process messages faster.

More workers would write more data to Bigtable, potentially worsening the bottleneck.

Study all Ensuring solution quality common traps →

A
Increase the number of Bigtable nodes to handle the write throughput.
High CPU utilization suggests Bigtable is overwhelmed; adding nodes increases capacity.
B
Change the Dataflow worker machine type to n2-standard-8.
Why wrong: Faster workers would also increase write throughput, not solving the bottleneck.
C
Decrease the batch size in the Dataflow pipeline to reduce latency.
Why wrong: Smaller batches increase the number of write requests, putting more load on Bigtable.
D
Increase the number of Dataflow workers to process messages faster.
Why wrong: More workers would write more data to Bigtable, potentially worsening the bottleneck.

Full breakdown with real-world context →

Question 7mediumdrag order

Read the full Ensuring solution quality explanation →

Drag and drop the steps to create a Cloud Composer environment for Apache Airflow into the correct order.

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps

Order

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

Question 8mediumdrag order

Read the full Ensuring solution quality explanation →

Drag and drop the steps to migrate an on-premises MySQL database to Cloud SQL using Database Migration Service into the correct order.

Drag steps to the numbered slots on the right, or tap a step then tap a slot.

Steps

Order

1Step 1

2Step 2

3Step 3

4Step 4

5Step 5

Question 9mediummatching

Read the full Ensuring solution quality explanation →

Match each machine learning term to its description.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts

Matches

Model trained on labeled data

Model trained on unlabeled data

Agent learns by interacting with environment

Model performs well on training data but poorly on new data

Question 10mediummatching

Read the full Ensuring solution quality explanation →

Match each data encryption concept to its description.

Drag a concept onto its matching description — or click a concept then click the description.

Concepts

Matches

Customer-supplied encryption key

Customer-managed encryption key via Cloud KMS

CSEK: keys provided by customer; CMEK: keys managed in Cloud KMS

Data encrypted while moving across networks

Question 11easymultiple choice

Read the full Ensuring solution quality explanation →

A data pipeline processes streaming data with Dataflow. The team notices occasional data duplication in BigQuery. What is the best approach to ensure exactly-once processing?

Trap 1: Use Pub/Sub with at-least-once delivery and deduplicate in BigQuery…

At-least-once delivery requires manual deduplication, which is error-prone and not automatically ensured by Dataflow.

Trap 2: Use Cloud Functions to deduplicate messages before they enter the…

Deduplicating before the pipeline adds latency and complexity; it is not a built-in Dataflow feature.

Trap 3: Enable idempotent writes in BigQuery.

BigQuery does not support idempotent writes; deduplication must be handled upstream.

Study all Ensuring solution quality common traps →

A
Use Pub/Sub with at-least-once delivery and deduplicate in BigQuery using a unique identifier.
Why wrong: At-least-once delivery requires manual deduplication, which is error-prone and not automatically ensured by Dataflow.
B
Configure Dataflow with exactly-once sinks using file staging and deduplication.
Dataflow's exactly-once sink mechanism ensures each record is written exactly once, preventing duplicates.
C
Use Cloud Functions to deduplicate messages before they enter the pipeline.
Why wrong: Deduplicating before the pipeline adds latency and complexity; it is not a built-in Dataflow feature.
D
Enable idempotent writes in BigQuery.
Why wrong: BigQuery does not support idempotent writes; deduplication must be handled upstream.

Full breakdown with real-world context →

Question 12mediummultiple choice

Read the full NAT/PAT explanation →

A company is deploying a large-scale streaming application on Google Kubernetes Engine. They need to ensure the application can handle sudden traffic spikes without dropping data. Which architectural pattern is most appropriate?

Trap 1: Implement custom retry logic with exponential backoff in the…

Retry logic only handles transient failures; it does not provide capacity to absorb traffic spikes.

Trap 2: Use Cloud SQL as a temporary buffer and process from there.

Cloud SQL is not designed for high-throughput streaming buffering and can become a bottleneck.

Trap 3: Pre-provision 3x the expected peak capacity to handle spikes.

Pre-provisioning is wasteful and does not dynamically adapt; it can lead to unused resources.

Study all Ensuring solution quality common traps →

A
Implement custom retry logic with exponential backoff in the application.
Why wrong: Retry logic only handles transient failures; it does not provide capacity to absorb traffic spikes.
B
Use Cloud SQL as a temporary buffer and process from there.
Why wrong: Cloud SQL is not designed for high-throughput streaming buffering and can become a bottleneck.
C
Pre-provision 3x the expected peak capacity to handle spikes.
Why wrong: Pre-provisioning is wasteful and does not dynamically adapt; it can lead to unused resources.
D
Use a Pub/Sub topic as a buffer and autoscale consumer pods based on Pub/Sub subscription backlog.
Pub/Sub provides a highly scalable buffer; autoscaling consumers based on backlog ensures capacity matches demand.

