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This page has 12 Refer to the Exhibit Practice Questions scenario questions for the PCDE exam, each with detailed explanations and wrong-answer analysis.

How should I approach PCDE scenario questions?

Read the full scenario before looking at the answer options. Identify the constraint or requirement in the scenario, then eliminate options that are generally true but wrong for this specific case. Scenario questions reward careful reading over pattern matching.

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Refer to the Exhibit Practice Questions

Practise Google Professional Cloud Database Engineer practice questions — original exam-style scenarios covering every exam domain, with detailed explanations, wrong-answer analysis, and common exam traps.

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How to approach refer to the exhibit practice questions

Practise exhibit-style questions that ask you to read a topology, table, command output or diagram before choosing the best answer.

Quick answer

Exhibit-style questions test whether you can read a topology, command output, diagram or table before choosing the best answer.

How to extract the relevant detail from an exhibit.

How topology, command output or routing information affects the answer.

How to avoid answering from memory before reading the evidence.

How to map the exhibit back to the exam objective.

Practice scenarios

Question 1hardmultiple choice

Full question →

Refer to the exhibit. This DDL is used to create a table in Cloud Spanner. The table will be used for storing user data with high write throughput. What is one performance issue with this table design?

Exhibit

CREATE TABLE Users (
  UserId INT64 NOT NULL,
  Name STRING(MAX),
) PRIMARY KEY (UserId);

A
The primary key is a monotonically increasing integer
Sequential keys cause write hotspots in Spanner, leading to uneven load.
B
The Name column is of type STRING(MAX)
Why wrong: STRING(MAX) is valid and does not inherently cause performance issues.
C
There are no secondary indexes
Why wrong: Lack of indexes affects reads but not write throughput directly.
D
The table is not interleaved with another table
Why wrong: Interleaving is optional and not related to this performance issue.

Full breakdown with real-world context →

Question 2mediummultiple choice

Full question →

The exhibit shows query metadata for a query that scans 10 GB. Given the table is 100 GB and partitioned by hire_date, why did the query scan 10 GB and not less?

Exhibit

Refer to the exhibit.

```sql
-- BigQuery query results metadata
Query statement: SELECT department, COUNT(*) as cnt
FROM `project.dataset.employees`
WHERE hire_date >= '2023-01-01'
GROUP BY department
ORDER BY cnt DESC

Query plan:
- Stage 1: Input (scan) - 10 GB processed
- Stage 2: Aggregate - 5 GB processed
- Stage 3: Sort - 0 GB processed

Table details:
- Table size: 100 GB
- Partitioned by: hire_date (daily)
- Clustered by: department
```

A
The filter on hire_date is not selective enough to prune most partitions
Correct. The filter on hire_date is not selective enough to prune most partitions, so 10 GB are scanned.
B
Clustering on department is not being used because the query has ORDER BY
Why wrong: Incorrect. Clustering on department can help with data skipped within a partition but does not affect partition pruning. ORDER BY does not prevent clustering from being used.
C
The query uses GROUP BY, which forces a full table scan
Why wrong: Incorrect. GROUP BY does not force a full table scan in BigQuery; partition pruning still applies based on the WHERE clause.
D
The table is not clustered properly
Why wrong: Incorrect. Clustering is not relevant here; the issue is partition pruning, not clustering. The table may be properly clustered.

Full breakdown with real-world context →

Question 3mediummultiple choice

Full question →

Refer to the exhibit. You receive an alert from this policy for a Cloud Spanner instance. Which action should you take first?

Exhibit

Refer to the exhibit.

```
# Cloud Monitoring alert policy configuration
condition:
  conditionThreshold:
    filter: metric.type="spanner.googleapis.com/instance/node_cpu_utilization"
           resource.type="spanner_instance"
           resource.label.instance_id="my-spanner-instance"
    aggregations:
    - alignmentPeriod: 60s
      perSeriesAligner: ALIGN_MEAN
    - crossSeriesReducer: REDUCE_MEAN
    duration: 300s
    comparison: COMPARISON_GT
    thresholdValue: 0.7
```

A
Identify and remove unused indexes
Why wrong: May reduce overhead, but not immediate.
B
Add more nodes to the instance
Directly reduces per-node CPU utilization.
C
Review the top queries by CPU usage in the Spanner console
Why wrong: Useful but not the first action; the alert indicates resource saturation.
D
Split large tables into smaller ones
Why wrong: Schema optimization does not increase computational capacity.

