PCDE Mock Exam 79 — 25 Questions

Question 1

A team wants to implement a GitOps workflow for deploying applications to GKE. They want to use a tool that continuously reconciles the cluster state with a Git repository. Which TWO tools can they use? (Select TWO)

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

Accepted Answer

Argo CD. Argo CD is a declarative GitOps tool that continuously monitors a Git repository and automatically reconciles the cluster state to match the desired state defined in the repository. It directly supports the GitOps workflow requirement by polling or using webhooks to detect changes and applying them to GKE clusters.

Answer

Cloud Deploy

Answer

Cloud Build

Answer

Skaffold

Question 2

You are configuring a Cloud Build pipeline that builds a Docker image, pushes it to Artifact Registry, and deploys to Cloud Run. The build requires network access to a private VPC to download dependencies. Which approach should you use to provide VPC access?

Accepted Answer

Create a Cloud Build private pool connected to the VPC, and run the build using that pool.. Cloud Build private pools provide direct VPC connectivity by running worker VMs inside a customer-managed subnet within the specified VPC. This allows the build to access private resources (e.g., dependency mirrors, internal repositories) without traversing the public internet, meeting the requirement for network access to a private VPC.

Answer

Add the VPC firewall rule to allow traffic from Cloud Build's default IP range.

Answer

Use the default Cloud Build pool and configure VPC peering in the build steps.

Answer

Use Cloud NAT to allow egress from the default pool to the VPC.

Question 3

A team uses Cloud Deploy with a canary deployment strategy to GKE. They want to automatically promote the canary to full production if the error rate is below 1% for 10 minutes. What should they configure?

Accepted Answer

Configure a canary deployment with a metric threshold in clouddeploy.yaml. Cloud Deploy supports canary deployments with automated promotion based on metric thresholds defined in the `clouddeploy.yaml` configuration. By specifying a metric threshold (e.g., error rate < 1% for 10 minutes) under the `canaryDeployment` strategy, Cloud Deploy can automatically promote the canary to full production without manual intervention or external scripts.

Answer

Use a Cloud Build step to check error rates

Answer

Use a manual approval gate

Answer

Set the deployment strategy to blue/green

Question 4

A team wants to adopt GitOps for a GKE cluster. They need a solution that automatically syncs their Kubernetes manifests from a Git repository to the cluster and ensures the cluster state matches the repo. Which Google Cloud service should they use?

Accepted Answer

Config Sync. Config Sync is the correct choice because it is the native GitOps agent within Google Cloud's Anthos platform, designed specifically to continuously reconcile the state of a GKE cluster with Kubernetes manifests stored in a Git repository. It automatically detects changes in the configured source of truth and applies them to the cluster, ensuring drift is corrected without manual intervention.

Answer

Config Connector

Answer

Cloud Deploy

Answer

Argo CD on GKE

Question 5

A team is setting up a Cloud Build private pool to build Docker images that need access to resources in a VPC. After creating the private pool, builds fail with network errors. What is the most likely missing step?

Accepted Answer

Grant the Cloud Build service account the 'compute.networkUser' role. For Cloud Build private pools to access resources in a VPC, the Cloud Build service account must have the 'compute.networkUser' role on the VPC. This role allows the pool to create and use network interfaces within the VPC. Without this permission, builds fail with network errors even if the pool is correctly configured.

Answer

Configure Service Directory (private connection) between the pool and the VPC

Answer

Enable Cloud NAT on the VPC

Answer

Assign a public IP to the build instances

Question 6

A DevOps team uses Cloud Build to build a multi-service application. They have three services: frontend, backend, and worker. They want to run builds for all three services in parallel to speed up the pipeline. Which of the following cloudbuild.yaml configurations are valid for achieving parallel execution? (Choose TWO).

Accepted Answer

Set waitFor: ['-'] on all three steps.. Both options C and E achieve parallel execution of the three services. In option C, setting `waitFor: ['-']` on all three steps explicitly tells Cloud Build that each step has no dependencies, so they all start simultaneously. In option E, defining the three steps without any `waitFor` field also results in parallel execution because Cloud Build treats steps with no `waitFor` as having no dependencies and starts them at the same time. Options A, B, and D do not produce parallel execution: A runs only the first step immediately and the others sequentially, B defines a step that runs after all three (not parallel for the three), and D uses a single step which runs commands sequentially.

Answer

Set waitFor: ['-'] on the first step and omit waitFor on the others.

Answer

Set waitFor: ['frontend', 'backend', 'worker'] on a subsequent step to run after all three.

Answer

Use a single step with multiple entrypoints and args.

Question 7

A company wants to implement granular cost tracking for their cloud resources. They need to attribute costs to specific teams and environments. Which approach should they use?

