PDE Practice Test 35 — 15 Questions

Question 1

A company stores highly sensitive financial data in BigQuery. They need to encrypt certain columns (e.g., credit card numbers) with customer-managed encryption keys (CMEK) at the column level. Which BigQuery feature should they use?

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

Accepted Answer

BigQuery Data Catalog with policy tags. BigQuery supports column-level encryption with customer-managed encryption keys (CMEK) through column-level encryption using policy tags in BigQuery Data Catalog. You create a policy tag, associate a Cloud KMS key with that policy tag, and apply the policy tag to a column. BigQuery then encrypts that column with the customer-managed key. AEAD functions with Cloud KMS provide application-layer encryption of individual values, but they are not the native BigQuery CMEK feature and should not be described as CMEK.

Answer

Customer-managed encryption keys (CMEK) on the dataset

Answer

VPC Service Controls

Answer

AEAD encryption functions with Cloud KMS

Question 2

A company has a Cloud SQL for PostgreSQL instance and wants to create a read replica to offload read traffic from the primary. They also need to ensure the replica is in a different region for disaster recovery. Which Cloud SQL feature should they use?

Accepted Answer

Cross-region read replica. Cross-region read replicas in Cloud SQL for PostgreSQL allow you to create a replica in a different region from the primary instance. This offloads read traffic from the primary while also providing disaster recovery capabilities by maintaining a standby copy in a geographically separate location. The replica uses asynchronous replication to stay up-to-date with the primary.

Answer

External replica

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Automatic backups with PITR

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High availability (HA) configuration

Question 3

A mobile app needs a real-time NoSQL database that supports offline sync and automatic conflict resolution. Which Google Cloud database is best suited?

Accepted Answer

Firestore. Firestore is the correct choice because it is a NoSQL, real-time database with client SDKs that provide offline data persistence and automatic conflict resolution using last-write-wins semantics. Its SDKs synchronize data in the background when connectivity is restored, making it well suited for mobile apps requiring offline-first functionality.

Answer

Cloud SQL

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Cloud Bigtable

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Cloud Spanner

Question 4

A team is designing a Spanner database for a global inventory system. They need to optimize query performance for frequently joined tables. Which THREE design decisions help achieve this? (Choose 3.)

Accepted Answer

Design primary keys to distribute write load evenly across splits.. Option B is correct because Spanner scales by splitting data into ranges, and a monotonically increasing or skewed primary key concentrates writes on a single split; designing keys (for example, using hash prefixes or reversed timestamps) to distribute writes evenly avoids hotspots and keeps join-driving lookups performant. Option C is correct because interleaved tables physically co-locate child rows with their parent row in the same split, so joins between parent and child on the interleaved key prefix are executed locally without network shuffling, dramatically improving join performance. Option E is correct because secondary indexes let Spanner satisfy WHERE-clause predicates by index scan rather than full table scan, reducing the rows read before the join and thus lowering latency and cost. Option A is not appropriate because Cloud SQL is a regional, non-horizontally-scalable relational service and does not provide Spanner's global distribution, external consistency, or interleaving; joins are fully supported in Spanner. Option D is not appropriate because collapsing everything into a single table with JSON columns discards Spanner's relational join and interleaving capabilities, prevents effective secondary indexing on nested fields, and typically worsens performance and schema maintainability.

Answer

Use Cloud SQL instead if joins are needed.

Answer

Store all data in a single table with JSON columns to avoid joins.

Question 5

A company is designing a data lake on Cloud Storage with three zones: raw, curated, and processed. They need to enforce data governance by restricting access to each zone using IAM. Which approach should they take?

Accepted Answer

Create three separate buckets, one per zone, and assign IAM roles per bucket. Cloud Storage buckets are the fundamental access boundary for IAM policies. By creating three separate buckets (raw, curated, processed), you can assign distinct IAM roles (e.g., roles/storage.objectViewer, roles/storage.objectAdmin) per bucket, ensuring that users or service accounts only have access to the specific zone they are authorized for. This approach aligns with the principle of least privilege and avoids the complexity and limitations of IAM conditions or object-level ACLs.

Answer

Create a single bucket with folders for each zone, and use IAM conditions to restrict access

Answer

Use Cloud Storage lifecycle rules to move objects between zones

Answer

Use a single bucket and rely on object ACLs

Question 6

A company building a real-time analytics pipeline with Pub/Sub and Dataflow. Which THREE best practices should they follow?