Full breakdown with real-world context →

Question 13hardmultiple choice

Read the full Ensuring solution quality explanation →

A data science team uses AI Platform Training with hyperparameter tuning. They observe that some trials fail due to transient errors. To improve solution quality and reduce costs, what should they do?

Trap 1: Enable early stopping using a Bayesian optimization algorithm.

Early stopping prunes unpromising trials, saving time, but does not recover from transient errors.

Trap 2: Use larger machine types for each trial.

Larger machines may reduce failures but increase cost per trial, potentially increasing overall cost.

Trap 3: Increase the number of parallel trials.

More parallel trials may speed up search but does not reduce cost or handle failure recovery.

Study all Ensuring solution quality common traps →

A
Enable early stopping using a Bayesian optimization algorithm.
Why wrong: Early stopping prunes unpromising trials, saving time, but does not recover from transient errors.
B
Set the maxFailedTrials parameter to a high value (e.g., 10).
This allows the tuning job to tolerate transient failures and continue searching without aborting, improving completion rate and model quality.
C
Use larger machine types for each trial.
Why wrong: Larger machines may reduce failures but increase cost per trial, potentially increasing overall cost.
D
Increase the number of parallel trials.
Why wrong: More parallel trials may speed up search but does not reduce cost or handle failure recovery.

Full breakdown with real-world context →

Question 14easymultiple choice

Read the full Ensuring solution quality explanation →

A company runs batch jobs on Dataproc. They need to ensure that if a job fails, it automatically retries with exponential backoff. What is the recommended approach?

Trap 1: Schedule a cron job to check job status and restart manually.

A cron job is manual and not integrated with Dataproc's built-in orchestration.

Trap 2: Use a Cloud Function triggered by Stackdriver alerts to restart the…

This approach requires setting up alerts and custom code, adding complexity and potential delay.

Trap 3: Create a Cloud Composer DAG that monitors job status and retries on…

Cloud Composer is a powerful orchestrator but is unnecessarily complex for a simple retry requirement.

Study all Ensuring solution quality common traps →

A
Schedule a cron job to check job status and restart manually.
Why wrong: A cron job is manual and not integrated with Dataproc's built-in orchestration.
B
Use a Cloud Function triggered by Stackdriver alerts to restart the job.
Why wrong: This approach requires setting up alerts and custom code, adding complexity and potential delay.
C
Use Dataproc Workflow Templates with the maxAttempts parameter set to 3.
Workflow Templates natively support retries with configurable backoff, making it the simplest and most robust solution.
D
Create a Cloud Composer DAG that monitors job status and retries on failure.
Why wrong: Cloud Composer is a powerful orchestrator but is unnecessarily complex for a simple retry requirement.

Full breakdown with real-world context →

Question 15easymultiple choice

Read the full Ensuring solution quality explanation →

A team developed a microservice that writes logs to stdout. They want to centralize logs for analysis. Which GCP service should they use to automatically collect and store logs?

Trap 1: Install the Cloud Logging agent on the VM running the microservice.

The agent is for VM-based logging, not for containers or serverless; also not automatic for stdout.

Trap 2: Publish logs to a Pub/Sub topic and later store them.

Pub/Sub is a messaging layer, not a log storage service; additional components needed.

Trap 3: Write logs directly to Cloud Storage.

Writing to Cloud Storage would require custom code and does not support Logs Explorer.

Study all Ensuring solution quality common traps →

A
Install the Cloud Logging agent on the VM running the microservice.
Why wrong: The agent is for VM-based logging, not for containers or serverless; also not automatic for stdout.
B
Publish logs to a Pub/Sub topic and later store them.
Why wrong: Pub/Sub is a messaging layer, not a log storage service; additional components needed.
C
Write logs directly to Cloud Storage.
Why wrong: Writing to Cloud Storage would require custom code and does not support Logs Explorer.
D
Use the Cloud Logging client library (google-cloud-logging) for the microservice's language.
The client library automatically sends structured logs to Cloud Logging, enabling centralized analysis.

Full breakdown with real-world context →

Question 16mediummultiple choice

Read the full Ensuring solution quality explanation →

A data platform uses Cloud Spanner for transactional data. They are experiencing high latency during write-heavy periods. To maintain solution quality, what configuration change is most effective?

