PCDE Practice Exam 55 — 20 Questions

Question 1

A company stores log files in Cloud Storage buckets. The logs are accessed frequently for the first 30 days, then rarely for the next 6 months, after which they must be archived for 7 years. They want to minimize storage costs. Which two actions should they take? (Choose two.)

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

Accepted Answer

Set a lifecycle rule to change storage class to Nearline after 30 days.. Option B is correct because Nearline storage is designed for data accessed less than once per month, so transitioning objects to Nearline after the initial 30-day frequent-access period matches the "rarely accessed for the next 6 months" requirement while lowering cost compared to Standard. Option E is correct because Archive storage is the lowest-cost class intended for data retained for long periods (minimum 365 days) and accessed at most once a year, which fits the 7-year archival requirement after the 6-month Nearline window. Option A is wrong because keeping objects in Standard for the entire retention period ignores the colder access patterns and fails to minimize storage costs. Option C is wrong because deleting objects after 30 days violates the requirement to retain the logs for 7 years. Option D is wrong because Autoclass manages transitions automatically based on access patterns and does not give the deterministic, scheduled 30-day and 6-month transitions the scenario requires.

Answer

Keep objects in Standard storage class for the entire retention period.

Answer

Set a lifecycle rule to delete the objects after 30 days.

Answer

Enable Autoclass to automatically transition objects to colder storage classes.

Question 2

An organization is implementing GitOps using Config Sync. They have a Git repository containing Kubernetes manifests for multiple GKE clusters. They want to ensure that only authorized engineers can modify the configuration and that changes are automatically applied to clusters. What is the recommended way to secure Config Sync?

Accepted Answer

Store the Git repository in Cloud Source Repositories and use IAM to control access. Use branch protection rules to require pull request approvals before merging to the main branch.. Storing the Git repository in Cloud Source Repositories and using IAM to control access, combined with branch protection rules requiring pull request approvals, is the recommended way to secure Config Sync. This ensures only authorized engineers can modify configurations, and changes are automatically applied via Config Sync's GitOps model. IAM provides fine-grained access control, and branch protection enforces review, aligning with security best practices.

Answer

Use a public Git repository and rely on Kubernetes RBAC to control who can modify Config Sync objects.

Answer

Use a separate Git repository for each cluster and restrict access via SSH keys.

Answer

Encrypt the Kubernetes secrets in the repo and use a KMS key to decrypt them in the cluster.

Question 3

A company is building a business intelligence dashboard on BigQuery to analyze daily sales data. The table contains a TIMESTAMP column 'order_ts' and a string column 'region'. The BI team frequently filters by month and region. Which table design best optimizes query performance and cost?

Accepted Answer

Partition the table by date (month) and cluster by region. Partitioning the table by month (using the DATE_TRUNC function on order_ts) allows BigQuery to prune entire partitions when filtering by month, reducing the amount of data scanned and thus lowering cost. Clustering by region further organizes data within each partition, enabling efficient block-level pruning for region filters. This combination optimizes both query performance and cost for the BI team's common filter pattern.

Answer

Use a separate table for each region

Answer

Clustering by order_ts and region without partitioning

Answer

Partition the table by region and cluster by order_ts

Question 4

A data engineering team wants to monitor BigQuery query performance and slot utilization. Which TWO tools or features should they use? (Choose two.)

Accepted Answer

INFORMATION_SCHEMA tables (e.g., JOBS_BY_PROJECT). Option A, INFORMATION_SCHEMA tables such as JOBS_BY_PROJECT, is correct because these views expose per-job metadata including query text, elapsed/execution time, bytes processed, slot-milliseconds consumed, and errors, which is exactly what is needed to analyze BigQuery query performance and slot usage historically. Option B, Cloud Monitoring metrics like 'bigquery.googleapis.com/scheduler/slot_allocation', is correct because Cloud Monitoring surfaces BigQuery slot allocation, slot utilization, and query execution time as time-series metrics, enabling dashboards and alerts on slot consumption and performance trends. Option C, Cloud Logging audit logs, is not the right fit because audit logs record administrative and data-access events (who did what) rather than detailed query performance and slot-utilization metrics. Option D, the bq command-line tool with '--format=prettyjson', only changes output formatting of bq results and does not provide performance or slot monitoring. Option E, Cloud Profiler, is a continuous CPU/heap profiler for application code and has no visibility into BigQuery query execution or slot usage.

Answer

Cloud Logging (audit logs)

Answer

bq command-line tool with the '--format=prettyjson' flag

Answer

Cloud Profiler

Question 5

A company is designing a Cloud Spanner database for a global user base. They need to support strong consistency and low-latency reads across multiple regions. Which schema design practice is most important?

Accepted Answer

Use interleaved tables to co-locate related rows that are queried together.. Interleaved tables in Cloud Spanner physically co-locate parent and child rows on the same split, enabling local joins with strong consistency and low latency across regions. This design minimizes cross-node communication, which is critical for global workloads that require both strong consistency and fast reads.