Full breakdown with real-world context →

Question 4easymultiple choice

Full question →

Refer to the exhibit. A company wants to perform point-in-time recovery (PITR) for their Cloud SQL MySQL instance. Is PITR enabled?

Exhibit

gcloud sql instances describe my-instance --format='json' | jq '.settings.backupConfiguration'
{
  "binaryLogEnabled": true,
  "enabled": true,
  "startTime": "03:00",
  "transactionLogRetentionDays": 7
}

A
No, because PITR requires the backupConfiguration to have 'pointInTimeRecoveryEnabled: true'.
Why wrong: Cloud SQL does not use a pointInTimeRecoveryEnabled flag; PITR is implied by binaryLogEnabled.
B
Yes, because binaryLogEnabled is true.
Binary logs are used for MySQL PITR; their presence indicates PITR is enabled.
C
No, because transactionLogRetentionDays is set to 7.
Why wrong: transactionLogRetentionDays controls how long binary logs are retained, but PITR still works; the value does not disable it.
D
Yes, because enabled is true.
Why wrong: The 'enabled' field refers to automatic backups, not PITR; PITR requires binary logging.

Full breakdown with real-world context →

Question 5mediummultiple choice

Full question →

A company notices that their Cloud SQL for PostgreSQL instance, as shown in the exhibit, frequently runs out of storage, causing downtime. They have set up automated backups with point-in-time recovery. What is the most likely cause of the storage issue?

Exhibit

Refer to the exhibit.

gcloud sql instances describe my-instance
...
databaseVersion: POSTGRES_13
settings:
  activationPolicy: ALWAYS
  availabilityType: ZONAL
  backupConfiguration:
    enabled: true
    pointInTimeRecoveryEnabled: true
    startTime: '03:00'
    transactionLogRetentionDays: '7'
  dataDiskSizeGb: '100'
  dataDiskType: PD_SSD
  tier: db-custom-4-15360
...

A
Transaction logs for point-in-time recovery are consuming disk space.
Transaction logs are stored on the same disk and can grow large.
B
The activation policy is set to ALWAYS, causing continuous writes.
Why wrong: Activation policy does not affect storage usage.
C
The instance tier (db-custom-4-15360) is too low for the workload.
Why wrong: Tier affects CPU and memory, not storage.
D
The data disk type (PD_SSD) is not suitable for PostgreSQL.
Why wrong: PD_SSD is appropriate.

Full breakdown with real-world context →

Question 6hardmultiple choice

Full question →

A team is reviewing IAM permissions on a Cloud Storage bucket. The exhibit shows the bucket's IAM policy. A developer is using the service account sa-1 and reports that they cannot delete objects in the bucket. What is the likely reason?

Exhibit

Refer to the exhibit.

{
  "bindings": [
    {
      "role": "roles/storage.objectViewer",
      "members": [
        "serviceAccount:sa-1@project.iam.gserviceaccount.com",
        "user:admin@example.com"
      ]
    },
    {
      "role": "roles/storage.objectAdmin",
      "members": [
        "serviceAccount:sa-2@project.iam.gserviceaccount.com"
      ]
    }
  ],
  "etag": "BwXQ=="
}

A
The etag value must be updated before any delete operation.
Why wrong: Etag is for condition updates, not for authorization.
B
The service account sa-1 does not have the storage.objects.delete permission.
objectViewer only grants read access.
C
A condition is attached to the objectViewer role that prevents deletion.
Why wrong: No condition is shown in the exhibit.
D
The policy only allows deletion by the service account sa-2.
Why wrong: This is true but not the reason sa-1 cannot delete; sa-1 lacks permission independently.

Full breakdown with real-world context →

Question 7hardmultiple choice

Full question →

Refer to the exhibit. You restored a Spanner database from a backup and are checking the status of the optimize operation. The operation has been running for 15 minutes and is 45% complete. The database is already accessible but queries on it are slower than expected. What should you do?