Trap 1: Enable online schema changes.

Online schema changes allow non-blocking DDL but do not address write latency.

Trap 2: Increase the number of nodes in the Cloud Spanner instance.

Adding nodes increases throughput but can increase write latency due to two-phase commits across nodes.

Trap 3: Manually split the table using ALTER TABLE statements.

Manual splitting is not supported and can lead to uneven splits and performance degradation.

Study all Ensuring solution quality common traps →

A
Use interleaved tables to reduce the number of split operations.
Interleaved tables store related rows in the same split, minimizing distributed transaction overhead.
B
Enable online schema changes.
Why wrong: Online schema changes allow non-blocking DDL but do not address write latency.
C
Increase the number of nodes in the Cloud Spanner instance.
Why wrong: Adding nodes increases throughput but can increase write latency due to two-phase commits across nodes.
D
Manually split the table using ALTER TABLE statements.
Why wrong: Manual splitting is not supported and can lead to uneven splits and performance degradation.

Full breakdown with real-world context →

Question 17easymultiple choice

Read the full Ensuring solution quality explanation →

A company uses Cloud Functions to process events from Cloud Storage. They notice that occasionally functions are not triggered. What should they check first to ensure solution quality?

Trap 1: Check the logs for function execution.

Logs are useful after ensuring trigger setup; if the function wasn't triggered, there will be no logs.

Trap 2: Increase the function memory allocation.

Memory affects execution performance, not triggering.

Trap 3: Increase the function timeout.

Timeout affects execution duration, not triggering.

Study all Ensuring solution quality common traps →

A
Verify that the Cloud Storage bucket has notifications configured for the correct event type.
A misconfigured notification will prevent the function from being triggered at all.
B
Check the logs for function execution.
Why wrong: Logs are useful after ensuring trigger setup; if the function wasn't triggered, there will be no logs.
C
Increase the function memory allocation.
Why wrong: Memory affects execution performance, not triggering.
D
Increase the function timeout.
Why wrong: Timeout affects execution duration, not triggering.

Full breakdown with real-world context →

Question 18hardmultiple choice

Read the full Ensuring solution quality explanation →

A financial services company uses Dataflow pipelines with late data handling. They need to ensure that all late-arriving data is processed correctly but also want to control costs. What is the best configuration?

Trap 1: Use a global window with a very long allowed lateness (e.g., 7…

Global windows retain state for all late data, leading to high state storage costs.

Trap 2: Use session windows with a gap duration of 1 hour and allowed…

Session windows can merge unrelated events if gap is too large, and still incur high state cost.

Trap 3: Use sliding windows with a short allowed lateness (e.g., 10…

Sliding windows with short lateness may drop late data, and side inputs add complexity.

Study all Ensuring solution quality common traps →

A
Use a global window with a very long allowed lateness (e.g., 7 days).
Why wrong: Global windows retain state for all late data, leading to high state storage costs.
B
Use session windows with a gap duration of 1 hour and allowed lateness of 2 days.
Why wrong: Session windows can merge unrelated events if gap is too large, and still incur high state cost.
C
Use sliding windows with a short allowed lateness (e.g., 10 minutes) and a side input containing historical data.
Why wrong: Sliding windows with short lateness may drop late data, and side inputs add complexity.
D
Use fixed windows with allowed lateness set to the maximum expected delay (e.g., 2 days) and a trivial watermark.
Fixed windows with a realistic allowed lateness capture late data without excessive state cost, and a trivial watermark ensures no data is dropped.

Full breakdown with real-world context →

Question 19easymultiple choice

Read the full Ensuring solution quality explanation →

A team is deploying a model on AI Platform Prediction. They want to monitor for data drift to maintain model quality. Which service should they use?

Trap 1: Cloud DLP

Cloud DLP is used for inspecting and classifying sensitive data.

Trap 2: Cloud Monitoring

Cloud Monitoring tracks system metrics, not model-specific drift.

Trap 3: Cloud Audit Logs

Audit logs record API calls, not model quality metrics.

Study all Ensuring solution quality common traps →

A
Cloud DLP
Why wrong: Cloud DLP is used for inspecting and classifying sensitive data.
B
AI Platform Continuous Evaluation
This service provides monitoring for model predictions and drift analysis.
C
Cloud Monitoring
Why wrong: Cloud Monitoring tracks system metrics, not model-specific drift.
D
Cloud Audit Logs
Why wrong: Audit logs record API calls, not model quality metrics.