Accepted Answer

Use event time processing with watermarks and allowed lateness. In streaming pipelines, event time processing with watermarks and allowed lateness is essential for handling out-of-order data. Watermarks track the progress of event time, and allowed lateness specifies how long to wait for late-arriving data before considering it as late, ensuring accurate windowed aggregations.

Answer

Use exactly-once processing for all transforms

Answer

Use event time processing only for batch pipelines

Question 7

A company wants to use Dataproc Metastore to manage metadata for their Spark jobs. Which TWO benefits does Dataproc Metastore provide?

Accepted Answer

High availability with automatic failover. Option B is correct because Dataproc Metastore is a highly available, fully managed service that provides automatic failover across zones, ensuring metadata remains accessible even if a zone fails. Option C is correct because Dataproc Metastore is essentially a fully managed, serverless implementation of the Hive Metastore (HMS), which Spark and other engines use to store and retrieve metadata such as table schemas and partitions. Option A is incorrect because automatic scaling of compute resources is a feature of Dataproc clusters or autoscaling policies, not of the metadata service itself. Option D is incorrect because Dataproc Metastore does not provide native BigQuery integration; it serves Hive/Spark-style metadata via the Hive Metastore Thrift protocol. Option E is incorrect because data lineage tracking is provided by tools like Data Catalog or Dataplex, not by Dataproc Metastore.

Answer

Automatic scaling of compute resources

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Integration with BigQuery

Answer

Built-in data lineage tracking

Question 8

A company uses Eventarc to trigger a Cloud Run service when new objects appear in a GCS bucket. Recently, the Cloud Run service has been failing with 429 errors (too many requests) during high-velocity uploads. They need to handle the load without losing events. What should they do?

Accepted Answer

Configure the Eventarc trigger to send events to a Pub/Sub topic, and have the Cloud Run service pull from Pub/Sub. Sending events to a Pub/Sub topic decouples event production from consumption. Pub/Sub acts as a buffer that can absorb spikes in event volume, and the Cloud Run service can pull messages at its own pace, preventing 429 errors. This also ensures no events are lost, as Pub/Sub retains unacknowledged messages and retries delivery.

Answer

Use Cloud Functions instead of Cloud Run

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Increase the maximum number of retries on the Eventarc trigger

Answer

Increase the Cloud Run service's request timeout

Question 9

A company is migrating their on-premises data warehouse to BigQuery. They have a mix of batch and streaming ingestion. The data team wants to optimize query costs. Which THREE practices should they adopt?

Accepted Answer

Use materialized views for frequently executed aggregations.. Option B is correct because materialized views precompute and store the results of frequently executed aggregations, so repeated queries read the smaller cached result set instead of rescanning the full base tables, directly reducing bytes processed and thus query cost. Option C is correct because partitioning a table by a DATE or TIMESTAMP column enables partition pruning, so queries with filters on that column scan only the relevant partitions rather than the entire table, cutting the data billed per query. Option E is correct because clustering on columns frequently used in filters and joins co-locates related data in storage blocks, allowing BigQuery to skip irrelevant blocks and reduce bytes scanned for those predicates. Option A is not correct because flat-rate pricing changes how slots are billed (a committed capacity model) rather than reducing the bytes processed by queries, so it does not itself optimize query cost for this migration. Option D is not correct because capping concurrent queries or slot capacity is a workload-management throttle, not a cost-optimization practice, and it can even slow throughput without lowering bytes-scanned charges.

Answer

Switch to flat-rate pricing to cap slot usage.

Answer

Limit the number of concurrent queries by setting a maximum slot capacity.

Question 10

An organization needs to store transactional data for a global e-commerce platform with strong consistency across regions and an SLA of 99.999% availability. The application requires SQL semantics with horizontal scaling. Which Google Cloud database should they choose?

Accepted Answer

Cloud Spanner. Cloud Spanner is the correct choice because it provides globally distributed, strongly consistent SQL semantics with horizontal scaling and a 99.999% availability SLA. It uses synchronous replication and the TrueTime API to ensure external consistency across regions, meeting the strict consistency and uptime requirements of a global e-commerce platform.

