PCDE Practice Test 7 — 10 Questions

Question 1

An organization wants to store Docker images in a private Google Cloud repository with vulnerability scanning enabled. Which service should they use?

Accepted Answer

Artifact Registry. Artifact Registry is Google Cloud's recommended service for storing container images and other artifacts (Maven, npm, Python packages). It natively supports vulnerability scanning via Container Analysis, integrates with IAM for fine-grained access control, and supports regional repositories. Container Registry (GCR) is deprecated in favor of Artifact Registry, making Artifact Registry the correct modern choice.

Answer

Container Registry

Answer

Cloud Build

Answer

Cloud Storage

Question 2

An organization needs to run both transactional (OLTP) and real-time analytical (OLAP) queries on the same dataset without data duplication. The dataset is moderately large (a few terabytes). Which Google Cloud database service is MOST appropriate for this HTAP workload?

Accepted Answer

AlloyDB. AlloyDB is Google Cloud's PostgreSQL-compatible, fully managed database designed for HTAP workloads. It uses a columnar engine that automatically accelerates analytical queries on the same data used for transactional processing, eliminating the need for ETL or data duplication. Its architecture separates compute and storage, allowing independent scaling for OLTP and OLAP, and it supports real-time analytics with minimal latency. For a few terabytes, AlloyDB provides the best balance of transactional throughput and analytical performance without duplicating data.

Answer

Bigtable

Answer

BigQuery

Answer

Cloud SQL for PostgreSQL

Question 3

An SRE team wants to track the amount of toil each week and ensure it does not exceed 50% of the team's time. Which approach is most aligned with SRE best practices?

Accepted Answer

Have each team member log their time and estimate toil percentage weekly. SRE best practices emphasize measuring toil to ensure it does not exceed 50% of the team's time. The most practical approach is to have each team member log their time and estimate toil percentage weekly. This provides a qualitative and quantitative measure, allowing the team to track and address toil.

Answer

Use Cloud Monitoring to automatically classify all tasks as toil

Answer

Use Cloud Tasks to queue up toil items and measure completion time

Answer

Set a Cloud Scheduler job to remind team members to automate toil

Question 4

You need to monitor the number of ERROR-level log entries in Cloud Logging and trigger an alert when the count exceeds 100 in 5 minutes. Which approach should you use?

Accepted Answer

Create a log-based counter metric with filter 'severity=ERROR' and then set an alert on that metric. A log-based counter metric counts matching log entries, and a filter of severity=ERROR scopes it to error logs. An alerting policy on that counter metric with a threshold of 100 over a 5-minute window triggers exactly as required. This is the standard Cloud Monitoring pattern for log-derived alerts.

Answer

Create a log-based distribution metric and set an alert on the count

Answer

Use a logs-based alerting policy directly from Cloud Logging

Answer

Export logs to BigQuery and run a scheduled query to trigger a custom alert

Question 5

A company is migrating a large on-premises MySQL database to Cloud SQL. The source database supports heavy reporting queries that scan millions of rows and join multiple tables. The team wants to minimize downtime and avoid performance degradation during migration. Which migration approach should they use?

Accepted Answer

Set up Database Migration Service (DMS) with continuous replication from the source to Cloud SQL, then cut over when replication lag is minimal. Database Migration Service (DMS) performs an initial full dump-and-load followed by continuous change data capture (CDC) replication from the source MySQL to Cloud SQL, allowing the source to remain fully operational during migration. Because replication is continuous, the cutover can be scheduled when replication lag is near zero, minimizing downtime and avoiding the performance hit of a cold dump/restore. This is the recommended approach for minimizing downtime on large, actively used MySQL databases.

Answer

Use gcloud sql import command to import a CSV export while the source database remains in use

Answer

Create a BigQuery transfer service to move data from MySQL to Cloud SQL

Answer

Use mysqldump to export the data and import it into Cloud SQL during a maintenance window

Question 6

You are designing a BigQuery schema for IoT sensor data. The sensor readings have varying fields depending on the sensor type. You want to minimize storage costs and avoid schema maintenance when new sensor types are added. What is the best schema design?

