PDE Practice Test 1 — 25 Questions

Question 1

A company uses Cloud Storage to store sensitive customer data. They need to restrict access to the data so that only requests from within a specific VPC network are allowed, and block all access from the public internet. Which TWO configurations should they implement? (Choose 2.)

Accepted Answer

Use IAM conditions to restrict access based on VPC network.. Option D is correct because IAM conditions can be used to restrict access to Cloud Storage based on the requester's VPC network, ensuring only requests originating from the specified VPC are allowed. Option E is correct because VPC Service Controls create a service perimeter that prevents data exfiltration and blocks access from outside the perimeter, effectively denying public internet requests.

Answer

Enable Private Google Access on the VPC subnet.

Answer

Use Cloud Storage signed URLs for all access.

Answer

Disable public internet access by turning off the default internet gateway.

Question 2

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.. Option A is correct because 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 3

You need to store and query a large dataset of customer profiles. The data is semi-structured and frequently updated. The application requires offline support for mobile users. Which database is MOST appropriate?

Accepted Answer

Firestore. Firestore is the most appropriate choice because it is a NoSQL document database designed for semi-structured data, real-time synchronization, and offline support. It provides built-in offline persistence for mobile clients, allowing users to read and write data even without network connectivity, and automatically syncs changes when the connection is restored. This directly meets the requirements of semi-structured data, frequent updates, and offline mobile support.

Answer

BigQuery

Answer

Cloud Bigtable

Answer

Cloud SQL

Question 4

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. VPC Service Controls (A) is correct because it allows you to define a security perimeter around BigQuery and Cloud Storage, restricting access to these services from only a specific VPC network. This ensures that even if an attacker obtains valid credentials, they cannot access the data from outside the authorized VPC, which is essential for limiting encryption key access to the designated network.

Answer

Cloud HSM

Answer

Identity-Aware Proxy (IAP)

Answer

Cloud Armor

Question 5

A healthcare company stores patient records as JSON files in Cloud Storage for analysis. They want to design a data lake that enables querying the data with BigQuery while minimizing storage costs and maintaining data security. Which two actions should they take? (Choose two.)

Accepted Answer

Configure object lifecycle management to transition files older than 90 days to Nearline storage.. Option B is correct because Cloud Storage object lifecycle management can automatically transition objects older than 90 days to Nearline storage, which offers lower storage costs than Standard storage while still providing low-latency access for occasional queries. This reduces storage costs without compromising the ability to query the data with BigQuery, as BigQuery can read from Nearline storage via external tables or BigLake.

Answer

Partition the data by date and store in separate directories for each partition.

Answer

Convert all JSON files to CSV to reduce storage size.

Answer

Enable Cloud KMS to encrypt the data with customer-managed encryption keys.

Question 6

A media company streams real-time viewer data from Pub/Sub to BigQuery using a Dataflow pipeline. They need to handle occasional malformed messages without losing valid data. Which pattern should they implement?

Accepted Answer

Implement a dead letter sink to store malformed messages for later analysis. Option C is correct because a dead letter sink (e.g., a separate Pub/Sub topic or a BigQuery error table) allows the Dataflow pipeline to route malformed messages out of the main processing path while continuing to process valid data. This pattern ensures no valid data is lost and provides a durable location for later analysis or reprocessing of the malformed records, which is essential for streaming pipelines where data quality issues are intermittent.

Answer

Raise an exception in the pipeline and stop processing

Answer

Use retry logic in the pipeline to reprocess malformed messages indefinitely

Answer

Discard malformed messages and log an error

Question 7

Your Cloud Dataflow pipeline is failing due to a 'Permission denied' error when writing to a BigQuery table. The error persists even though the service account has bigquery.dataEditor role. What is the most likely missing permission?

Accepted Answer

bigquery.tables.create on the dataset. The bigquery.dataEditor role grants permissions to read and modify existing tables but does not include bigquery.tables.create, which is required when a Dataflow pipeline writes to a BigQuery table that does not already exist. The 'Permission denied' error occurs because the service account lacks the ability to create the destination table in the dataset, even though it can edit existing ones.

