PCDE Practice Test 1 — 10 Questions

Question 1

A company is planning to use Cloud Spanner for a new global application. They estimate a peak write throughput of 10,000 mutations per second. What is the minimum number of processing units (PUs) required, given that each PU supports up to 2000 mutations/second?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

5 processing units. Cloud Spanner's write throughput scales with the number of processing units (PUs), with each PU supporting approximately 2,000 mutations per second. To sustain a peak of 10,000 mutations per second, you divide 10,000 by 2,000, which equals 5 PUs. Therefore, 5 processing units is the minimum required to meet the stated write throughput.

Answer

1 processing unit

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100 processing units

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10 processing units

Question 2

Refer to the exhibit. A BI query is performing slowly. The query plan shows a large shuffle in the aggregate stage. The table is not partitioned or clustered. Which optimization would most directly reduce the shuffle size?

Accepted Answer

Clustering the table on the grouping columns.. Clustering the table on the grouping columns physically co-locates rows with the same group key values within the same storage units (e.g., files or partitions). This allows the query engine to perform partial aggregation locally before the shuffle, dramatically reducing the amount of data that must be moved across the network during the aggregate stage. In systems like BigQuery or Spark SQL, clustering on grouping columns directly minimizes shuffle size by enabling pre-aggregation at the storage layer.

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Converting the query to use a window function.

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Using a materialized view.

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Adding a WHERE clause to filter recent data.

Question 3

An engineer needs to restore a Cloud Spanner database from a backup that was taken 2 hours ago. The backup is stored in Cloud Storage. What steps are required?

Accepted Answer

Use gcloud spanner databases restore --backup= --destination-database=. To restore a Cloud Spanner database from a backup, use the `gcloud spanner databases restore` command and specify the backup ID and destination database. This is the correct method. Import/export uses Cloud Storage for moving data, but restoring a backup is a separate operation and cannot be done with the import command.

Answer

Create a new instance and use gcloud spanner instances restore.

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Use gcloud spanner databases import from the Cloud Storage backup file.

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Restore is not possible; you must use point-in-time recovery.

Question 4

Your team needs to add a new non-nullable column with a default value to a large Cloud Spanner table. The table has thousands of simultaneous writes per second. Which approach minimizes downtime and resource usage?

Accepted Answer

Use ALTER TABLE ADD COLUMN with a non-null default value. Adding a non-nullable column with a default value in Cloud Spanner is a metadata-only operation that does not rewrite existing rows or block reads/writes. This minimizes downtime and resource usage even under thousands of concurrent writes per second, as the default value is applied logically at read time.

Answer

Use ALTER TABLE ADD COLUMN without a default value and then update rows in batches

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Create a new table and use batch operations to copy data

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Drop and recreate the table with the new column

Question 5

A BI team uses BigQuery to report on customer orders. The 'customers' dimension table is updated nightly with Type 2 Slowly Changing Dimensions (SCD). However, some reports show incorrect historical aggregates because the fact table references only the current customer key. Which approach resolves this issue?

Accepted Answer

Store the surrogate customer key from the dimension table in the fact table at transaction time. With Type 2 SCD, each customer row has a unique surrogate key that represents a specific version of the customer's attributes over time. Storing that surrogate key in the fact table at transaction time ensures that historical facts are permanently linked to the correct customer attributes as they existed at the time of the order. This prevents incorrect aggregates when the dimension table is updated, as the fact table will always join to the precise version of the customer record that was active when the transaction occurred.

Answer

Update the fact table nightly to replace old customer keys with the current key

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Denormalize customer attributes into the fact table

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Use the natural customer ID in the fact table and join with the dimension using a BETWEEN condition on effective dates

Question 6

A company wants to implement least-privilege access for service accounts. Which THREE practices should they follow? (Choose 3)

Accepted Answer

Use Workload Identity Federation instead of service account keys.. Option A is correct because Workload Identity Federation lets external workloads (e.g., on AWS, Azure, or GitHub) impersonate a service account via short-lived OIDC tokens, eliminating long-lived JSON key files that are the primary credential-leak risk. Option D is correct because when keys must be used, regularly rotating them limits the window of exposure if a key is compromised; rotation should be automated and old keys disabled/deleted promptly. Option E is correct because custom IAM roles let you define a permission set containing only the specific permissions the service account needs, which is the essence of least privilege when predefined roles are too broad. Option B is wrong because primitive roles (Owner, Editor, Viewer) are coarse-grained and grant far more access than needed, directly violating least privilege. Option C is wrong because predefined roles are not inherently least-privilege; they often bundle extra permissions, so choosing them merely for compatibility does not minimize access.

