PCD Quick Quiz — 10 Questions

Question 1

Your application uses Cloud Spanner with strong reads, but you are experiencing high latency for read requests. You don't need absolute consistency for every read; stale data up to 5 seconds is acceptable. How can you reduce read latency?

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

Accepted Answer

Use bounded staleness reads with a max staleness of 5 seconds. Using bounded staleness allows reads to be served from replicas that may be slightly stale (up to 5 seconds), reducing latency. Read timestamps and DML are not relevant, and global indexes would not help.

Answer

Use the read timestamp to read the latest data

Answer

Create a global secondary index on frequently read columns

Answer

Use DML instead of mutations for writes

Question 2

A company wants to run hybrid transactional/analytical processing (HTAP) workloads on a PostgreSQL-compatible database with built-in columnar engine for faster analytical queries. Which Google Cloud database should they choose?

Accepted Answer

AlloyDB. AlloyDB is a PostgreSQL-compatible database service with a built-in columnar engine that accelerates analytical queries by storing data in columns rather than rows, enabling HTAP workloads without needing to move data between separate transactional and analytical systems. Its architecture decouples compute from storage and uses a columnar cache to deliver up to 100x faster analytical queries than standard PostgreSQL, making it the correct choice for HTAP on Google Cloud.

Answer

Cloud SQL for PostgreSQL

Answer

Cloud Spanner

Answer

BigQuery

Question 3

An engineer notices high CPU utilization on a Cloud Spanner instance and wants to identify the queries consuming the most resources. Which tool should they use?

Accepted Answer

Query Insights for Spanner.. Cloud Spanner's Query Insights provides query performance statistics, including CPU usage, latency, and execution counts. It helps identify problematic queries. Stackdriver (Cloud Monitoring) shows overall metrics but not per-query details. Key Visualizer is for Bigtable. Cloud Logging can show logs but not specific query resource consumption.

Answer

Cloud Monitoring (Stackdriver) dashboards for Spanner.

Answer

Key Visualizer for Bigtable.

Answer

Cloud Logging with query logs.

Question 4

A company is designing a cloud-native application on Google Kubernetes Engine. They want to ensure high availability and scalability for their microservices. Which two best practices should they follow?

Accepted Answer

Use horizontal pod autoscaling based on custom metrics.. Horizontal Pod Autoscaling (HPA) based on custom metrics allows the application to automatically scale the number of pod replicas in response to application-specific signals (e.g., requests per second, queue depth) rather than just CPU/memory. This ensures that each microservice can handle varying load efficiently, maintaining high availability and scalability without over-provisioning.

Answer

Use a single cluster per region.

Answer

Use a single replica for each service to reduce cost.

Answer

Use stateful sets for all services.

Question 5

A developer runs the command above. What is the effect of the --promote flag in this deployment?

Accepted Answer

It causes the new version (v2) to receive 100% of traffic after deployment.. The --promote flag in a Google Cloud App Engine deployment command (gcloud app deploy --promote) causes the newly deployed version (v2) to immediately receive 100% of traffic, effectively making it the default serving version. This is the standard behavior unless the --no-promote flag is explicitly used to keep traffic on the previous version.

Answer

It creates a new default service version with split traffic.

Answer

It promotes the previous version to receive traffic.

Answer

It enables automatic scaling for the new version.

Question 6

An organization is migrating a Teradata data warehouse to BigQuery. They want to minimize manual SQL rewriting. Which approach is most effective for converting Teradata SQL to BigQuery-compatible SQL?

Accepted Answer

Manually rewrite all Teradata SQL queries to BigQuery syntax.. BigQuery Data Transfer Service for Teradata only migrates data and schema, not SQL queries. To convert Teradata SQL to BigQuery-compatible syntax, manual rewriting is required or use of a dedicated SQL conversion tool. Option C correctly identifies this approach, as manual rewriting directly addresses SQL conversion challenges.

Answer

Use BigQuery Data Transfer Service for Teradata to automate migration.

Answer

Use gcloud command-line tool to export Teradata data and import into BigQuery.

Answer

Use Cloud SQL to create a linked server to Teradata.

Question 7

A team uses Cloud Endpoints to manage their API. They want to monitor API latency for each API method. What is the recommended approach?

Accepted Answer

View the built-in Cloud Endpoints latency metrics in Cloud Monitoring.. Cloud Endpoints automatically sends metrics including request latency per method to Cloud Monitoring. Cloud Trace can trace individual requests but not aggregate per method easily. Custom metrics require code changes. Cloud Logging latency is not built-in.

Answer

Parse Cloud Logging endpoint logs to calculate latency.

Answer

Use Cloud Trace to analyze samples and estimate latency.

Answer

Instrument the API code with a custom metric for each method.

Question 8

Which TWO are best practices for setting up Cloud Monitoring alerting policies to minimize alert fatigue? (Select exactly 2.)

Accepted Answer

Use condition thresholds with an 'AND' combination of multiple metrics.. Using multiple conditions with AND logic reduces false positives. Setting 'for' duration prevents transient spikes from alerting. Aggregating across projects first is not best practice; it's better to alert per project. Using log-based metrics for everything is not always appropriate. Synthetic monitors are for availability, not general alerting.

Answer

Aggregate metrics across all projects before alerting.

Answer

Use log-based metrics for all alerts instead of metric-based alerts.

Answer

Create a separate alerting policy for each possible symptom.

Question 9

A developer wants to quickly test changes to a containerized web application that will run on Cloud Run, without building and deploying a new container. Which approach should they use?

Accepted Answer

Run locally with Docker. Running locally with Docker allows rapid iteration without the overhead of building and pushing to a registry and redeploying. Staging deployment is slower. Traffic splitting is for production traffic management. Cloud Run for Anthos is for hybrid deployments.

Answer

Deploy to a staging Cloud Run service

Answer

Use traffic splitting to test a new revision

Answer

Use Cloud Run for Anthos

Question 10

Refer to the exhibit. A company configured an HPA for their deployment. They notice that the HPA is not scaling based on the 'packets-per-second' metric. What is the most likely reason?

Accepted Answer

The metric is not available in the cluster.. The 'packets-per-second' metric is a custom metric. If it is not registered in the cluster's metrics server (e.g., via custom metrics adapter), the HPA will not be able to collect it. The metric name and target type are correct. The scaleTargetRef matches the deployment. Therefore, the metric being unavailable is the most likely issue.

Answer

The metric name 'packets-per-second' is incorrect.

Answer

The target type should be 'Value' instead of 'AverageValue'.

Answer

The HPA is using the wrong scaleTargetRef.