Google PCA Practice Test 23 — 15 Questions

Question 1

A team uses BigQuery for analytics. They notice that queries against a table with billions of rows are slow and expensive. The table is partitioned by ingestion time and has no clustering. Queries frequently filter on a 'customer_id' column. Which optimization would MOST reduce query cost and latency?

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

Accepted Answer

Cluster the table on customer_id. Clustering on 'customer_id' will physically co-locate rows with the same customer_id within each partition. This allows BigQuery to prune blocks, reducing the amount of data scanned for queries that filter on customer_id. Partitioning alone is not enough; clustering on the filter column is key.

Answer

Use a materialized view pre-aggregated by customer_id

Answer

Switch to on-demand pricing

Answer

Add a clustered index on customer_id

Question 2

A security team wants to ensure that all Compute Engine instances in a project are launched with a specific custom encryption key (CMEK) stored in Cloud KMS. What is the correct way to enforce this policy?

Accepted Answer

Use the organization policy constraint constraints/compute.requireCmek.. Using organization policy constraints, specifically constraints/compute.requireCmek, enforces that all new disks are encrypted with a CMEK from Cloud KMS. Other options do not enforce encryption at a project level.

Answer

Set a service account with permissions to use the CMEK.

Answer

Create a firewall rule to block unencrypted traffic.

Answer

Configure the VPC Service Perimeter to block non-CMEK instances.

Question 3

A company wants to optimise costs for their BigQuery workloads. They have a predictable set of queries that run every hour, with occasional ad-hoc queries from analysts. They want to reduce the cost of the scheduled queries while keeping the ad-hoc queries cost-effective. Which pricing model should they choose?

Accepted Answer

Use Editions with a baseline of reserved slots for scheduled queries and allow autoscaling for ad-hoc. BigQuery slot reservations (editions) allow allocating dedicated slots for predictable workloads, with separate pricing for ad-hoc queries (on-demand). Flat-rate pricing is cheaper for large steady-state usage but may waste slots during idle time. On-demand is pay-per-query; for predictable queries, it may be expensive. The best approach is to use a hybrid: reserved slots for scheduled queries and on-demand for ad-hoc.

Answer

On-demand pricing

Answer

Use committed use discounts on slots

Answer

Flat-rate pricing (slot reservations) for all queries

Question 4

A company's BigQuery costs are higher than expected. They run many ad-hoc queries with filters on the 'transaction_date' column and 'customer_id' column. They also have a materialized view that is rarely used. Which combination of actions will MOST effectively reduce query costs?

Accepted Answer

Partition on transaction_date, cluster on customer_id, and drop the unused materialized view. Partitioning by transaction_date reduces scanned data for date filters. Clustering by customer_id further reduces bytes billed for queries filtering on that column. Dropping unused materialized views avoids storage costs.

Answer

Convert to a clustered table on transaction_date and disable caching

Answer

Use flat-rate pricing for all queries and cluster on transaction_date

Answer

Partition on customer_id and use materialized views for all queries

Question 5

A company wants to set a monthly spending limit for their Compute Engine usage and receive alerts when spending exceeds a threshold. Which tool should they use?

Accepted Answer

Cloud Budget alerts. Budget alerts in Cloud Billing allow you to set a spending budget and receive notifications when actual spending exceeds thresholds. They can be scoped to projects or services.

Answer

Cloud Monitoring

Answer

Cloud Logging

Answer

Cloud Armor

Question 6

A company has a BigQuery dataset with a growing fact table (500 million rows, added daily). Queries that filter on a date column and group by a product ID are slow. The team wants to optimise query performance without increasing slot costs. Which two actions should they take? (Choose TWO.)

Accepted Answer

Cluster the table by the product ID column. Partitioning by date and clustering by product ID can significantly improve query performance: partitioning prunes scans to relevant days, and clustering co-locates rows with similar product IDs. Materialised views pre-aggregate data but increase storage costs. SELECT * is inefficient and should be avoided. Slots pricing does not help performance without changing configuration.

Answer

Switch to BigQuery Editions with Autoscaling slots

Answer

Use SELECT * only when necessary

Answer

Create a materialised view that pre-aggregates the data

Question 7

A company is designing a disaster recovery plan for a critical application running on Compute Engine. The application requires a Recovery Point Objective (RPO) of 15 minutes and a Recovery Time Objective (RTO) of 4 hours. Which TWO strategies meet these requirements? (Choose 2)

Accepted Answer

Warm standby: run a scaled-down version of the environment in another region. Pilot light involves replicating data and having a minimal environment ready to scale. Warm standby maintains a partially scaled environment. Both can achieve RPO of 15 minutes and RTO of 4 hours with proper setup. Multi-region deployment and active-passive are not defined in terms of RPO/RTO.

Answer

Multi-region deployment with Cloud Load Balancing

Answer

Backup and restore: daily backups to Cloud Storage

Answer

Cold standby: periodic snapshots to another region

Question 8

A company needs to transfer 200 TB of data from on-premises to Cloud Storage within one week. The on-premises network upload speed is 100 Mbps. Which method should they use?

