PCDE Timed Practice Test 2 — 10 Questions

Question 1

Order the steps to migrate an on-premises MySQL database to Cloud SQL using Database Migration Service (DMS).

Question 2

Arrange the steps to import data from Cloud Storage into Cloud Firestore using a managed import.

Question 3

A company is migrating an on-premises Oracle database to Cloud SQL for PostgreSQL. The database is 2 TB in size and the network bandwidth to Google Cloud is limited to 500 Mbps. The migration window is 48 hours. Which migration strategy should the Database Engineer recommend?

Accepted Answer

Export the database to flat files, compress, upload to Cloud Storage, then import to Cloud SQL.. Option C is correct because the 2 TB database size and 500 Mbps bandwidth yield a theoretical transfer time of approximately 9.5 hours (2 TB * 1024 GB/TB * 8 bits/byte / 500 Mbps / 3600 seconds/hour), which fits within the 48-hour window. However, pg_dump/pg_restore over a VPN (Option A) would be slower due to TCP overhead and latency, and Database Migration Service with continuous replication (Option B) requires ongoing connectivity and may not complete the initial load within the window. Exporting to flat files, compressing them (e.g., with gzip), uploading to Cloud Storage, and then importing to Cloud SQL leverages high-throughput parallel uploads and avoids network latency issues, making it the most reliable strategy for a one-time migration within the given constraints.

Answer

Create a VPN tunnel and use pg_dump/pg_restore over the network.

Answer

Use Database Migration Service with continuous replication.

Answer

Request a dedicated interconnect and then migrate.

Question 4

A company runs a production Cloud SQL for PostgreSQL instance used by a web application. The instance experiences intermittent latency spikes during peak hours. You need to diagnose the cause without downtime. Which tool should you use first?

Accepted Answer

Use Cloud SQL Insights to analyze query performance and identify slow queries.. Cloud SQL Insights provides built-in query performance monitoring and diagnostics without requiring any downtime. It surfaces slow queries, lock contention, and resource bottlenecks directly from the PostgreSQL engine, making it the ideal first step to identify the root cause of intermittent latency spikes during peak hours.

Answer

Use Database Migration Service to failover to a read replica.

Answer

Use gcloud sql instances describe to check instance configuration.

Answer

Use VPC Flow Logs to analyze network traffic.

Question 5

A financial services company uses Cloud Spanner for transaction processing. They need to ensure zero downtime during a schema change that adds a new column with a default value to a large table. Which approach should the Database Engineer take?

Accepted Answer

Use ALTER TABLE to add the column; Spanner handles schema changes online.. Option D is correct because Cloud Spanner supports online schema changes, including adding columns with default values, without requiring a maintenance window or causing downtime. Spanner applies schema updates asynchronously across all nodes while the database remains fully available for reads and writes, making it the ideal approach for zero-downtime requirements.

Answer

Create a new table with the new column, then use a fan-out pattern to write to both tables until the old table is deprecated.

Answer

Use an ALTER TABLE statement during a maintenance window.

Answer

Drop the table and recreate it with the new schema.

Question 6

Your company runs a global application on Cloud Spanner. You notice that recent schema changes have caused a significant increase in latency for cross-node transactions. The previous schema used interleaved tables for parent-child relationships, but the new schema uses separate tables with foreign keys. What is the most likely cause of the increased latency?

Accepted Answer

The new schema uses foreign keys that require cross-node transactions.. The new schema uses separate tables with foreign keys instead of interleaved tables. In Cloud Spanner, interleaved tables guarantee that parent and child rows are co-located on the same split, allowing local joins without cross-node communication. Foreign keys between non-interleaved tables can reference rows stored on different splits, forcing distributed transactions that require two-phase commit across nodes, which significantly increases latency.

Answer

The new schema does not use commit timestamps for versioning.

Answer

The new schema lacks secondary indexes on foreign key columns.

Answer

The Spanner instance was not resized after the schema change.

Question 7

A financial services company uses BigQuery to run complex analytical queries on trading data. They notice that a particular query joining a large fact table (10 TB) with a small dimension table (100 MB) is slow. The fact table is partitioned by date and clustered by symbol. The dimension table is not partitioned. The query filters on a specific date range and a few symbols. Which optimization is MOST likely to improve query performance?

Accepted Answer

Enable automatic query rewriting to use clustering keys for pruning on the dimension table join.. Option B is correct because BigQuery's automatic query rewriting can leverage clustering keys from the fact table to prune the join, even though the dimension table is not clustered. When the query filters on a specific date range and symbols, BigQuery can use the fact table's clustering on symbol to skip irrelevant blocks during the join, reducing data scanned and improving performance. This optimization is automatic and does not require manual denormalization or repartitioning.

Answer

Denormalize the dimension table into the fact table.

Answer

Partition the dimension table by its primary key.

Answer

Cluster the dimension table on its primary key.

Question 8

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.. Option B is correct because a star schema with a fact table for sales and dimension tables for region and product is optimized for analytical queries in BigQuery. Option A is wrong because a flat table with all columns leads to higher storage costs and slower queries due to scanning unnecessary columns. Option C is wrong because a wide table with nested columns is better for hierarchical data, not for simple dimensional analysis. Option D is wrong because a normalized schema with many joins is not ideal for BI queries and increases complexity.

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 9

A team is designing a schema for a user activity logging system using Bigtable. Each log entry includes a user ID, activity type, timestamp, and details. The access pattern requires retrieving all activities for a specific user within a time range. Which TWO row key designs are suitable? (Choose TWO.)

Accepted Answer

user_id#activity_type#timestamp. Option D (user_id#activity_type#timestamp) is correct because it groups all activities for a user under a single row key prefix, enabling efficient row range scans for a specific user. The activity_type suffix allows filtering by activity type if needed, while the timestamp ensures uniqueness and ordered storage. Option E (user_id#timestamp) is correct because it directly supports the access pattern of retrieving all activities for a user within a time range by scanning rows with the user_id prefix and filtering on the timestamp component.

Answer

timestamp#user_id

Answer

random_uuid

Answer

reverse_timestamp

Question 10

You are running a Memorystore for Redis instance with a high write volume. You notice that the eviction rate is high and the cache hit ratio has dropped significantly. Which configuration change would most directly reduce the eviction rate?

Accepted Answer

Change maxmemory-policy to allkeys-lru.. Option D is correct because setting `maxmemory-policy` to `allkeys-lru` tells Redis to evict the least recently used keys from the entire keyspace when memory is full. This directly reduces the eviction rate by ensuring that only the least accessed data is removed, preserving the most frequently accessed data and improving the cache hit ratio. In a high-write-volume scenario, this policy adapts to access patterns and minimizes unnecessary evictions of hot data.

Answer

Use many small keys instead of a few large keys.

Answer

Decrease the maxmemory setting to force earlier eviction.

Answer

Set maxmemory-policy to noeviction.