Answer

Denormalize data into wide tables to reduce the number of joins.

Answer

Use a single table with composite primary key to avoid joins.

Answer

Create secondary indexes on every column to optimize read queries.

Question 6

You run the above command to create a Spanner instance. Later, you need to increase the instance's compute capacity to handle higher traffic. What is the correct approach?

Accepted Answer

Run gcloud spanner instances update test-instance --nodes=5. Cloud Spanner allows you to increase the compute capacity of an existing instance by updating the node count using the `gcloud spanner instances update` command. This operation is performed online without downtime, as Spanner supports live resizing of nodes to handle increased traffic.

Answer

Use the console to change the instance configuration to a larger one.

Answer

Delete the instance and recreate with --nodes=5.

Answer

Create a new instance with a larger config and migrate data.

Question 7

A site reliability engineer needs to alert when the error budget burn rate exceeds 14x the budget over a 1-hour window. Which type of alert should be configured in Cloud Monitoring?

Accepted Answer

Fast burn rate alert with a 1-hour window and 14x burn rate. A fast burn rate alert with a 1-hour window and 14x burn rate directly matches the requirement to alert when the error budget burn rate exceeds 14x over a 1-hour window. This is a standard multi-window, multi-burn-rate alerting pattern recommended by Google SRE. It detects rapid consumption of the error budget.

Answer

Error budget burn rate alert with a 30-day window and 1x burn rate

Answer

Slow burn rate alert with a 6-hour window and 5x burn rate

Answer

SLI threshold alert when latency exceeds 500 ms

Question 8

An e-commerce platform uses Cloud Bigtable for session data. They need to ensure that if one zone fails, the data is still available with eventual consistency. What should they configure?

Accepted Answer

Enable Bigtable replication across zones within the same region. Bigtable replication lets you configure a cluster in a different zone (or region) that receives asynchronous, eventually consistent copies of the data, so a single-zone failure does not take the data offline. This directly matches the requirement for zone-failure tolerance with eventual consistency.

Answer

Create a Cloud Bigtable cluster with multiple nodes

Answer

Export Bigtable tables to Cloud Storage periodically

Answer

Use Cloud Bigtable HDD storage for durability

Question 9

Your Cloud Spanner database has a table with a secondary index that is used for range queries. You notice that the index queries are slow because they require back-and-forth between the index and the base table. How can you optimize the index to reduce this overhead?

Accepted Answer

Use the STORING clause to include frequently queried columns in the index.. In Cloud Spanner, you can store additional columns from the base table in the secondary index using the STORING clause. This allows the index to satisfy queries without accessing the base table (covering index). INTERLEAVE IN PARENT for the index can also improve locality if the index is interleaved with a parent table. The question asks to reduce overhead of back-and-forth, which is solved by storing columns in the index.

Answer

Convert the table to use hash-prefixed keys.

Answer

Create a new interleaved table and move the index data there.

Answer

Use the spanner_interleave_in_parent option when creating the index.

Question 10

An application running on GKE needs to send custom metrics to Cloud Monitoring. The team wants to use a vendor-neutral instrumentation approach that also supports traces and logs. Which solution should they choose?

Accepted Answer

Use OpenTelemetry SDK and OTel Collector to export to Cloud Monitoring and Cloud Trace. OpenTelemetry is the vendor-neutral, CNCF-graduated standard for instrumenting applications to emit metrics, traces, and logs via a single SDK. The OTel Collector then acts as a pipeline that can export these signals to Cloud Monitoring (metrics) and Cloud Trace (traces) using Google's exporters, satisfying the requirement for a unified, portable instrumentation approach. This avoids lock-in to any single vendor's proprietary SDK while still integrating natively with Google Cloud Observability.

Answer

Use the Cloud Monitoring API directly from the application to write metrics

Answer

Use Prometheus client library and scrape metrics with Managed Service for Prometheus

Answer

Use the Stackdriver Monitoring client library

Question 11

An application running on Cloud SQL experiences high read latency. The team wants to offload read traffic from the primary instance and improve performance. Which approach should they take?

Accepted Answer

Create a single read replica and route read-only queries to it. Cloud SQL read replicas allow distributing read traffic, reducing load on the primary instance and improving read performance.

Answer

Add more memory to the primary instance

Answer

Enable the query cache

Answer

Use Cloud SQL Auth Proxy

Question 12

A company runs a batch data processing job on Compute Engine that is fault-tolerant. They want to reduce costs without affecting job completion time. The job can handle instance preemption gracefully. Which compute option should they use?

Accepted Answer

Use preemptible VMs.. Preemptible VMs are significantly cheaper and suitable for fault-tolerant batch jobs because they can be interrupted but the job can resume on new instances.

Answer

Use regular VMs with committed use discounts for 1 year.

Answer

Use GPU-accelerated instances.

Answer

Use sole-tenant nodes.