Accepted Answer

Order a Transfer Appliance. 200 TB at 100 Mbps = 200 * 1024 * 8 / (100/8) seconds ≈ 1,620,000 seconds ≈ 19 days, which exceeds one week. So network transfer is too slow. Transfer Appliance is a physical device for fast offline transfer. Storage Transfer Service is for cloud-to-cloud. gsutil rsync is an online tool. Velostrata is for VM migration.

Answer

Use Storage Transfer Service

Answer

Use gsutil rsync to transfer the data

Answer

Use Velostrata (Migrate for Compute Engine)

Question 9

A company is migrating 50 on-premises VMs to Compute Engine. They need to minimise downtime and want an automated lift-and-shift migration that replicates disks incrementally. Which Google Cloud service should be used?

Accepted Answer

Migrate for Compute Engine. Migrate for Compute Engine (formerly Velostrata) performs agentless, incremental replication of VM disks to Compute Engine, enabling minimal downtime migrations.

Answer

Database Migration Service

Answer

Storage Transfer Service

Answer

Transfer Appliance

Question 10

A company wants to reduce costs for a batch analytics job that runs nightly for 4 hours on Compute Engine VMs. The job is fault-tolerant and can handle instance restarts. Which Compute Engine VM pricing model is MOST cost-effective?

Accepted Answer

Preemptible VMs. Preemptible VMs offer the lowest cost (up to 80% discount) and are ideal for fault-tolerant, short-lived batch workloads that can handle interruptions. Sustained use discounts apply automatically but require running a VM for at least 25% of a month. Committed use discounts require a 1- or 3-year commitment and are not as flexible for a short nightly job.

Answer

3-year committed use discount (CUD)

Answer

1-year committed use discount (CUD)

Answer

Sustained use discounts

Question 11

An organization wants to reduce costs for a batch data processing job that runs nightly and is resilient to interruptions. The job can be restarted from checkpoints. Which Compute Engine VM pricing model should be used?

Accepted Answer

Preemptible VMs. Preemptible VMs and Spot VMs are significantly cheaper than standard VMs and can be terminated by Google Cloud at any time. Since the job is batch and can resume from checkpoints, interruptions are acceptable. Sustained use discounts apply automatically to standard VMs, but preemptible/spot VMs offer the lowest cost for fault-tolerant workloads.

Answer

On-demand VMs

Answer

Sustained use discounts

Answer

Committed use discounts (1-year)

Question 12

An organization is planning to move 500 TB of archival data from on-premises to Cloud Storage. The data is not frequently accessed, and the network bandwidth is limited to 100 Mbps. What is the most efficient migration approach?

Accepted Answer

Use Transfer Appliance. Transfer Appliance is a physical device for shipping large amounts of data when bandwidth is low. At 100 Mbps, 500 TB would take over 500 days; Transfer Appliance bypasses network constraints.

Answer

Use gsutil rsync with parallel composite uploads

Answer

Use Migrate for Compute Engine

Answer

Use Storage Transfer Service over the internet

Question 13

A company wants to use BigQuery with a predictable monthly cost, regardless of query volume. They have a steady state of around 500 concurrent slots. Which pricing model should they choose?

Accepted Answer

Slot reservations (flat-rate). BigQuery slot reservations (flat-rate) provide a fixed monthly cost based on reserved slots, suitable for predictable workloads. On-demand pricing charges per query and can vary.

Answer

Sustained use discounts

Answer

Committed use discounts for BigQuery

Answer

On-demand pricing

Question 14

A company wants to monitor the performance of their application against a Service Level Objective (SLO) of 99.9% availability over a 30-day rolling window. They plan to use Google Cloud's operations suite (formerly Stackdriver). Which metric should they use as the Service Level Indicator (SLI) for availability?

Accepted Answer

Successful request ratio. Availability is typically measured as the fraction of successful requests (e.g., HTTP 200-499) vs total requests. The other options are valid SLIs for other aspects: latency, error rate, or saturation.

Answer

Error rate

Answer

Request latency

Answer

CPU utilization

Question 15

An engineer is designing a Bigtable schema for time-series data consisting of sensor readings. Each sensor emits a reading every second. The access pattern is to retrieve all readings for a specific sensor within a time range. Which row key design will provide the best performance?

Accepted Answer

Use row key: [sensor_id]#[reverse_timestamp]. Bigtable stores rows sorted by key. A row key structured as [sensor_id]#[reverse_timestamp] ensures that all data for a sensor is contiguous, and sorting by reverse timestamp allows recent data to be retrieved first. A single row key per sensor with column qualifiers would cause hotspots and limit scalability.

Answer

Use a single row per sensor with column qualifiers as timestamps

Answer

Use timestamp as the row key and sensor ID as column qualifier

Answer

Use a random prefix to distribute writes evenly