PDE Storing the Data Practice Test 4 — 15 Questions

Question 1

A data engineer is designing a Bigtable row key for a time-series application that records temperature sensor readings every second. To avoid hotspotting, they want to distribute writes across all nodes. Which row key design is best?

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

Accepted Answer

[hash of sensor_id]#[timestamp]. Hotspotting occurs when sequential keys hit a single tablet server. A reversed timestamp or hashed prefix distributes writes. Pre-pending a hash ensures even distribution. Timestamp alone causes hotspotting. Sensor ID + timestamp can still be sequential if sensor ID is low cardinality.

Answer

[timestamp reversed]#[sensor_id]

Answer

[sensor_id]#[timestamp]

Answer

[timestamp]#[sensor_id]

Question 2

A team wants to use Cloud Storage to build a data lake with separate zones for raw, curated, and processed data. They need to automatically move objects older than 30 days from the raw zone to a cheaper storage class. How can they achieve this?

Accepted Answer

Configure a Cloud Storage object lifecycle rule with SetStorageClass action. Cloud Storage object lifecycle management rules can automatically transition objects from one storage class to a cheaper one (e.g., from Standard to Nearline or Coldline) based on age. By configuring a rule with the `SetStorageClass` action and a `Condition` of `age: 30`, objects in the raw zone bucket older than 30 days are moved to a lower-cost class without manual intervention or additional compute services.

Answer

Set a bucket retention policy that forces deletion after 30 days

Answer

Write a Cloud Function to delete objects older than 30 days

Answer

Use gsutil rsync to move objects between buckets

Question 3

A company uses BigQuery for analytics and needs to ensure that certain columns containing PII are encrypted at query time so that only authorized users can decrypt. What should they use?

Accepted Answer

BigQuery AEAD encryption functions. BigQuery AEAD encryption functions allow you to encrypt sensitive columns (e.g., PII) at query time using a user-managed key, so that only authorized users who possess the key can decrypt the data. This is the correct approach because it provides column-level, application-layer encryption that is transparent to the query engine and ensures that unauthorized users see only ciphertext.

Answer

VPC Service Controls

Answer

Customer-managed encryption keys (CMEK)

Answer

Fine-grained IAM roles

Question 4

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 provides offline persistence for mobile and web apps. It caches data locally and syncs when online. Cloud SQL and Spanner are relational; Bigtable does not have offline sync.

Answer

Cloud Bigtable

Answer

Cloud Spanner

Answer

Cloud SQL

Question 5

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

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IAM conditions

Question 6

A company uses Cloud Spanner for a global e-commerce platform. They have a table of orders and a table of order items. To optimize performance for queries that join these tables on order_id, which Spanner schema design feature should they use?

Accepted Answer

Use interleaved tables with order_items as a child table of orders. Interleaved tables store child rows physically with parent rows, reducing join latency. Secondary indexes are for filtering. Partitioned tables not in Spanner. Denormalization could help but interleaved tables are the designed approach.

Answer

Use Cloud Bigtable instead

Answer

Create a secondary index on order_id

Answer

Denormalize the order items into the orders table using repeated fields

Question 7

A company is designing a data lake on Cloud Storage with different zones. They need to enforce data retention so that objects in the 'raw' zone are automatically deleted after 1 year. Which TWO actions should they take? (Choose 2 correct options)

Accepted Answer

Configure a Cloud Storage object lifecycle rule with a Delete action. Cloud Storage object lifecycle management allows you to set rules that automatically delete objects after a specified age. By configuring a lifecycle rule with a Delete action and setting the condition to 'Age: 365 days', objects in the 'raw' zone will be automatically removed after 1 year, meeting the retention requirement without manual intervention.

Answer

Use a bucket retention policy with a retention period of 1 year

Answer

Set an IAM policy to prevent deletion of objects

Answer

Create a Cloud Function to check object age and delete them

Question 8

A global fintech company needs a database that can serve transactional (OLTP) and analytical (OLAP) workloads with strong consistency. They require high availability and PostgreSQL compatibility. Which TWO Google Cloud databases meet these requirements? (Choose 2 correct options)

Accepted Answer

Cloud Spanner. Cloud Spanner is correct because it provides a globally distributed, strongly consistent relational database service that supports both OLTP and OLAP workloads via PostgreSQL-compatible interfaces (including the PostgreSQL dialect). It offers high availability through synchronous replication across zones and regions, and its TrueTime-based atomic clocks ensure external consistency for transactions. AlloyDB is also correct because it is a fully PostgreSQL-compatible database service that delivers high availability and strong consistency. It is designed to handle both transactional and analytical workloads using a combined row-store and columnar engine, enabling high-performance OLTP and real-time analytics within a single database. Both services meet the fintech company's requirements for PostgreSQL compatibility, strong consistency, and high availability.

Answer

Cloud SQL

Answer

Cloud Bigtable

Answer

BigQuery

Question 9

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 10

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 11

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 supports nested and repeated fields (e.g., STRUCT and REPEATED), allowing denormalization. This reduces the need for joins and improves query performance. Partitioning and clustering are for physical data organization, not schema design.

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 12

An IoT application writes sensor readings to Cloud Bigtable with a row key of 'deviceID#timestamp'. The team notices high write latency and hotspots on a few nodes. Which row key design change would most likely improve performance?

Accepted Answer

Add a random prefix to the row key (e.g., hash of deviceID modulo 1000). Prefixing timestamps can cause hotspots because writes go to the same tablet server for the same time range. Hashing the device ID or using a field-leveled design (e.g., deviceID inverted) distributes writes across nodes. Adding a random prefix helps but salting with a hash is more systematic.

Answer

Reverse the key to 'timestamp#deviceID'

Answer

Use a single column family with many columns

Answer

Store all data in one row per device

Question 13

A company wants to use BigQuery to query data stored in Cloud Storage as Parquet files without loading the data into BigQuery storage. Which feature should they use?

Accepted Answer

BigQuery external tables. BigQuery external tables allow querying data stored in Cloud Storage (including Parquet files) directly without loading it into BigQuery storage. This is achieved by defining a table schema that references the external data source, enabling BigQuery to read the Parquet files on-the-fly using its federated query engine.

Answer

BigQuery ingestion from Cloud Storage using load jobs

Answer

Cloud Storage FUSE to mount bucket and query

Answer

BigQuery federated queries with Cloud Storage

Question 14

A healthcare company must encrypt data in BigQuery with customer-managed keys (CMEK). They want to control the key lifecycle independently. Which approach should they take?

Accepted Answer

Use Cloud KMS to create a key, then set it as the default encryption key for the BigQuery dataset. BigQuery supports CMEK through Cloud KMS. You can create a key ring and key in Cloud KMS, then specify it when creating datasets or tables. The key is used to encrypt data at rest. Column-level encryption is separate and uses AEAD functions with customer-managed keys at the application level.

Answer

Use BigQuery column-level encryption (AEAD) with a key from Cloud KMS

Answer

Enable default encryption on the Cloud Storage bucket used for staging data

Answer

Encrypt the data before loading using a custom application and store the key in Secret Manager

Question 15

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