Full breakdown with real-world context →

Question 20mediummulti select

Read the full Ensuring solution quality explanation →

A data engineer needs to monitor the performance of BigQuery queries to identify opportunities for optimization. Which TWO metrics should they focus on? (Choose two.)

Trap 1: Query execution time

While execution time is important, it is often a result of other factors; slot usage and data scanned are more direct optimization targets.

Trap 2: Number of tables joined

Number of joins is not a monitored metric and does not directly indicate performance issues.

Trap 3: Number of users

Number of users is not a performance metric for individual queries.

Study all Ensuring solution quality common traps →

A
Slot usage
Monitoring slot usage helps identify query resource consumption and opportunities to optimize.
B
Data scanned per query
Reducing data scanned is the most effective way to lower cost and improve performance.
C
Query execution time
Why wrong: While execution time is important, it is often a result of other factors; slot usage and data scanned are more direct optimization targets.
D
Number of tables joined
Why wrong: Number of joins is not a monitored metric and does not directly indicate performance issues.
E
Number of users
Why wrong: Number of users is not a performance metric for individual queries.

Full breakdown with real-world context →

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What does the PDE exam test about Ensuring solution quality?: Ensuring solution quality questions test whether you can apply the concept in context, not just recognise a definition.
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Can I practise just Ensuring solution quality questions in a focused session?: Yes — the session launcher on this page draws every question from the Ensuring solution quality domain. Use a 10-question session first to gauge your baseline, then move to 20 or 30 once the weak spots are clear.
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All PDE questions Ensuring solution quality domain overview PDE exam guide

Exam traps to avoid

▸Answering from memory before reading the full scenario.
▸Missing a constraint such as cost, availability, security, scope or command context.
▸Choosing a broad answer when the question asks for the most specific fix.
▸Ignoring why the wrong options are tempting.

Ensuring solution quality practice questions

What to know about Ensuring solution quality

Common Ensuring solution quality exam traps

Ensuring solution quality questions

A data pipeline ingests streaming data from Pub/Sub into BigQuery via Dataflow. Recently, the pipeline has been failing with 'deadline exceeded' errors. What is the most likely cause?

A team is designing a data lake on Google Cloud using Cloud Storage and BigQuery. They need to ensure that sensitive data (e.g., PII) is encrypted at rest and have the ability to audit access. Which approach meets these requirements?

A company runs a batch processing job on Dataproc that uses Apache Spark to process 500 GB of data daily. The job completes successfully but takes 4 hours. The team wants to reduce the runtime to under 2 hours without increasing cost. What should they do?

Which TWO actions are recommended to improve the reliability of a Cloud Dataflow streaming pipeline that processes event data from Pub/Sub?

A data analyst runs a complex SQL query in BigQuery that joins multiple large tables and receives the above error. Which action is most likely to resolve the issue?

Exhibit

Drag and drop the steps to create a Cloud Composer environment for Apache Airflow into the correct order.

Drag and drop the steps to migrate an on-premises MySQL database to Cloud SQL using Database Migration Service into the correct order.

Match each machine learning term to its description.

Match each data encryption concept to its description.

A data pipeline processes streaming data with Dataflow. The team notices occasional data duplication in BigQuery. What is the best approach to ensure exactly-once processing?

A company is deploying a large-scale streaming application on Google Kubernetes Engine. They need to ensure the application can handle sudden traffic spikes without dropping data. Which architectural pattern is most appropriate?

A data science team uses AI Platform Training with hyperparameter tuning. They observe that some trials fail due to transient errors. To improve solution quality and reduce costs, what should they do?

A company runs batch jobs on Dataproc. They need to ensure that if a job fails, it automatically retries with exponential backoff. What is the recommended approach?

A team developed a microservice that writes logs to stdout. They want to centralize logs for analysis. Which GCP service should they use to automatically collect and store logs?

A data platform uses Cloud Spanner for transactional data. They are experiencing high latency during write-heavy periods. To maintain solution quality, what configuration change is most effective?

A company uses Cloud Functions to process events from Cloud Storage. They notice that occasionally functions are not triggered. What should they check first to ensure solution quality?

A financial services company uses Dataflow pipelines with late data handling. They need to ensure that all late-arriving data is processed correctly but also want to control costs. What is the best configuration?

A team is deploying a model on AI Platform Prediction. They want to monitor for data drift to maintain model quality. Which service should they use?

A data engineer needs to monitor the performance of BigQuery queries to identify opportunities for optimization. Which TWO metrics should they focus on? (Choose two.)

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