PCDE Practice Test 50 — 15 Questions

Question 1

A team uses Cloud Build with a cloudbuild.yaml that builds a Docker image. They want to speed up builds by caching the Docker layers using Kaniko cache in Artifact Registry. Which configuration change is required?

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

Accepted Answer

Use the '--cache' and '--cache-repo' flags in the kaniko builder step. Kaniko supports caching layers to a remote repository. In cloudbuild.yaml, the step using kaniko should set --cache=true and specify the --cache-repo pointing to an Artifact Registry Docker repository.

Answer

Add a step that runs 'docker build --cache-from' pointing to an Artifact Registry repo

Answer

Enable Cloud Build's built-in cache feature in the build configuration

Answer

Add a 'docker push' step after build to store layers in Artifact Registry

Question 2

A team is using Skaffold for local development and CI/CD. They want to use Skaffold profiles to handle differences between local and production environments. In the CI pipeline (Cloud Build), they want Skaffold to build and deploy using the production profile. How should they invoke Skaffold in the cloudbuild.yaml?

Accepted Answer

skaffold run --profile production. Skaffold profiles can be selected with the --profile flag. In a CI environment, use skaffold run --profile production.

Answer

skaffold deploy --profile production

Answer

skaffold dev --profile production

Answer

skaffold build --profile production

Question 3

A company uses Cloud Deploy to manage deployments to multiple GKE clusters. They want to implement a canary deployment strategy where the new revision receives 10% of traffic initially, and after 5 minutes with no errors, it receives 100%. Which TWO actions are required to achieve this?

Accepted Answer

Integrate with Cloud Monitoring for metric-based canary analysis. Cloud Deploy supports canary deployments using the `canaryDeployment` strategy, which allows defining multiple phases with traffic percentages and wait times. In this scenario, the first phase sends 10% traffic, and after 5 minutes with no errors (detected via Cloud Monitoring integration), it progresses to 100%. Thus, configuring the `canaryDeployment` strategy with phases and wait times (E) is required. Additionally, integrating with Cloud Monitoring (A) is necessary to automatically detect errors and trigger the progression or rollback. Option D (manual approval) is not automated and does not fit the 'no errors' requirement.

Answer

Use a blue/green deployment strategy instead

Answer

Enable rollback on failure in the pipeline definition

Answer

Configure a manual approval gate for the canary phase

Question 4

A team is using Firestore in Native mode. They have a collection 'users' with an array field 'roles'. They need to query all users where roles contains 'admin'. What index configuration is required?

Accepted Answer

Create an index exemption on the 'roles' field.. Firestore supports array membership queries using the 'array-contains' operator. An index exemption for the array field is required because Firestore does not automatically index array fields for array-contains queries. The exemption is created manually.

Answer

Create a single-field index on roles with ascending order.

Answer

Create a composite index on roles and the default field.

Answer

No index is needed; Firestore automatically supports array-contains queries.

Question 5

Refer to the exhibit. A company creates these Cloud Spanner tables. What happens when a customer record is deleted?

Accepted Answer

All orders for that customer are automatically deleted.. Cloud Spanner enforces referential integrity through interleaved tables. When a parent row in the Customers table is deleted, all child rows in the Orders table that are interleaved under that customer are automatically deleted via a cascading delete. This behavior is inherent to the interleaved table structure, not an explicit ON DELETE CASCADE clause.

Answer

The deletion fails if there are orders.

Answer

The orders are deleted only if the order date is older than 30 days.

Answer

The orders remain orphaned.

Question 6

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

Answer

Create a new table and use batch operations to copy data

Answer

Drop and recreate the table with the new column

Question 7

Which THREE are best practices for designing a Cloud Spanner schema for high performance? (Choose three.)

Accepted Answer

Avoid using a monotonically increasing primary key as the first part of the key.. A monotonically increasing primary key (e.g., an auto-increment integer or timestamp) creates a hot spot on the last tablet server, causing all writes to be serialized on a single split. Cloud Spanner distributes splits based on the primary key range; a sequential key forces all new inserts into the same split, leading to write contention and poor throughput. Using a hash prefix or a UUID-like key spreads writes evenly across splits, maximizing parallelism.

Option A is wrong because spreading data across multiple regions does not inherently reduce latency; it may increase latency due to geographic distance. Cloud Spanner automatically replicates data across regions for high availability, and best practices recommend placing instances close to users for lower latency.

Option C is wrong because denormalization is generally not recommended in Cloud Spanner. Instead, use interleaved tables to efficiently join parent-child tables without data redundancy. Denormalization can lead to increased storage and update anomalies.

Option D is correct because interleaved tables allow parent and child rows to be stored together, providing efficient access when querying by the parent key. This reduces the need for joins and improves performance.

Option E is correct because secondary indexes are necessary for efficient queries on non-key columns. Without an index, Cloud Spanner would need to perform a full table scan, which is slow for large tables.

Answer

Spread data across multiple regions to reduce latency.

Answer

Denormalize frequently joined tables into a single table.

Question 8

Which TWO actions would help optimize a Cloud SQL for PostgreSQL database experiencing high read latency?

Accepted Answer

Add indexes on frequently queried columns. Adding indexes on frequently queried columns (B) reduces full table scans, and increasing the database tier machine type (C) provides more CPU/memory for query processing. Read replicas (A) distribute load but do not reduce individual query latency; connection pooling (E) helps connection management, not read latency; automatic storage increase (D) is irrelevant.

