PCDE Practice Test 35 — 15 Questions

Question 1

A startup is building a BI stack on Google Cloud. They have moderate data volumes and need to run ad-hoc analytical queries and real-time dashboards. Which Google Cloud database service is most appropriate for this workload?

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

Accepted Answer

BigQuery. BigQuery is a serverless, highly scalable data warehouse designed for analytical queries and real-time dashboards. It supports ad-hoc SQL queries on large datasets with fast execution via its columnar storage and distributed query engine, making it ideal for BI workloads with moderate data volumes.

Answer

Cloud Spanner

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Firestore

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Cloud SQL

Question 2

A company runs an e-commerce application on Cloud SQL for MySQL. They need to recover the database to a specific point in time 15 minutes ago after an accidental data deletion. What must be configured beforehand?

Accepted Answer

Configure binary logging and set a PITR retention period. Point-in-time recovery (PITR) in Cloud SQL for MySQL relies on binary logging (binlog) to capture all write operations. To recover to a specific moment (e.g., 15 minutes ago), you must enable automated backups (which provide the base restore point) and configure binary logging with a retention period that covers the desired recovery window. Option C correctly specifies both requirements: binary logging must be enabled, and a PITR retention period (e.g., 1–7 days) must be set to retain the necessary binlog files for replay.

Answer

Create a cross-region replica for failover

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Enable automated backups only

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Set up an on-demand backup every hour

Question 3

A data analyst needs to create a reporting table that aggregates sales data by month. They want to ensure the table is optimized for querying by month and product category. Which table design best supports this?

Accepted Answer

Partition by month and cluster by product_category.. Partitioning by month physically separates data into monthly segments, allowing query pruning to skip irrelevant partitions when filtering by month. Clustering by product_category within each partition co-locates rows with the same category, reducing the amount of data scanned for queries that filter on both month and category. This design optimizes both I/O and scan efficiency for the described workload.

Answer

Use a table with clustering on product_category only.

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Use a flat table with no partitioning.

Answer

Use a view that selects month and product_category.

Question 4

A team wants to create a log-based metric that counts ERROR log entries grouped by the service name extracted from a JSON field. The log entries are structured JSON with a 'service' key. Which type of log-based metric should they create?

Accepted Answer

Counter metric with a label extracted from 'service' field. A counter log-based metric is correct because the team wants to count occurrences of ERROR entries, and labels allow grouping by the extracted 'service' field. Counter metrics increment on each matching log line, and the label dimension enables per-service breakdowns in dashboards and alerts.

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Alerting policy with a condition on 'service' field

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Gauge metric with the 'service' field as value

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Distribution metric with a histogram of the 'service' field

Question 5

A company wants to implement security controls for Compute Engine VMs across their organization. Which THREE organization policies can enforce VM security? (Choose 3)

Accepted Answer

`compute.requireShieldedVm`. Option A, `compute.requireShieldedVm`, is correct because this organization policy constraint forces Compute Engine VMs to be created as Shielded VMs, enabling verified boot, vTPM, and integrity monitoring to protect against boot-level and kernel-level malware. Option C, `compute.vmExternalIpAccess`, is correct because it lets administrators define an allowlist (or deny all) of external IP addresses for VM instances, preventing unauthorized internet exposure of Compute Engine VMs. Option D, `compute.trustedImageProjects`, is correct because it restricts which projects' images can be used to create boot disks, ensuring VMs only boot from vetted, trusted image sources. Option B, `storage.uniformBucketLevelAccess`, is not correct here because it is a Cloud Storage bucket-level access control constraint, not a Compute Engine VM security policy. Option E, `iam.disableServiceAccountKeyCreation`, is not correct here because it governs IAM service account key creation rather than enforcing security controls on Compute Engine VMs themselves.

Answer

`storage.uniformBucketLevelAccess`

Answer

`iam.disableServiceAccountKeyCreation`

Question 6

An engineer needs to troubleshoot a performance issue where a specific function in a Go application is consuming excessive CPU. They want to identify the exact lines of code causing the bottleneck with minimal overhead. Which Google Cloud tool should they use?