Accepted Answer

Apply labels to resources, such as 'team:engineering' and 'environment:prod', and use billing export to BigQuery for cost analysis.. Labels are key-value pairs that can be applied to resources. By using labels like 'team' and 'environment', costs can be broken down in billing reports and BigQuery exports.

Answer

Use separate projects for each team and environment, and analyze costs per project.

Answer

Use separate billing accounts for each team.

Answer

Enable cost attribution in Cloud Monitoring.

Question 8

A company wants to migrate their on-premises PostgreSQL database to Cloud SQL using DMS. The source database is behind a firewall and does not have a public IP. The target Cloud SQL instance uses a private IP. How should the engineer connect the source to DMS?

Accepted Answer

Use a VPN or VPC peering to connect the source network to the GCP VPC.. For private connectivity, VPC peering between the source network and GCP VPC is required. Cloud SQL Auth Proxy is for connecting to Cloud SQL, not for source.

Answer

Assign a public IP to the source database.

Answer

Use DMS with a connection profile that specifies the source's private IP without any network configuration.

Answer

Configure Cloud SQL Auth Proxy on the source.

Question 9

Which TWO optimizations best address slow join performance caused by excessive broadcasting in BigQuery? (Choose two.)

Accepted Answer

Use a materialized view that pre-joins the tables.. A materialized view can pre-compute and store the join result, eliminating the need to re-execute the join at query time. This avoids the overhead of broadcasting the dimension table repeatedly, as the materialized view is incrementally refreshed and queried directly, reducing both shuffle and broadcast costs.

Answer

Use a large query timeout.

Answer

Set the dimension table to be very large to prevent broadcast.

Answer

Increase the number of slots.

Question 10

A company is designing a BigQuery data model for a business intelligence dashboard that shows sales by region and product. The data is refreshed daily. Which schema design is MOST cost-effective and performant for this use case?

Accepted Answer

A star schema with a fact table for sales and separate dimension tables for region and product.. A star schema with a fact table for sales and dimension tables for region and product is optimized for analytical queries in BigQuery, providing a balance of query performance and storage efficiency for daily refreshes. Option A is wrong because nested repeated columns can complicate queries and are less efficient for the simple dimensional analysis required by a BI dashboard. Option C is wrong because a fully normalized schema with many joins increases query complexity and latency, making it less performant for BI workloads. Option D is wrong because a single flat table leads to higher storage costs and slower queries due to scanning unnecessary columns and data duplication.

Answer

A table with nested repeated columns for regions and products within each sale.

Answer

A fully normalized schema with separate tables for each attribute.

Answer

A single flat table containing all sales, region, and product columns.

Question 11

Which TWO of the following are valid ways to improve the performance of a BigQuery query that joins two large tables?

Accepted Answer

Apply WHERE clauses to filter each table before the join.. Applying WHERE clauses before the join (e.g., using subqueries or CTEs to pre-filter each table) reduces the amount of data shuffled and processed during the join phase. BigQuery's query engine can push down filters to the storage layer, minimizing the bytes read and improving performance significantly. Option B is also correct because a materialized view can pre-join the tables and store the results, allowing subsequent queries to read from the pre-joined view instead of performing the join each time. This can drastically reduce query time, especially for repeated queries on the same join pattern.

Answer

Use the 'JOIN EACH' clause.

Answer

Denormalize the tables into a single table.

Answer

Set the query option 'USE_CACHE=TRUE'.

Question 12

A company tracks customer demographics that change over time (e.g., address). They need to maintain historical accuracy in BI reports. Which approach correctly implements a Type 2 slowly changing dimension?

Accepted Answer

Add effective start and end date columns for each dimension attribute. Type 2 SCD requires preserving full history by adding effective start and end date columns to the dimension table. This allows BI reports to join on a fact row's transaction timestamp and retrieve the exact dimension attribute values that were current at that point in time, ensuring historical accuracy without data loss.

Answer

Store only the current value and rely on the fact table's timestamp to infer history

Answer

Store only the current value in the dimension table and use an audit log for changes

Answer

Overwrite the old value with the new value

Question 13

A financial company runs BI queries on a BigQuery table that is partitioned by ingestion time. The table is 1 TB and receives streaming inserts every minute. Analysts query the last 24 hours of data. The queries are slow. The table is clustered by transaction_id. What is the likely cause?

Accepted Answer

The cluster column is not used in queries.. Clustering sorts data within partitions based on the cluster column. If queries filter or aggregate by `transaction_id`, clustering can significantly reduce the amount of data scanned. However, if analysts query the last 24 hours of data without referencing `transaction_id` in WHERE or GROUP BY clauses, the clustering provides no benefit, and the query must scan the entire partition, leading to slow performance.

Answer

Streaming buffer causes delays.

Answer

Queries use SELECT *.

Answer

Partition expiration is set too short.

Question 14

A BI team queries this table with a WHERE clause that filters on product_id but does not include a sale_date filter. What is the outcome?