Question 13

A company uses Cloud Profiler to identify performance bottlenecks. They notice that the CPU profile shows a function consuming high CPU, but they want to see the corresponding source code lines. What must they enable?

Accepted Answer

Enable debug symbols in the application build. Cloud Profiler can show source code lines in profiles only if the application binary contains debug symbols. Enabling debug symbols in the build (e.g., compiling with -g flag) allows the profiler to map memory addresses to function names and line numbers. Without debug symbols, the profiler can still show function-level CPU usage but cannot resolve specific source lines.

Answer

Enable flame graphs in the Profiler settings

Answer

Instrument the application with OpenTelemetry SDK

Answer

Increase the Profiler sampling rate

Question 14

A team wants to inject latency faults into their microservices running on Google Kubernetes Engine (GKE) to test resilience. Which tool or service should they use?

Accepted Answer

Chaos Mesh. Chaos Mesh is a Kubernetes-native chaos engineering platform that supports injecting latency, packet loss, and other faults into pods and services. It integrates with GKE through custom resource definitions (CRDs) and is the appropriate tool for deliberately adding latency to microservices to test resilience. It is open source and designed specifically for this purpose.

Answer

Cloud Build to orchestrate failure scenarios

Answer

Traffic Director fault injection with HTTP fault filter

Answer

Cloud Functions to simulate latency by delaying responses

Question 15

A team defines an SLO for a data pipeline: 99.9% of data records should be processed within 1 hour of ingestion. They need an SLI to measure this. Which SLI is most appropriate?

Accepted Answer

Pipeline freshness. The SLO specifies that 99.9% of data records should be processed within 1 hour of ingestion. This is a timeliness requirement, which is best measured by pipeline freshness—the age of the oldest unprocessed record or the time since the last successfully processed record. Freshness directly quantifies whether records are being processed within the 1-hour window, making it the most appropriate SLI.

Answer

Throughput

Answer

Error rate

Answer

Request latency

Question 16

A DevOps team uses Terraform Cloud to manage infrastructure. They want to enforce that all Terraform plans must pass a set of policy checks before they can be applied. The policies include restricting resource types and ensuring proper tagging. Which Terraform Cloud feature should they use?

Accepted Answer

Use Terraform Cloud's Sentinel policies.. Sentinel is Terraform Cloud's policy-as-code framework. It allows writing policies (in Sentinel language) that are evaluated during the plan phase. Policies can enforce constraints on resources and tags.

Answer

Use Terraform Cloud's Cost Estimation feature.

Answer

Use Terraform Cloud's Run Triggers to chain workspaces.

Answer

Use Terraform Cloud's API to run custom scripts after plan.

Question 17

A DevOps engineer wants to visualize the 99th percentile latency of an HTTP endpoint over the past week using Cloud Monitoring dashboards. The metric is available as a distribution from Cloud Trace. Which chart type should they use to display this percentile over time?

Accepted Answer

Line chart. A line chart is the correct choice for displaying a percentile (such as p99) of a distribution metric over time, because it plots a single aggregated value per time interval on a continuous time axis, making trends easy to read. Cloud Monitoring supports percentile aggregation (e.g., p99) on distribution metrics, and line charts are the standard visualization for time-series percentiles.

Answer

Heatmap

Answer

Stacked bar chart

Answer

Scatter chart

Question 18

A company is running a Cloud SQL for PostgreSQL database and wants to improve read performance for reporting queries. They have already optimized the queries but still see high CPU usage on the primary instance. What is the most cost-effective solution?

Accepted Answer

Add a read replica and direct reporting traffic to it.. Read replicas offload read traffic from the primary instance, reducing CPU usage without requiring a larger primary instance.

Answer

Enable connection pooling with PgBouncer on the primary.

Answer

Increase the memory of the primary instance.

Answer

Create a Cloud SQL Auth Proxy and use it for all connections.

Question 19

A Cloud Run service experiences cold starts on the first request after being idle, causing latency spikes. The team wants to eliminate cold starts entirely. Which configuration setting should they use?

Accepted Answer

Set min instances to a value greater than 0. Setting a minimum number of instances (min instances) keeps the specified number of instances always warm, preventing cold starts. CPU always on prevents throttling but does not eliminate cold starts. Max instances limits scale but does not prevent cold starts. Concurrency controls how many requests an instance can handle but does not eliminate cold starts.

Answer

Set CPU to always on

Answer

Set max instances to 1

Answer

Set concurrency to 1

Question 20

A site reliability engineer wants to implement chaos engineering on a Google Kubernetes Engine (GKE) cluster by injecting network latency into pods of a specific deployment. Which tool or service should they use?

Accepted Answer

Chaos Mesh. Chaos Mesh is a Kubernetes-native chaos engineering tool that allows injecting various faults, including network latency, into pods. It integrates with GKE and provides a custom resource definition (CRD) for network chaos, enabling targeted latency injection into specific deployments. This directly meets the requirement.

Answer

GKE Sandbox

Answer

Cloud CDN

Answer

Traffic Director with HTTP fault filter