Accepted Answer

Cloud Profiler. Cloud Profiler is Google Cloud's continuous CPU and heap profiling service that instruments production applications with very low overhead (typically under 1% CPU) and attributes consumption down to individual functions and source lines. For a Go application, the profiler agent uses Go's built-in pprof runtime hooks to sample stack traces, so it can pinpoint the exact function and line causing the CPU bottleneck. This is precisely the use case Cloud Profiler was designed for.

Answer

Error Reporting

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Cloud Monitoring

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Cloud Trace

Question 7

A team notices that queries on a Cloud Spanner database are slow. They want to identify which queries are consuming the most resources. What should they use?

Accepted Answer

Query Insights. Query Insights is the correct tool because it is specifically designed for Cloud Spanner to analyze query performance, providing detailed metrics such as execution latency, CPU usage, and rows scanned per query. It helps identify the most resource-intensive queries by breaking down performance by query fingerprint, allowing the team to pinpoint and optimize slow queries directly.

Answer

Cloud Logging

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Performance Dashboard

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Cloud Monitoring metrics

Question 8

A company uses Cloud Deploy to manage deployments to multiple GKE clusters across environments. They want to implement a canary deployment strategy where 10% of traffic goes to the new version initially, and after manual approval, the remaining 90% rolls out. What should be included in the delivery pipeline configuration?

Accepted Answer

Define a 'canary' strategy with a first phase of 10% and a second phase of 90% with an approval gate. Cloud Deploy's 'canary' strategy allows defining multiple phases with incremental traffic percentages, and an approval gate can be inserted between phases to require manual sign-off before proceeding. This directly matches the requirement: a first phase sending 10% traffic to the new version, then a manual approval, followed by a second phase rolling out the remaining 90%.

Answer

Configure a 'canary' strategy with a single phase of 10% and rely on external traffic management

Answer

Set the rollout strategy to 'standard' and use skaffold.yaml for traffic splitting

Answer

Use a 'blueGreen' strategy with a 10% preview phase

Question 9

Your Spanner instance is running a workload with high read throughput. You notice that read latency has increased significantly. Upon investigating, you find that the instance is experiencing high CPU utilization on the Spanner nodes. The workload consists of many small point lookups (reads by primary key). Which action is most likely to reduce read latency?

Accepted Answer

Use stale reads with a timestamp bound to allow reads from replicas.. B is correct because stale reads allow Cloud Spanner to serve read requests from read-only replicas, which offloads CPU-intensive processing from the leading replica nodes. By using a timestamp bound (e.g., exact_staleness or max_staleness), the workload can tolerate slightly outdated data, reducing the load on the nodes and lowering read latency for point lookups.

Answer

Add secondary indexes on the primary key columns.

Answer

Redesign the schema to use interleaved tables.

Answer

Reduce the number of Spanner nodes to lower CPU overhead.

Question 10

An SRE team is creating a postmortem after a service outage. They want to ensure the process is blameless and focuses on systemic improvements. Which practice is central to a blameless postmortem?

Accepted Answer

Assigning action items with owners and due dates to prevent recurrence. A blameless postmortem focuses on systemic causes and produces actionable improvements — assigning action items with clear owners and due dates is the practice that converts findings into prevention. This is central because it ensures the learning translates into concrete changes rather than remaining a discussion.

Answer

Determining the financial cost of the outage

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Escalating the issue to senior management

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Identifying the individual who caused the incident for performance review

Question 11

A company is migrating an on-premises PostgreSQL database to Cloud SQL for PostgreSQL. They need to ensure minimal downtime and maintain ongoing replication until cutover. Which approach should they use?

Accepted Answer

Use Database Migration Service with a continuous migration job.. Database Migration Service (DMS) with a continuous migration job is the correct approach because it supports minimal-downtime migrations from on-premises PostgreSQL to Cloud SQL by using PostgreSQL's native logical replication. DMS handles the initial one-time data load and then continuously replicates ongoing changes until you are ready to cut over, meeting the requirement for minimal downtime and ongoing replication.