Accepted Answer

The query runs successfully but scans all partitions.. In BigQuery, when a table is partitioned on sale_date but the WHERE clause filters only on product_id (without sale_date), the query cannot prune partitions. Without the require_partition_filter option, the query runs successfully but scans all partitions. Option D correctly describes this outcome.

Answer

The query fails with an error.

Answer

The query runs successfully and only scans partitions containing product_id values.

Answer

The query runs successfully and scans only the latest partition.

Question 15

A BI manager needs to restrict access to sensitive sales data so that salespeople can only see their own region's data. Which BigQuery feature should be used to implement row-level security without duplicating tables?

Accepted Answer

Use BigQuery row-level access policies. BigQuery row-level access policies are a native feature that allows restricting access to specific rows in a table without duplicating data. They provide a direct, scalable, and efficient way to implement row-level security based on user attributes like region. While authorized views with SESSION_USER() can also achieve similar functionality, they are not the native feature and require additional management overhead. Therefore, Option B is the correct answer.

Answer

Use column-level security to hide sensitive columns

Answer

Create an authorized view that uses SESSION_USER() in a WHERE clause to filter rows

Answer

Create separate IAM roles for each region

Question 16

A company runs a MySQL database on Cloud SQL for an e-commerce platform. They need to add a new column to a table with millions of rows without causing downtime. What is the recommended approach?

Accepted Answer

Use a tool like pt-online-schema-change to perform the change with minimal impact.. Pt-online-schema-change (or gh-ost) creates a shadow table with the new schema, incrementally copies rows using triggers or binary log replay, and then atomically swaps the tables. This avoids holding any locks on the original table, preventing downtime for an e-commerce platform with millions of rows. Cloud SQL's InnoDB does not support true online DDL for all ALTER TABLE operations, especially on large tables, making a dedicated online schema change tool the safest approach.

Answer

Use 'ALTER TABLE ... ADD COLUMN ... ALGORITHM=INPLACE, LOCK=NONE'.

Answer

Create a new table with the column, copy data manually, then swap tables.

Answer

Use 'ALTER TABLE ... ADD COLUMN' directly; Cloud SQL handles it online.

Question 17

A company is migrating their on-premises MySQL database to Cloud SQL using Database Migration Service (DMS). The source database is MySQL 5.7, and the target is a Cloud SQL MySQL 8.0 instance. The migration job is set to continuous, but after the full dump phase, the CDC phase keeps failing with the error 'binary log not found'. What is the most likely cause?

Accepted Answer

The source MySQL binary logs have been purged before DMS could replicate them.. DMS continuous migration requires binary logging enabled on the source with row-based format, and the binary log files must be retained until they are consumed. If the source binary logs are purged before DMS reads them, CDC fails.

Answer

Cloud SQL Auth Proxy is not configured on the source.

Answer

The Cloud SQL instance does not have binary logging enabled.

Answer

The DMS migration job uses the wrong source connection profile.

Question 18

A Cloud SQL instance is running low on disk space. You want to increase disk size with zero downtime. Which action should you take?

Accepted Answer

Use gcloud sql instances patch to increase the storage size. Cloud SQL supports online storage increases without downtime. The `gcloud sql instances patch` command (or the equivalent Console/API operation) dynamically adds more disk capacity to the running instance while it continues serving traffic. This is possible because Cloud SQL uses persistent disks that can be resized online, and the database engine (e.g., MySQL, PostgreSQL) does not need to restart to recognize the additional space.

Answer

Stop the instance, increase disk, and restart

Answer

Export data, create a new instance with larger disk, and import

Answer

Create a clone with larger disk and failover

Question 19

Refer to the exhibit. The company plans to store 3 TB of data in this instance. What is the minimum number of nodes required? (Assume 2 TB per node for HDD and 4 TB per node for SSD; this instance uses SSD.)

Accepted Answer

1. The question explicitly assumes 4 TB per SSD node and asks for the minimum number of nodes required to store 3 TB of data. Since 4 TB is greater than 3 TB, a single node provides sufficient raw storage capacity. The question does not specify any requirements for high availability, replication, or redundancy, so the minimum number of nodes is 1.

Answer

4

Answer

2

Answer

3

Question 20

A company wants to migrate their on-premises SQL Server database to Cloud SQL for PostgreSQL using Database Migration Service. They need to minimize downtime. The source database is 2 TB and the network link has 1 Gbps bandwidth. What should they do first?

Accepted Answer

Create a continuous migration job with DMS using a VPC peering connection.. For minimal downtime, a continuous migration job should be used so that after the initial full dump, ongoing changes are replicated until cutover.

Answer

Use pg_dump to export the database and then import into Cloud SQL.

Answer

Create a one-time migration job to copy the database during a maintenance window.

Answer

Use mysqldump to export and import the database.