PDE Storing the Data Practice Test 2 — 15 Questions

Question 1

A company stores data in a Cloud Storage bucket with versioning enabled. They want to automatically delete objects that are noncurrent (i.e., previous versions) after 30 days, and also delete the current version if it is older than 365 days. Which three Object Lifecycle Management conditions can be used together? (Choose three.)

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

Accepted Answer

age: 365. Option B (age: 365) is correct because the age condition measures the time since an object's current version was created, so age: 365 will delete the live (current) version once it is older than 365 days. Option C (numNewerVersions: 1) is correct because it identifies noncurrent versions by counting how many newer versions exist; with numNewerVersions: 1, any object version that has at least one newer version is treated as noncurrent and eligible for deletion. Option E (noncurrentTimeBefore: 30) is correct because noncurrentTimeBefore specifically targets the time a version became noncurrent, so a value of 30 days deletes previous versions 30 days after they were superseded. Option A (lastAccessTime: 30) is not part of the marked correct set and is not a valid Object Lifecycle Management condition for this requirement, since lastAccessTime is not a supported lifecycle condition in Cloud Storage. Option D (daysSinceCustomTime: 30) is also not in the correct set; daysSinceCustomTime applies only when an object has a custom time set and does not address noncurrent version deletion based on becoming noncurrent.

Answer

lastAccessTime: 30

Answer

daysSinceCustomTime: 30

Question 2

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 storage model and automatic sharding make it ideal for time-series IoT sensor data with simple timestamp-based keys, supporting high-throughput, low-latency access without the overhead of relational features.

Answer

BigQuery

Answer

Cloud Spanner

Answer

Firestore

Question 3

A multinational corporation needs a globally distributed database that supports strong consistency, SQL queries, and automatic failover across regions. They also want to optimize join performance for parent-child relationships. Which TWO features of Cloud Spanner should they use?

Accepted Answer

Strong consistency and automatic failover across regions. Option A is correct because Cloud Spanner natively provides strong consistency (external consistency via TrueTime) and automatic failover across regions through its multi-region configurations, directly matching the requirement for a globally distributed database with strong consistency and automatic regional failover. Option E is correct because interleaved tables physically co-locate child rows with their parent rows, which optimizes join performance for parent-child relationships by reducing data shuffling and network hops. Option B is incorrect because secondary indexes improve query filtering and lookup performance, not parent-child join optimization. Option C is incorrect because Bigtable is a separate NoSQL service and cannot serve as a caching layer that provides SQL strong consistency for Spanner. Option D is incorrect because read replicas in Cloud Spanner are for scaling reads and serving data closer to users, not for providing the strong-consistency, automatic-failover multi-region capability described.

Answer

Secondary indexes

Answer

Bigtable as a caching layer

Answer

Read replicas for global distribution

Question 4

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 the correct choice because it is a fully managed, scalable NoSQL database designed for large analytical and operational workloads, offering consistent sub-10ms latency for high-throughput reads and writes. It natively supports time-series data with row key design optimized for timestamp-based queries, and can handle millions of reads per second across petabytes of data, making it ideal for IoT sensor data.

Answer

BigQuery

Answer

Firestore

Answer

Cloud Spanner

Question 5

You are designing a Cloud Storage bucket to hold sensitive financial documents that must not be deleted or overwritten for 7 years. After the retention period, the documents can be deleted automatically. Which configuration should you use?

Accepted Answer

Use Bucket Lock with a retention policy of 7 years and configure a lifecycle rule to delete objects after 7 years.. Bucket Lock (also known as Object Lock) provides a WORM (Write Once, Read Many) retention policy that prevents objects from being deleted or overwritten for a specified period. By setting a retention policy of 7 years, you enforce the required compliance hold. Then, a lifecycle rule configured to delete objects after 7 years ensures automatic removal once the retention period expires. This combination meets both the retention and automatic deletion requirements.

Answer

Set a retention policy on the bucket for 7 years and enable Object Versioning.

Answer

Use Object Lock with WORM mode and set a retention period of 7 years. After the period, objects are automatically deleted.

Answer

Set a lifecycle rule to delete objects after 7 years and enable Bucket Lock.

Question 6

A company is using BigQuery for analytics and needs to ensure that certain columns containing PII are encrypted with a customer-managed key (CMEK). Which approach should they take?

