PDE Storing the Data Practice Test 6 — 15 Questions

Question 1

A mobile app needs a NoSQL database that supports offline synchronization when the device goes offline and later reconnects. Which Google Cloud database should be used?

Accepted Answer

Firestore. Firestore is a NoSQL document database that provides built-in offline synchronization for mobile and web apps. It caches data locally and automatically syncs changes when the device reconnects, making it ideal for offline-capable applications.

Answer

Cloud Bigtable

Answer

Cloud Spanner

Answer

Cloud SQL

Question 2

An organization needs to restrict access to BigQuery and Cloud Storage so that data can only be accessed from within a specific VPC network and cannot be exfiltrated. Which Google Cloud feature should they use?

Accepted Answer

VPC Service Controls. VPC Service Controls (option B) is the correct choice because it creates a security perimeter around Google Cloud services like BigQuery and Cloud Storage, preventing data exfiltration even from within a VPC. It enforces context-aware access based on the VPC network, ensuring data can only be accessed from authorized VPC sources and blocking unauthorized transfers outside the perimeter.

Answer

Private Service Access

Answer

VPC firewall rules

Answer

IAM conditions

Question 3

A team wants to store semi-structured user profile data for a web application. The data is accessed via a REST API and requires security rules to control read/write access. Which database fits best?

Accepted Answer

Firestore. Firestore is a NoSQL document database that natively stores semi-structured data (JSON-like documents) and integrates directly with Firebase Authentication and security rules to control read/write access per document or collection. Its REST API support and real-time capabilities make it ideal for web application user profiles that require flexible schemas and fine-grained access control.

Answer

BigQuery

Answer

Cloud SQL

Answer

Cloud Bigtable

Question 4

A company wants to build a reporting pipeline where data is collected from IoT devices, stored raw in Cloud Storage, and then processed into BigQuery for analytics. They need to ensure data is encrypted at rest using customer-managed keys. Which THREE steps should they take? (Choose 3 correct options)

Accepted Answer

Enable CMEK on the Cloud Storage bucket by specifying the KMS key. The scenario requires customer-managed encryption keys (CMEK) for data at rest across Cloud Storage and BigQuery, so the foundational step is option E: create a key ring and key in Cloud Key Management Service, since CMEK always begins with a KMS key ring and a key (or key version) that will be referenced by the services. Option B is correct because enabling CMEK on the Cloud Storage bucket by specifying the KMS key ensures the raw IoT data written to the bucket is encrypted at rest with that customer-managed key rather than a Google-managed key. Option C is correct because configuring the BigQuery dataset to use a CMEK key ensures the processed analytics data stored in BigQuery is also encrypted at rest with the customer-managed key, satisfying the end-to-end requirement. Option A is wrong because deleting the Cloud KMS key after loading would make the encrypted data unrecoverable and break decryption for both Cloud Storage and BigQuery. Option D is wrong because Google-managed encryption keys are the default and do not meet the requirement for customer-managed keys.

Answer

Delete the Cloud KMS key after data is loaded to BigQuery

Answer

Use Google-managed encryption keys

Question 5

A company needs to store petabytes of time-series IoT sensor data and query it with single-digit millisecond latency at millions of reads per second. The data has a simple key-value structure with timestamps. Which Google Cloud database is MOST appropriate?

Accepted Answer

Cloud Bigtable. Cloud Bigtable is a fully managed, scalable NoSQL database designed for large analytical and operational workloads, handling petabytes of data with consistent single-digit millisecond latency for high-throughput reads and writes. Its key-value model with timestamp-based versioning is ideal for time-series IoT sensor data, and it supports millions of reads per second via its HBase API and Bigtable's underlying tablet-based architecture.

Answer

Firestore

Answer

Cloud Spanner

Answer

BigQuery

Question 6

A data engineer wants to store archived log files in Cloud Storage with a retention policy that prevents deletion for 5 years. Which feature should they use?

Accepted Answer

Retention Policy on the bucket set to 5 years. A retention policy on a Cloud Storage bucket enforces a minimum retention period for all objects in the bucket, preventing deletion or overwrite until the policy duration has elapsed. Setting it to 5 years ensures that archived log files cannot be deleted before that time, meeting the data engineer's requirement exactly. This is a bucket-level, immutable setting that applies to all objects, unlike object-level holds or lifecycle rules.

Answer

Object Lifecycle rule with Delete action after 5 years

Answer

Object Hold (temporal)

Answer

Versioning enabled

Question 7

A mobile app uses Firestore to store user profiles. The app allows offline data creation and syncing when connectivity resumes. Which Firestore feature should the developer enable?

Accepted Answer

Enable offline persistence in the Firestore client SDK. Firestore's offline persistence feature allows the client SDK to automatically cache data locally on the device. When the app creates or modifies data while offline, the SDK stores the changes in a local queue and syncs them with the Firestore backend once connectivity is restored. This is the correct and built-in mechanism for offline data creation and syncing.