Exhibit

gcloud spanner operations describe projects/my-project/instances/test-instance/databases/test-db/operations/_auto_12345

--output:
name: projects/my-project/instances/test-instance/databases/test-db/operations/_auto_12345
metadata:
  '@type': type.googleapis.com/google.spanner.admin.database.v1.OptimizeRestoredDatabaseMetadata
  name: projects/my-project/instances/test-instance/databases/test-db
  progress:
    progress_percent: 45
  start_time: '2023-10-01T10:00:00Z'
  end_time: '2023-10-01T10:15:00Z'
done: false
error: null

A
Continue running queries; performance will improve once the optimize operation completes.
The optimize operation rebuilds indexes and updates statistics, which improves query performance.
B
Wait for the operation to finish before allowing any queries.
Why wrong: Queries are allowed during optimize, and waiting is unnecessary.
C
Drop and restore the database again to start fresh.
Why wrong: This would restart the optimize process and waste time.
D
Cancel the optimize operation to reduce resource usage.
Why wrong: Canceling would leave indexes unoptimized and statistics outdated, hurting performance long-term.

Full breakdown with real-world context →

Question 8mediummultiple choice

Full question →

Refer to the exhibit. The query joins two large tables and aggregates results. Which optimization would most likely reduce the high shuffle bytes in Stage 3?

Exhibit

Refer to the exhibit. In a BigQuery query plan, you see the following stage statistics:

Stage 2: WRITE, 1.2 GB shuffled, 45 seconds
Stage 3: SHUFFLE, 2.5 GB shuffled, 80 seconds
Stage 4: AGGREGATE, 0.5 GB input, 15 seconds

A
Add a WHERE clause to filter rows before the join.
Filtering early reduces the data that needs to be shuffled.
B
Ensure both tables are clustered on the join key.
Why wrong: Clustering can reduce shuffle but not as much as filtering.
C
Use a broadcast join hint to force one table to be broadcast.
Why wrong: Broadcast joins only help if one table is small.
D
Add an ORDER BY clause to sort the data before aggregation.
Why wrong: ORDER BY would increase shuffle.

Full breakdown with real-world context →

Question 9hardmultiple choice

Full question →

Refer to the exhibit. A data engineer created a materialized view on a table that receives streaming inserts. When they query the materialized view, they get this error. What is the most likely cause?

Exhibit

Refer to the exhibit.
bigquery error: Query failed: Cannot query a materialized view that references a table with streaming buffer data.

A
The materialized view definition includes a JOIN that is not supported.
Why wrong: JOINs are supported in materialized views; this error is specific to streaming buffer.
B
The materialized view has reached its maximum size limit.
Why wrong: Size limits would cause a different error.
C
The materialized view cannot read data from the streaming buffer.
Materialized views require data to be committed; streaming buffer data is not yet readable by materialized views.
D
The base table has a schema change that the materialized view cannot adapt to.
Why wrong: Schema changes may cause failure, but the error message indicates streaming buffer, not schema.

Full breakdown with real-world context →

Question 10hardmulti select

Full question →

Refer to the exhibit. A company has a Cloud Spanner instance with the backup configuration shown. They need to improve disaster recovery. Which THREE strategies should they implement?

Exhibit

Refer to the exhibit.

```json
[
  {
    "name": "projects/my-project/instances/test-instance/backups/backup-1",
    "createTime": "2024-01-15T10:00:00Z",
    "expireTime": "2024-01-22T10:00:00Z",
    "versionTime": "2024-01-15T10:00:00Z",
    "database": "projects/my-project/instances/test-instance/databases/mydb",
    "state": "READY",
    "referenceBackup": null,
    "encryptionInfo": {
      "encryptionType": "GOOGLE_DEFAULT_ENCRYPTION"
    }
  }
]
```

A
Schedule regular exports to Cloud Storage
Why wrong: Exports are not suitable for DR due to long export times and potential loss of recent changes.
B
Enable point-in-time recovery (PITR)
PITR allows restoring to any point in time within the retention period, improving recovery granularity.
C
Use multi-region instance configuration
Multi-region provides automatic synchronous replication across regions for high availability and DR.
D
Configure cross-region backups
Cross-region backups store backup data in a different region, protecting against regional failures.
E
Use read replicas in another region
Why wrong: Cloud Spanner does not support read replicas; all nodes are read-write.

Full breakdown with real-world context →

Question 11easymultiple choice

Full question →

Refer to the exhibit. You are analyzing a slow query in Cloud SQL for PostgreSQL. The execution plan shows a sequential scan. Which index should you create to most effectively improve query performance?