Answer

Increase the number of read replicas

Answer

Configure automatic storage increase

Answer

Use pgBouncer connection pooling

Question 9

Your team wants to collect custom metrics from a .NET application running on Compute Engine and send them to Cloud Monitoring. Which TWO approaches are valid?

Accepted Answer

Use the Google.Cloud.Monitoring.V3 client library to call the metric API directly. Valid approaches: (1) Use the OpenTelemetry .NET SDK to instrument the application and export metrics to Cloud Monitoring via the Google Cloud exporter. (2) Use the Google.Cloud.Monitoring.V3 client library to write custom metrics directly via the Monitoring API. The other options are invalid: Stackdriver client is deprecated, StatsD is not directly supported by Cloud Monitoring, and gcloud commands are for administration, not application-level metric writing.

Answer

Configure the application to emit metrics to a StatsD daemon and configure Cloud Monitoring to scrape it

Answer

Install the Stackdriver Monitoring agent and enable custom metrics

Answer

Use gcloud beta monitoring metrics create to define and write metrics from the application

Question 10

A BI team in a large enterprise uses Looker connected to BigQuery. The data model has a primary table 'sales_fact' with billions of rows and multiple dimensions. The team notices that Looker queries often time out. Which approach would most likely resolve this without changing the data model?

Accepted Answer

Create a materialized view in BigQuery for the most common aggregations. Materialized views in BigQuery pre-compute and store the results of common aggregations, allowing subsequent queries to read pre-aggregated data instead of scanning the full sales_fact table. This reduces query execution time significantly without altering the underlying data model, directly addressing the timeout issue.

Answer

Request Google Support to increase BigQuery timeout

Answer

Increase BigQuery slot capacity

Answer

Switch Looker to use SQL Runner only

Question 11

A company is migrating their on-premises PostgreSQL database to Cloud SQL. They want to minimize downtime during the migration. Which approach should they use?

Accepted Answer

Use Database Migration Service with continuous replication. Database Migration Service (DMS) with continuous replication is the correct approach because it uses change data capture (CDC) to replicate ongoing transactions from the source PostgreSQL database to Cloud SQL, enabling a near-zero downtime migration. DMS handles schema conversion, data validation, and automated failover, which minimizes the cutover window to seconds or minutes.

Answer

Use pg_dump and pg_restore

Answer

Export data to CSV and import into Cloud SQL

Answer

Use a third-party ETL tool

Question 12

You need to export data from Cloud Spanner for archival purposes. Which method is most cost-effective?

Accepted Answer

Use gcloud spanner databases export. `gcloud spanner databases export` uses Cloud Spanner's built-in managed export feature, which directly exports data to Cloud Storage in Avro format without incurring compute costs from Dataflow or Cloud Functions. This is the most cost-effective method for archival purposes as it leverages Spanner's internal export infrastructure, which is optimized for bulk data movement and has no additional per-operation compute charges beyond the storage and network egress costs.

Answer

Use a Cloud Function to copy data

Answer

Use Dataflow to read and write to Cloud Storage

Answer

Use gcloud spanner databases execute-sql

Question 13

A Bigtable cluster has 10 nodes and is experiencing 90% CPU utilization, causing increased latency. The workload is mostly random reads (70%) and writes (30%). The table has 50TB of data, and the row key design is efficient. What is the best way to reduce CPU utilization?

Accepted Answer

Increase the number of nodes to 20.. Increasing the number of nodes distributes the load and reduces CPU per node, directly addressing the high utilization. Adding SSDs or compaction may help marginally but not as effectively as adding nodes.

Answer

Add SSDs instead of HDDs.

Answer

Enable replication for read offloading.

Answer

Compact the table to reduce SSTable count.

Question 14

You are monitoring a Memorystore for Redis instance serving as a cache for an e-commerce application. The cache hit ratio has dropped from 95% to 70%. Which action is most likely to restore the hit ratio?

Accepted Answer

Increase the maxmemory setting to accommodate more cache entries.. A drop in cache hit ratio from 95% to 70% indicates that the cache is evicting frequently accessed keys to make room for new ones. Increasing the maxmemory setting allows Redis to store more entries, reducing evictions and restoring the hit ratio. This is the most direct way to address the capacity issue without changing application behavior.

Answer

Reduce the network latency between the application and Redis by moving them to the same zone.

Answer

Change the eviction policy to 'noeviction' to prevent key removal.

Answer

Enable AOF persistence to ensure data survives restarts.

Question 15

A developer is designing a schema for Firestore to store user profiles. Each user has a unique ID and multiple addresses. Which data modeling approach is recommended for Firestore?

Accepted Answer

Store addresses as a nested map within the user document.. Firestore recommends denormalizing one-to-few relationships. Storing addresses as a nested map within the user document (Option D) preserves structure and is efficient for small, fixed sets. Option A (string array) loses key-value structure. Option B is not possible because Firestore does not support relational joins. Option C (separate collection) is more appropriate for large or frequently changing lists, not for a few addresses per user.

Answer

Store addresses as a string array in the user document.

Answer

Use a relational join between users and addresses collection.

Answer

Create a separate collection for addresses with a reference to user ID.