Accepted Answer

Store the sensor data in a JSON column. Storing sensor data in a JSON column leverages BigQuery's native support for semi-structured data (the `JSON` data type), allowing you to ingest records with varying fields without schema changes. This minimizes storage costs by avoiding the overhead of many NULL columns and eliminates the need for schema maintenance when new sensor types are added, as BigQuery can query JSON fields directly using functions like `JSON_EXTRACT` or dot notation.

Answer

Use a separate table per sensor type

Answer

Use a schema with a STRUCT containing all possible fields as optional

Answer

Use a wide table with many nullable columns

Question 7

A BI report requires a running total of sales over the last 30 days for each product. The data is in a BigQuery table with columns: sale_date, product_id, amount. Which SQL window function is most efficient?

Accepted Answer

Use SUM(amount) OVER (ORDER BY sale_date ROWS BETWEEN 30 PRECEDING AND CURRENT ROW). The most efficient among the given choices for a 30-day rolling sum, because it uses a window function with `ROWS BETWEEN 30 PRECEDING AND CURRENT ROW` to limit the sum to the last 30 rows. Although a true time-based window would require `RANGE BETWEEN INTERVAL 29 DAY PRECEDING AND CURRENT ROW` with `PARTITION BY product_id`, that is not offered. Option C gives a cumulative sum (unbounded) which is not a 30-day total. Options A and D are set-based or inefficient. Thus B is the best available.

Answer

Use GROUP BY with SUM(amount)

Answer

Use SUM(amount) OVER (ORDER BY sale_date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW)

Answer

Use a correlated subquery to sum over previous dates

Question 8

An organization uses Cloud Build and wants to inject a secret API key into a build step without exposing it in the cloudbuild.yaml. Which approach should they use?

Accepted Answer

Use `secretEnv` to reference a Secret Manager secret. Cloud Build's `secretEnv` field allows you to reference a secret stored in Secret Manager and inject it as an environment variable into a build step. This approach ensures the secret value is never exposed in the `cloudbuild.yaml` file or build logs, as Cloud Build retrieves it securely at runtime using the Secret Manager API.

Answer

Store the key in a Cloud Build substitution variable

Answer

Pass the key via `args` with a substitution

Answer

Store the key in a Cloud Storage bucket and download it in the build

Question 9

You notice that your Cloud SQL for MySQL instance's storage is filling up quickly. You have enabled automatic storage increase, but you want to manually increase the storage size by 50 GB to avoid any risk of reaching the limit. The current disk size is 200 GB. What is the correct gcloud command to resize the disk?

Accepted Answer

gcloud sql instances patch my-instance --storage-size 250. The `gcloud sql instances patch` command is used to modify an existing Cloud SQL instance, and the `--storage-size` flag expects the value in GB as an integer (without the 'GB' suffix). Specifying `250` correctly increases the storage from 200 GB to 250 GB.

Answer

gcloud sql instances patch my-instance --storage-size 250GB

Answer

gcloud sql instances update my-instance --storage-size 250

Answer

gcloud sql instances resize my-instance 250

Question 10

A financial BI application stores monetary values such as revenue and tax amounts. Which BigQuery data type should be used to ensure accuracy in calculations?

Accepted Answer

Use NUMERIC or BIGNUMERIC. NUMERIC and BIGNUMERIC are exact numeric types with fixed precision and scale, designed to avoid floating-point rounding errors. In BigQuery, monetary calculations require exact decimal arithmetic, and these types provide up to 38 (NUMERIC) or 76 (BIGNUMERIC) digits of precision, ensuring accuracy for revenue and tax computations.

Answer

Use STRING and parse numbers as needed

Answer

Use INT64 and store amounts in cents

Answer

Use FLOAT64