PCDE Practice Test 21 — 15 Questions

Question 1

An organization wants to reduce toil by automating a recurring process: every night, a script must run Cloud Build to rebuild a Docker image and deploy it to a GKE cluster. The script currently requires manual invocation by an engineer. Which Google Cloud service can trigger this automation on a schedule without manual intervention?

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

Accepted Answer

Cloud Scheduler. Cloud Scheduler is Google Cloud's fully managed cron service that can invoke HTTP endpoints, Pub/Sub topics, and App Engine handlers on a defined schedule. Pointing a nightly job at a Cloud Build trigger (or a Cloud Function that starts the build) removes the manual invocation step. It is purpose-built for time-based automation without servers to manage.

Answer

Workflows

Answer

Cloud Tasks

Answer

Cloud Functions

Question 2

Arrange the steps to perform a point-in-time recovery (PITR) for a Cloud SQL instance.

Accepted Answer

Enable automatic backups and binary logging, then create a new Cloud SQL instance from the backup at the desired point in time.. PITR requires backups and binary logs enabled; then you create a new instance from backup at the specific time.

Answer

Create a new Cloud SQL instance from the latest backup, then enable binary logging, then perform point-in-time recovery.

Answer

Perform point-in-time recovery directly on the existing Cloud SQL instance by restoring the backup.

Answer

Enable binary logging only, then create a new instance from the backup, then enable automatic backups.

Question 3

A team is using Cloud Spanner and wants to reduce latency for queries that filter on a column that is not part of the primary key. Which feature should they use?

Accepted Answer

Secondary index. Cloud Spanner secondary indexes are the correct feature for accelerating queries that filter on non-primary-key columns. A secondary index creates an additional sorted data structure on the chosen column(s), allowing Spanner to seek directly to matching rows instead of scanning the base table. This is exactly the use case described — reducing latency for filters on a non-key column.

Answer

Storing index

Answer

Interleaved table

Answer

Query explain plan

Question 4

Which THREE techniques can improve query performance in BigQuery for BI workloads? (Choose three.)

Accepted Answer

Use approximate aggregation functions when exact results are not required.. Approximate aggregation functions (Option A) improve performance by using algorithms like HyperLogLog++ to return near-exact results with lower resource consumption. Selecting only needed columns (Option B) reduces the amount of data scanned, lowering query costs and improving speed. Clustering tables on columns used in WHERE clauses (Option E) enables better data pruning and reduces the amount of data processed. These three techniques are commonly tested in the Google Cloud Professional Data Engineer exam as performance optimizations for BI workloads.

Answer

Use SELECT * with LIMIT to preview data.

Answer

Use ORDER BY on large result sets without LIMIT.

Question 5

A developer needs to manually instrument a Go application with distributed tracing and send traces to Cloud Trace. Which approach should they use?

Accepted Answer

Use the OpenTelemetry Go SDK and configure the Cloud Trace exporter.. For manual instrumentation of a Go application sending traces to Cloud Trace, the recommended approach is the OpenTelemetry Go SDK with the Cloud Trace exporter. OpenTelemetry is the vendor-neutral standard, and the Cloud Trace exporter translates OTLP spans into Cloud Trace format.

Answer

Enable automatic instrumentation by adding a Cloud Trace agent to the application.

Answer

Write logs with trace IDs and use log-based metric to track traces.

Answer

Add trace statements using the Cloud Trace API directly.

Question 6

A BI developer needs to display sales data in a dashboard that shows sales in local time zones. The source data stores all timestamps in UTC. Which is the best practice for handling time zone conversions?

Accepted Answer

Store all timestamps in UTC and convert them to the desired time zone in SQL queries. Storing timestamps in UTC and converting them in SQL queries ensures that the conversion logic is centralized, auditable, and consistent across all BI reports. This approach leverages the database engine's time zone functions (e.g., AT TIME ZONE in SQL Server or CONVERT_TZ in MySQL) to handle daylight saving time transitions accurately, avoiding the pitfalls of application-layer conversions that may be inconsistent or not applied uniformly.

Answer

Store timestamps in UTC and convert to local time in the BI tool's application layer

Answer

Store timestamps as text strings with time zone offset to avoid conversion

Answer

Store both UTC and local time in separate columns

Question 7

A team is migrating an on-premises MySQL database to Cloud SQL. The current schema usesMyISAM tables. What is the recommended approach?

Accepted Answer

Convert MyISAM tables to InnoDB before migration.. Cloud SQL for MySQL does not support MyISAM tables because MyISAM lacks transaction support, row-level locking, and crash recovery, which are essential for a managed database service. Converting MyISAM tables to InnoDB before migration ensures compatibility, data integrity, and performance. The recommended approach is to alter the table engine to InnoDB prior to export or use a migration tool that handles the conversion.

Answer

Keep the schema as is; Cloud SQL supports MyISAM.

Answer

Replicate the on-premises MySQL to Cloud SQL using Database Migration Service.

Answer

Export the database using mysqldump and import directly into Cloud SQL.

Question 8

A team is using Cloud Spanner for a global user database. They frequently run JOIN queries between a Users table and an Orders table. The queries are slow and the team suspects they are causing cross-node fan-out. Which schema design technique would reduce latency by co-locating related data?