Answer

Create a replica of the on-premises PostgreSQL and use pg_basebackup to stream to Cloud SQL.

Answer

Export using pg_dump with --no-owner and --no-acl flags, then manually configure logical replication slots.

Answer

Use pg_dump to export the database and import it into Cloud SQL, then set up application-level dual writes.

Question 12

A DevOps team wants to use Cloud Deploy to promote releases across multiple targets. They need to define a delivery pipeline that includes a canary deployment for a GKE cluster and a standard deployment for Cloud Run. Which two strategies can they use in the same pipeline?

Accepted Answer

Canary strategy for GKE. Option D is correct because Cloud Deploy supports a canary deployment strategy for GKE targets, which allows a percentage of traffic to be shifted to the new revision in increments with optional verification and can be defined as a stage in the delivery pipeline. Option E is correct because Cloud Deploy supports the standard (non-progressive) deployment strategy for Cloud Run targets, which deploys the new revision and routes all traffic to it, and this can coexist with a canary stage for GKE in the same pipeline since each target defines its own strategy. Option A is not correct because progressive delivery with manual approval is not a Cloud Deploy strategy type; approvals are configured via the pipeline's approval phase, not as a deployment strategy. Option B is not correct because Cloud Deploy does not offer a blue/green strategy for Cloud Run; blue/green is available for GKE and Anthos targets. Option C is not correct because Cloud Deploy does not provide a rolling update strategy for GKE; GKE strategies in Cloud Deploy are standard and canary.

Answer

Progressive delivery strategy with manual approval

Answer

Blue/green strategy for Cloud Run

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Rolling update strategy for GKE

Question 13

A team uses Cloud Build to deploy to Cloud Run via gcloud run deploy. They need to perform a canary deployment where 10% of traffic goes to the new revision for 10 minutes before shifting to 100%. The deployment must be automated without manual steps. What approach should they take?

Accepted Answer

Use Cloud Deploy with a canary strategy targeting Cloud Run. Cloud Deploy supports a native canary deployment strategy that can target Cloud Run, allowing declarative definition of a 10% traffic split for a specified duration before automatic promotion to 100%. This satisfies the requirement for a fully automated, multi-phase rollout without manual intervention. It is the purpose-built GCP service for progressive delivery.

Answer

Use Cloud Build with a substitution variable to control traffic percentage across two builds

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Use two separate Cloud Build steps: first deploy with --no-traffic, then second step after 10 minutes with --to-revisions

Answer

Use a single gcloud run deploy with --traffic flags to set 10% initially, then manually update traffic later

Question 14

Refer to the exhibit. You receive the following query output showing bytes processed for a BigQuery query. The table is partitioned by date and clustered on country. What is the most likely reason for the high bytes processed?

Accepted Answer

The query does not filter on the clustering column, causing full scan of selected partitions. BigQuery clustering only reduces the bytes scanned when the query filters on the clustering column (country). Without a WHERE clause on country, BigQuery must scan all rows in the selected partitions, even though partition pruning may reduce the date range. The high bytes processed indicates that clustering is not being leveraged, so the query performs a full scan of the chosen partitions.

Answer

The GROUP BY country requires sorting all rows

Answer

The table is not partitioned correctly

Answer

The date range is too wide

Question 15

You have a Cloud Bigtable instance with a single cluster. To improve availability and durability, you want to enable replication across two regions. After configuring replication, you notice that reads from the replica cluster show data that is not yet consistent with the primary. What is the expected consistency model for Bigtable replication?

Accepted Answer

Eventually consistent reads from replicas. Cloud Bigtable replication uses an eventually consistent model for reads from replica clusters. When you write to the primary cluster, the data is asynchronously replicated to the replica cluster using a distributed replication pipeline. This means that reads from the replica cluster may return stale data until the replication process completes, and there is no guarantee of immediate consistency with the primary.

Answer

Read-your-writes consistency from the primary

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Strongly consistent reads from any cluster

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Strict serializability across clusters