Answer

Set up a Firestore trigger to cache data in Cloud Memorystore

Answer

Use Cloud Storage signed URLs for offline access

Answer

Enable Firestore multi-region replication

Question 8

A data engineer is designing a BigQuery table for a clickstream dataset with frequent queries aggregating over user sessions. Each user session has multiple events, and the engineer wants to avoid joins for performance. Which schema design pattern should they use?

Accepted Answer

Use nested and repeated fields to store events within each session row. BigQuery's nested and repeated fields (using STRUCT and ARRAY) allow you to store multiple events within a single session row, eliminating the need for joins when aggregating over sessions. This denormalized pattern is ideal for clickstream data and improves query performance by keeping related data together.

Answer

Use a normalized schema with separate tables for sessions and events, then join on session ID

Answer

Store each event as a separate row with session key and use clustering on session ID

Answer

Use partitioning on event timestamp and clustering on user ID

Question 9

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 10

A team needs to run hybrid transactional/analytical workloads on PostgreSQL-compatible data with low latency. They require high performance on both OLTP and OLAP queries, leveraging a columnar engine. Which Google Cloud service is best suited?

Accepted Answer

AlloyDB. AlloyDB is the correct choice because it is a fully managed PostgreSQL-compatible database service on Google Cloud that combines a columnar engine for fast analytical queries with high transactional performance. It uses a columnar query accelerator to offload analytical workloads from the transactional engine, enabling low-latency hybrid transactional/analytical processing (HTAP) without data movement.

Answer

Cloud SQL for PostgreSQL

Answer

Cloud Spanner

Answer

BigQuery

Question 11

A data engineer wants to automatically move objects from Standard storage class to Nearline after 30 days, and then to Archive after 365 days. Which Cloud Storage feature should they configure?

Accepted Answer

Object Lifecycle rule with SetStorageClass actions. Object Lifecycle rules in Google Cloud Storage allow you to automatically transition objects between storage classes (e.g., from Standard to Nearline after 30 days, then to Archive after 365 days) using the SetStorageClass action. This feature is specifically designed for automated lifecycle management, including deletion and class transitions, based on object age or other conditions.

Answer

Object Versioning

Answer

Retention Policy

Answer

Bucket Lock

Question 12

An e-commerce application uses Cloud SQL (MySQL) for transaction processing. To improve read performance for reporting queries, the team wants to offload read traffic to a separate database instance that stays in sync with the primary. Which Cloud SQL feature should they use?

Accepted Answer

Create a cross-region read replica. Cloud SQL read replicas are designed to offload read traffic from the primary instance while staying in sync using asynchronous replication. Cross-region read replicas specifically allow you to place a replica in a different region, improving read performance for geographically distributed reporting queries without affecting the primary's transaction processing.

Answer

Configure a high availability (HA) replica

Answer

Use Cloud SQL's failover replica

Answer

Enable automatic backups and point-in-time recovery

Question 13

A company is designing a data lake on Cloud Storage with BigLake tables for unified governance. Which TWO statements about BigLake are correct? (Choose 2.)

Accepted Answer

BigLake tables allow querying data in Cloud Storage using BigQuery without loading into BigQuery storage.. Option B is correct because BigLake tables are external tables that let BigQuery query data residing in Cloud Storage (Parquet, ORC, Avro, JSON, CSV, Iceberg, Delta) directly, without ingesting it into BigQuery's native storage. Option D is correct because BigLake extends BigQuery's fine-grained governance to external data, supporting row-level security and column-level security (via policy tags in Data Catalog) so access controls apply uniformly across the lakehouse. Option A is wrong because BigLake's defining feature is avoiding the load into BigQuery storage; that describes native BigQuery tables. Option C is wrong because BigLake handles structured and semi-structured formats in Cloud Storage, not only structured data in BigQuery storage. Option E is wrong because BigLake does not perform automatic format conversion of CSV to Parquet; it queries files in their existing format.

Answer

BigLake requires data to be loaded into BigQuery storage.

Answer

BigLake only supports structured data in BigQuery storage.

Answer

BigLake automatically converts CSV files to Parquet.

Question 14

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 15

A company needs to store petabytes of time-series IoT sensor data and query it with single-digit millisecond latency at millions of reads per second. The data has a simple key-value structure with timestamps. Which Google Cloud database is MOST appropriate?

Accepted Answer

Cloud Bigtable. Cloud Bigtable is a fully managed, scalable NoSQL database designed for large analytical and operational workloads, handling petabytes of data with consistent sub-10ms latency at millions of reads per second. Its key-value model with timestamps directly matches the time-series IoT sensor data structure, and it supports high-throughput, low-latency access via the HBase API or Bigtable client libraries.

Answer

BigQuery

Answer

Cloud Spanner

Answer

Firestore