Accepted Answer

Use interleaved tables (Orders interleaved in Users). Interleaved tables in Cloud Spanner store child rows physically with the parent row, allowing efficient JOINs without cross-node fan-out. Secondary indexes improve lookup but do not co-locate data. Hash-prefixed keys distribute writes but don't co-locate parent-child data. Denormalization avoids JOINs but may not be suitable for all cases.

Answer

Add a hash prefix to the primary key of both tables

Answer

Denormalize the Orders data into the Users table

Answer

Create a secondary index on the foreign key column

Question 9

A DevOps team wants to implement policy-as-code to enforce that all Terraform configurations comply with security rules before deployment. Which tool is most appropriate for pre-commit policy checks on Terraform plans?

Accepted Answer

Conftest. Conftest is a purpose-built tool for testing structured configuration files (like Terraform plan JSON) against policies written in Rego. It integrates directly into CI/CD pipelines and pre-commit hooks, allowing teams to enforce security rules on Terraform plans before deployment. Unlike raw OPA, Conftest provides a streamlined CLI and parsing for multiple formats, making it ideal for pre-commit policy checks on Terraform.

Answer

Google Cloud Deployment Manager

Answer

Open Policy Agent (OPA)

Answer

Sentinel

Question 10

A DevOps engineer is bootstrapping a new organization. They need to set up a centralized logging project to collect audit logs from all projects. What is the required step to enable cross-project log sinks?

Accepted Answer

Create a log sink in each source project with the destination set to the central project's BigQuery dataset.. In Google Cloud, log sinks are created in the project where the logs originate, and the sink's destination can be a centralized project's BigQuery dataset, Cloud Storage bucket, or Pub/Sub topic. To aggregate audit logs from all projects into a central project, you must create a sink in each source project pointing to the central destination. This is the required step because sinks are project-scoped and cannot pull logs from other projects.

Answer

Create a log sink in the central project that pulls logs from other projects.

Answer

Use Cloud Audit Logs API to stream logs to the central project.

Answer

Grant the central project's logging service account the `roles/logging.admin` role in each source project.

Question 11

The query above fails with 'Resources exceeded: UDF out of memory' on a large table. What is the best way to fix this?

Accepted Answer

Rewrite the function as a SQL UDF to avoid JavaScript overhead. JavaScript UDFs in BigQuery run in a sandbox with limited memory (typically 6 MB per UDF instance). When processing a large table, the UDF may exceed this memory due to per-row overhead or large intermediate results. Rewriting the function as a SQL UDF eliminates JavaScript overhead and runs natively within BigQuery's distributed execution engine, which can handle larger datasets without memory constraints.

Answer

Add a GROUP BY clause to reduce the number of rows processed

Answer

Convert the temporary UDF to a persistent UDF

Answer

Increase the memory allocation for JavaScript UDFs

Question 12

A team has a service with an SLO of 99.5% uptime over 30 days. They track error budget and want to alert when the error budget is almost exhausted. What is their total error budget in minutes per month?

Accepted Answer

216 minutes. An SLO of 99.5% uptime over 30 days means 0.5% of the time may be unavailable. Thirty days is 43,200 minutes, and 0.5% of 43,200 is 216 minutes. That is the total error budget for the month.

Answer

360 minutes

Answer

43.2 minutes

Answer

72 minutes

Question 13

A small business runs a MySQL OLTP database for their inventory management system. They need high availability with automatic failover and regional disaster recovery. Which Google Cloud database service meets these requirements with minimal operational overhead?

Accepted Answer

Cloud SQL with HA and cross-region read replicas. Cloud SQL for MySQL with high availability (HA) configuration provides automatic failover within a region and can be configured with cross-region replicas for disaster recovery.

Answer

Cloud Spanner

Answer

Cloud Bigtable

Answer

Compute Engine with self-managed MySQL

Question 14

A company runs an e-commerce website on Cloud SQL. They want to scale read traffic without impacting write performance and need high availability across zones. Which configuration should they use?

Accepted Answer

Use a regional Cloud SQL instance with automatic failover and add read replicas. Cloud SQL offers read replicas for scaling read traffic and regional (multi-zone) instances for high availability. Using a regional instance with automatic failover provides HA; adding read replicas offloads reads. A cross-region replica adds latency, and a single zone with increased tier does not provide HA.

Answer

Create a cross-region replica and use it for reads

Answer

Increase the machine tier of the existing instance

Answer

Migrate to Cloud Spanner for automatic read scaling

Question 15

A BI team wants to create a report that shows daily active users for the last 7 days. Which SQL construct is most appropriate for fast performance on a large dataset?

Accepted Answer

SELECT APPROX_COUNT_DISTINCT(user_id) ... WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY). APPROX_COUNT_DISTINCT uses HyperLogLog (HLL) algorithm, which provides near-exact distinct counts with significantly less memory and faster performance than COUNT(DISTINCT) on large datasets. This is ideal for a daily active users report over 7 days where exact precision is not critical.

Answer

SELECT COUNT(DISTINCT user_id) ... WHERE date >= DATE_SUB(CURRENT_DATE(), INTERVAL 7 DAY)

Answer

SELECT DISTINCT user_id ...

Answer

SELECT COUNT(user_id) ... GROUP BY user_id