Answer

Grant primitive roles for simplicity.

Answer

Use predefined roles to ensure compatibility.

Question 7

A retail company is deploying a new Cloud SQL for MySQL instance for an order management system. The database must survive a zonal failure with minimal downtime and no data loss for committed transactions. They want the simplest configuration that meets this requirement. What should they do?

Accepted Answer

Create the instance with high availability enabled, which provisions a standby in a different zone with synchronous replication and automatic failover.. Enabling high availability on a Cloud SQL for MySQL instance provisions a standby in a different zone with synchronous replication and automatic failover. This provides minimal downtime and no committed data loss during a zonal failure, which is the simplest configuration that meets both requirements for the order management workload.

Answer

Create the instance with a single zone and configure a maintenance window during off-peak hours to reduce the impact of zonal failures.

Answer

Create the instance with a single zone and add a cross-region read replica, then promote the replica if the primary zone fails.

Answer

Create the instance with a single zone and enable automated backups with binary logging for point-in-time recovery.

Question 8

A company is setting up access control for a BigQuery dataset using the above IAM policy. An analyst who is a member of the group 'analysts@example.com' also has the user account 'analyst@example.com'. They need to create new tables in the dataset. What will be the outcome?

Accepted Answer

The analyst can create tables because they have dataOwner role on their user account.. IAM policies grant permissions based on the union of all roles assigned to the user, regardless of whether they come from group membership or direct user assignment. The analyst has the `dataOwner` role directly on their user account, which includes the `bigquery.tables.create` permission required to create new tables. Group membership with a lower-privilege role (e.g., `dataViewer`) does not override or conflict with the higher-privilege role on the user account.

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The analyst will get an error because of conflicting roles.

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The analyst cannot create tables because the group only has dataViewer.

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The analyst can create tables if they also have jobUser role.

Question 9

A company wants to reduce BigQuery query costs for their BI workloads. Which THREE actions effectively lower the amount of data processed per query? (Choose THREE.)

Accepted Answer

Use partitioned tables on date column. Partitioned tables in BigQuery allow queries to use the WHERE clause to filter on the partition column (e.g., a date column), so BigQuery can prune entire partitions from the scan. This directly reduces the amount of data read and billed, lowering query costs. Option A is correct because it is a primary cost-control mechanism in BigQuery.

Answer

Use LIMIT in subqueries to reduce output

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Use SELECT * to avoid missing columns

Question 10

A company runs a microservices application on GKE. They want to automatically adjust both the number of pods (for varying load) and the resource limits of individual pods (to avoid resource waste). Which two Kubernetes resources should they configure together? (Choose two.)

Accepted Answer

Vertical Pod Autoscaler (VPA). The Horizontal Pod Autoscaler (HPA) (C) is correct because it automatically scales the number of pod replicas in a Deployment, ReplicaSet, or StatefulSet based on observed metrics such as CPU utilization, memory, or custom metrics, directly addressing the requirement to adjust pod count for varying load. The Vertical Pod Autoscaler (VPA) (B) is correct because it automatically adjusts the CPU and memory requests/limits of individual pods (typically by evicting and recreating them with updated resource values), which addresses the requirement to tune resource limits and avoid resource waste. Used together, HPA handles horizontal scaling while VPA handles vertical right-sizing. PodDisruptionBudget (A) is not correct because it only controls voluntary disruptions to maintain availability during operations like node drains, not autoscaling. Cluster Autoscaler (D) is not correct because it scales the number of nodes in the cluster, not pods or pod resource limits. Node Auto-Provisioning (E) is not correct because it dynamically creates node pools with appropriate machine types, again operating at the node level rather than adjusting pod counts or pod resource limits.

Answer

PodDisruptionBudget

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Cluster Autoscaler

Answer

Node Auto-Provisioning