Network Topology

A
CREATE INDEX idx_orders_partial ON orders(created_at) WHERE user_id = 123;
Why wrong: Only helps for user_id 123; other users still see sequential scans.
B
CREATE INDEX idx_orders_created_at ON orders(created_at);
Why wrong: Does not cover the equality condition on user_id.
C
CREATE INDEX idx_orders_created_user ON orders(created_at, user_id);
Why wrong: Less efficient because range condition on created_at reduces index usefulness for user_id.
D
CREATE INDEX idx_orders_user_created ON orders(user_id, created_at);
Allows index seek on user_id then range scan on created_at.

Full breakdown with real-world context →

Question 12easymultiple choice

Full question →

Refer to the exhibit. The BI team creates a view to summarize sales. When they query the view with an additional WHERE clause on region, they notice that the underlying query still processes the same amount of data regardless of the filter. What is the most likely reason?

Exhibit

Refer to the exhibit.

CREATE VIEW `myproject.mydataset.sales_summary` AS
SELECT region, SUM(sales) AS total_sales
FROM `myproject.mydataset.sales`
WHERE date >= '2023-01-01'
GROUP BY region;

A
The view is a materialized view that refreshes every 30 minutes.
Why wrong: A materialized view would store pre-aggregated data, but the exhibit shows a standard CREATE VIEW.
B
The view's WHERE clause on date is too restrictive, causing a full scan.
Why wrong: The date filter limits rows, but the outer filter on region does not push down.
C
The view uses authorized views, which prevent predicate pushdown.
Why wrong: Authorized views control access but do not affect query performance.
D
The view is a logical view, not a materialized view, so filters on the view do not reduce the scanned data.
Logical views execute the defining query each time; filters are applied after the view query.

Full breakdown with real-world context →

These PCDE practice questions are part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style PCDE questions with detailed explanations, topic-based practice, mock exams, readiness tracking, and study analytics.

Refer to the Exhibit Practice Questions

How to approach refer to the exhibit practice questions

Quick answer

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Practice scenarios

Refer to the exhibit. This DDL is used to create a table in Cloud Spanner. The table will be used for storing user data with high write throughput. What is one performance issue with this table design?

Exhibit

The exhibit shows query metadata for a query that scans 10 GB. Given the table is 100 GB and partitioned by hire_date, why did the query scan 10 GB and not less?

Exhibit

Refer to the exhibit. You receive an alert from this policy for a Cloud Spanner instance. Which action should you take first?

Exhibit

Refer to the exhibit. A company wants to perform point-in-time recovery (PITR) for their Cloud SQL MySQL instance. Is PITR enabled?

Exhibit

A company notices that their Cloud SQL for PostgreSQL instance, as shown in the exhibit, frequently runs out of storage, causing downtime. They have set up automated backups with point-in-time recovery. What is the most likely cause of the storage issue?

Exhibit

A team is reviewing IAM permissions on a Cloud Storage bucket. The exhibit shows the bucket's IAM policy. A developer is using the service account sa-1 and reports that they cannot delete objects in the bucket. What is the likely reason?

Exhibit

Refer to the exhibit. You restored a Spanner database from a backup and are checking the status of the optimize operation. The operation has been running for 15 minutes and is 45% complete. The database is already accessible but queries on it are slower than expected. What should you do?

Exhibit

Refer to the exhibit. The query joins two large tables and aggregates results. Which optimization would most likely reduce the high shuffle bytes in Stage 3?

Exhibit

Refer to the exhibit. A data engineer created a materialized view on a table that receives streaming inserts. When they query the materialized view, they get this error. What is the most likely cause?

Exhibit

Refer to the exhibit. A company has a Cloud Spanner instance with the backup configuration shown. They need to improve disaster recovery. Which THREE strategies should they implement?

Exhibit

Refer to the exhibit. You are analyzing a slow query in Cloud SQL for PostgreSQL. The execution plan shows a sequential scan. Which index should you create to most effectively improve query performance?

Refer to the exhibit. The BI team creates a view to summarize sales. When they query the view with an additional WHERE clause on region, they notice that the underlying query still processes the same amount of data regardless of the filter. What is the most likely reason?

Exhibit