Accepted Answer

Use BigQuery column-level encryption with AEAD functions and a Cloud KMS key.. BigQuery column-level encryption using AEAD (Authenticated Encryption with Associated Data) functions allows you to encrypt specific columns containing PII with a customer-managed key (CMEK) stored in Cloud KMS. This approach provides granular, field-level encryption that meets compliance requirements without affecting the rest of the table or dataset, and the encryption/decryption is performed transparently within BigQuery using the AEAD.DECRYPT_STRING function.

Answer

Use Cloud Data Loss Prevention (DLP) to mask the columns during query.

Answer

Apply CMEK at the dataset level; all tables inherit the encryption.

Answer

Store the data encrypted in Cloud Storage and use external tables with a CMEK.

Question 7

You need to create a Cloud Storage bucket for a data lake that will store raw ingested data. The data must be immutable and cannot be deleted or overwritten for a compliance period of 5 years. Which feature should you enable?

Accepted Answer

Bucket Lock with a retention policy of 5 years. Bucket Lock with a retention policy of 5 years is the correct feature because it enforces immutability on all objects in the bucket for the specified retention period, preventing deletion or overwriting. This meets the compliance requirement for data that cannot be deleted or overwritten for 5 years. Bucket Lock is a bucket-level setting that applies a retention policy to all objects, ensuring they are protected.

Answer

Object Versioning

Answer

Lifecycle rules to delete objects after 5 years

Answer

Object Lock with governance mode

Question 8

A team needs to run analytics on data stored in Cloud Storage (Parquet format) without moving it into BigQuery storage. They want to use SQL queries and BigQuery features like caching and partitioning. Which approach should they use?

Accepted Answer

Create a BigQuery external table pointing to the Cloud Storage location.. An external table in BigQuery allows you to query data stored in Cloud Storage (Parquet format) using standard SQL without moving the data into BigQuery's managed storage. This approach supports BigQuery features like caching (results caching) and partitioning (by defining a Hive-partitioned external table), meeting all requirements.

Answer

Use Cloud Dataproc to run Spark SQL on the data.

Answer

Load the data into BigQuery tables using a batch load job.

Answer

Use BigLake tables with Cloud Storage.

Question 9

A company is building a data lake on Cloud Storage. They need to organise data into zones for raw, curated, and processed layers. Which TWO practices should they follow? (Choose 2.)

Accepted Answer

Use separate Cloud Storage buckets for each zone (raw, curated, processed).. Using separate Cloud Storage buckets for the raw, curated, and processed zones provides clear isolation of data layers, allowing independent IAM policies, lifecycle rules, retention settings, and storage classes per zone, which is a recommended data lake organization pattern on Google Cloud. Option E is also correct because a single bucket with distinct prefixes (e.g., /raw, /curated, /processed) is a valid and common way to logically organize data lake zones within Cloud Storage, since prefixes act as pseudo-folders and can be targeted by lifecycle rules and IAM conditions. Option A is not correct because using the same storage class for all zones ignores the different access patterns and cost profiles of raw versus curated versus processed data, reducing cost efficiency. Option C is not correct because while retention policies can support immutability, the question asks about organizing data into zones, and immutability of the raw zone is not a required practice for zone organization. Option D is not correct because enabling object versioning on all buckets is a data protection measure, not a zone organization practice, and it can increase storage costs unnecessarily across all layers.

Answer

Use the same storage class for all zones to simplify management.

Answer

Set retention policies on the raw zone to make data immutable.

Answer

Enable object versioning on all buckets to prevent data loss.

Question 10

You are designing a Cloud Spanner schema for a global e-commerce application. The database will include a Customers table and an Orders table. To optimise performance for queries that join Customers with their Orders, which THREE design choices are recommended? (Choose 3.)

Accepted Answer

Use interleaved tables: make Orders an interleaved table under Customers.. Option B is correct because interleaving Orders under Customers physically co-locates each customer's order rows with the parent customer row, so joins on the parent-child key are executed locally without a distributed cross-node lookup, which is the recommended Cloud Spanner pattern for parent-child relationships. Option D is correct because placing CustomerId as the first part of the Orders primary key makes it the prefix of the interleaved key, enabling efficient key-range scans and joins that fetch all orders for a given customer in a single locality. Option E is correct because a secondary index on OrderDate in Orders supports efficient range and ordering queries over order dates (e.g., recent orders) without full table scans, and Cloud Spanner indexes can be made covering or storing to avoid extra lookups. Option A is not recommended because collapsing Customers and Orders into one table creates wide rows, redundant customer data, and loses the relational modeling and locality benefits of interleaving. Option C is not recommended because embedding order details as repeated fields in Customers denormalizes data, causes row-size growth and update contention on the customer row, and is unsuitable for high-volume, independently mutated order records.