Answer

Firestore

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Cloud SQL

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Cloud Bigtable

Question 11

You want to optimize BigQuery costs for a large dataset that is frequently queried by time range. You also need to ensure that predictable workloads have dedicated slot capacity. Which TWO strategies should you combine? (Choose 2)

Accepted Answer

Partition the table by date. Option B is correct because partitioning the table by date lets BigQuery prune partitions and scan only the date ranges a query touches, which directly reduces bytes processed and therefore cost for a dataset that is frequently queried by time range. Option C is correct because committed use reservations (capacity commitments) provide dedicated, predictable slot capacity at a discounted rate for steady baseline workloads, satisfying the requirement that predictable workloads have dedicated slots. Option A is not appropriate as a primary cost strategy here because query caching only helps when identical queries are repeated and results are still cached, which does not address time-range pruning or dedicated capacity. Option D is wrong because a materialized view over the entire table would be expensive to maintain and does not target the time-range access pattern. Option E is wrong because autoscaling slots add flexible, on-demand capacity rather than the dedicated, predictable capacity the scenario requires.

Answer

Use query caching

Answer

Create a materialized view for the entire table

Answer

Enable autoscaling slots

Question 12

A data engineer needs to orchestrate a complex data pipeline that involves multiple steps including data extraction from Cloud Storage, transformation using Dataflow, and loading into BigQuery. The pipeline has dependencies between tasks and requires monitoring and retries. Which Google Cloud service should be used for orchestration?

Accepted Answer

Cloud Composer. Cloud Composer is a fully managed workflow orchestration service built on Apache Airflow. It is designed to orchestrate complex data pipelines with dependencies, scheduling, monitoring, and retries, making it the correct choice for orchestrating extraction, transformation, and loading tasks across Google Cloud services.

Answer

Workflows

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Cloud Scheduler

Answer

Cloud Tasks

Question 13

A company has a production model deployed on Vertex AI that shows declining accuracy over time. The model uses features from a BigQuery feature store. The data science team suspects data drift. What is the most efficient way to monitor and detect drift for this model?

Accepted Answer

Enable Vertex AI Model Monitoring on the endpoint to automatically detect skew and drift. Vertex AI Model Monitoring is purpose-built for detecting feature skew and drift in production models. It automatically compares the distribution of prediction request data against training data statistics, alerting when significant divergence occurs — this is the most efficient and integrated approach for a Vertex AI endpoint.

Answer

Periodically export training data and production data to CSV and compare distributions manually

Answer

Create a scheduled retraining pipeline that runs weekly

Answer

Set up Cloud Monitoring dashboards to track prediction request volumes and error rates

Question 14

Which TWO strategies help reduce prediction latency for a real-time model deployed on Vertex AI Endpoint?

Accepted Answer

Reduce model complexity (e.g., quantize or prune). Option D is correct because reducing model complexity via quantization or pruning shrinks the compute and memory footprint of each inference, directly lowering the time the model takes to produce a prediction on the Vertex AI Endpoint. Option E is correct because enabling autoscaling with a minimum replica count keeps warm instances available so incoming real-time requests avoid cold-start delays and are served immediately, reducing prediction latency. Option A is wrong because batch prediction is an offline, asynchronous mode and does not serve real-time online requests. Option B is wrong because Cloud CDN caches HTTP content, not model inference results, and Vertex AI Endpoint predictions are not cacheable that way. Option C is wrong because simply choosing a larger machine type does not guarantee lower latency and may not address the actual bottleneck, unlike reducing model complexity or keeping replicas warm.

Answer

Use batch prediction instead of online

Answer

Use Cloud CDN to cache predictions

Answer

Use a larger machine type (e.g., n1-highcpu-16)

Question 15

A financial services company must ensure that predictions from a deployed model do not become biased against protected groups. They have a monitoring system in place. Which metric should they track?

Accepted Answer

Prediction distribution across demographic segments. Tracking prediction distribution across demographic segments (option B) directly monitors for bias by comparing the model's output rates for different protected groups. If the distribution diverges significantly, it indicates potential disparate impact, which is the core concern for fairness in deployed models. This aligns with monitoring for algorithmic fairness, not just operational performance.

Answer

Prediction latency

Answer

Per-query input feature distribution

Answer

Model accuracy over time