Answer

Use a single table for both Customers and Orders and filter by CustomerId.

Answer

Denormalise the schema by embedding order details into the Customers table as repeated fields.

Question 11

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 the correct choice because it is a fully managed, scalable NoSQL database designed for large analytical and operational workloads, offering single-digit millisecond latency for high-throughput reads and writes. Its key-value model with timestamp-based versioning directly matches the time-series IoT sensor data pattern, and it can handle petabytes of data with millions of reads per second using its distributed, sorted key-value store (based on Google's Chubby and GFS).

Answer

Cloud Spanner

Answer

Firestore

Answer

BigQuery

Question 12

A data engineer needs to store transactional data for an e-commerce application that requires ACID compliance, automatic failover, and point-in-time recovery. The expected throughput is a few thousand transactions per second. Which Google Cloud storage option should they choose?

Accepted Answer

Cloud SQL. Cloud SQL is the correct choice because it provides full ACID compliance, automated failover with high availability configurations, and point-in-time recovery via binary log replay. It supports up to several thousand transactions per second with appropriate machine sizing, making it suitable for this e-commerce workload.

Answer

Firestore

Answer

Cloud Bigtable

Answer

Cloud Spanner

Question 13

A company stores sensitive customer data in BigQuery and Cloud Storage. They want to encrypt the data with customer-managed encryption keys (CMEK) and ensure that access to the key material is restricted to only approved networks. Which additional Google Cloud control should they implement to enforce network-based access to the encryption keys?

Accepted Answer

VPC Service Controls. VPC Service Controls creates a service perimeter around BigQuery and Cloud Storage, and critically, it also protects the Cloud KMS keys used to encrypt that data by restricting key access to approved networks and identities. This is the only control in the list that enforces network-based (perimeter) access to key material, satisfying the requirement to limit key access to approved networks.

Answer

Identity-Aware Proxy (IAP)

Answer

Private Google Access

Answer

Cloud Armor

Question 14

A company needs to store transactional data for a global customer base with strong consistency and 99.999% availability SLA. They anticipate millions of transactions per day across multiple regions. Which TWO storage options meet these requirements? (Choose 2)

Accepted Answer

Firestore (in Datastore mode). Cloud Spanner (E) is correct because it is Google Cloud's globally distributed, horizontally scalable relational database that provides strong consistency (external consistency via TrueTime) and a 99.999% availability SLA with multi-region configurations, making it ideal for millions of transactions per day across regions. Firestore in Datastore mode (C) is also correct because it is a globally distributed, strongly consistent NoSQL database that offers a 99.999% SLA for multi-region deployments and automatically scales to handle high transaction volumes across regions. Cloud Bigtable (A) is not suitable because it provides only eventual consistency for multi-region replication and a lower availability SLA, and it is designed for analytical workloads rather than strongly consistent transactions. AlloyDB (B) and Cloud SQL with HA (D) are regional relational databases that do not provide global distribution or a 99.999% availability SLA, so they cannot meet the global, high-availability transactional requirements.

Answer

Cloud Bigtable

Answer

AlloyDB

Answer

Cloud SQL (with HA)

Question 15

A company stores sensitive data in BigQuery and Cloud Storage. They must encrypt data with customer-managed keys and restrict access to the encryption keys to only a specific VPC network. Which THREE components should they configure? (Choose 3)

Accepted Answer

VPC Service Controls. Option C (Cloud KMS with a customer-managed key) is correct because customer-managed encryption keys (CMEKs) in Cloud KMS are exactly what allow the company to encrypt BigQuery and Cloud Storage data with keys they own and control, rather than Google-managed keys. Option B (Cloud HSM) is correct because Cloud HSM provides hardware-backed key protection within Cloud KMS, satisfying the requirement for customer-managed keys with stronger hardware security guarantees for sensitive data. Option A (VPC Service Controls) is correct because a VPC Service Controls perimeter restricts access to the KMS API and the protected BigQuery/Cloud Storage services so that only resources inside the specified VPC network can use the keys, which directly meets the 'restrict access to keys to a specific VPC network' requirement. Option D (Identity-Aware Proxy) is not correct because IAP controls access to web applications and backend services, not encryption key access or VPC-level service perimeters. Option E (Cloud Armor) is not correct because Cloud Armor provides WAF/DDoS protection at the edge for HTTP(S) load balancing, which is unrelated to key management or VPC network restriction of KMS keys.

Answer

Identity-Aware Proxy (IAP)

Answer

Cloud Armor