Answer

pubsub.topics.publish on a notification topic

Answer

storage.objects.create on the staging bucket

Answer

bigquery.tables.get on the table

Question 8

A data engineer is migrating on-premises Hadoop jobs to Dataproc. Which TWO considerations are important?

Accepted Answer

Use Preemptible VMs for worker nodes to reduce cost. Option A is correct because Preemptible VMs are significantly cheaper than standard VMs (up to 80% discount) and are ideal for worker nodes in Dataproc clusters, as they can handle transient failures gracefully through checkpointing and job retries. This cost optimization is a key consideration when migrating Hadoop jobs to the cloud, where compute costs can dominate.

Answer

Avoid using Cloud Storage connector to prevent overhead

Answer

Keep HDFS for better performance

Answer

Use on-demand VMs for master node to ensure availability

Question 9

A streaming pipeline uses Cloud Pub/Sub and Dataflow to process financial transactions. The pipeline must guarantee that each transaction is processed exactly once and in order per customer key. Which two configurations are necessary? (Choose two.)

Accepted Answer

Use Dataflow stateful processing with event time ordering. Option C is correct because Dataflow's stateful processing with event time ordering allows you to maintain per-key state and process elements in the order of their event timestamps, which is essential for guaranteeing exactly-once processing and in-order handling per customer key. This ensures that each transaction is processed once and in the correct sequence, even in the presence of out-of-order data arrival.

Answer

Use a session window with max gap duration

Answer

Use a keyed state with a value state per customer

Answer

Use a global window with a trigger

Question 10

A company wants to process large CSV files stored in Cloud Storage and load them into BigQuery. The files are generated daily and each file is about 10 GB. The data is not time-sensitive and can be processed within a 24-hour window. Which service is most cost-effective for this use case?

Accepted Answer

Dataproc Serverless with PySpark. Dataproc Serverless with PySpark is the most cost-effective choice because it eliminates cluster management overhead and automatically scales resources based on workload, charging only for the processing time used. For 10 GB CSV files processed daily within a 24-hour window, the serverless model avoids the fixed costs of a persistent cluster, making it ideal for batch, non-time-sensitive jobs. PySpark's native support for CSV parsing and BigQuery integration via the Spark BigQuery connector ensures efficient data loading without additional services.

Answer

Dataflow with batch mode

Answer

Cloud Data Fusion

Answer

BigQuery Data Transfer Service

Question 11

A company runs a Dataflow streaming pipeline that reads from Cloud Pub/Sub and writes to BigQuery. The pipeline uses a side input that is a large lookup table (10 GB) stored in Cloud Storage. The side input is updated hourly. The pipeline experiences high latency and OOM errors on workers. What is the best approach to resolve this?

Accepted Answer

Use a Cloud Bigtable table as a side input via a RichSDF.. Option A is correct because using a Cloud Bigtable table as a side input via a RichSDF (Rich Splittable DoFn) allows the pipeline to perform point lookups on the large (10 GB) lookup table without loading it entirely into worker memory. This avoids OOM errors and reduces latency by leveraging Bigtable's low-latency, scalable key-value storage, which is ideal for high-throughput streaming pipelines that require frequent, random access to a large, frequently updated dataset.

Answer

Use a side input from a PCollection and broadcast it.

Answer

Increase the number of workers to distribute the side input.

Answer

Increase the worker memory to 16 GB per worker.

Question 12

Your company uses Vertex AI Pipelines to automate model retraining. The pipeline has three steps: data extraction from BigQuery, feature engineering using Dataflow, and model training using a custom container on Vertex AI Training. Recently, the pipeline has been failing intermittently at the Dataflow step with a 'The job encountered a transient error. Please retry.' message. You have enabled pipeline retries with 3 attempts. However, the pipeline still fails after 3 retries. You check the logs and find that the Dataflow job requires more resources than the default worker configuration provides. Which change should you make to reduce the failure rate?

Accepted Answer

Increase the Dataflow worker machine type to have more memory and CPU in the pipeline step configuration. Option D is correct because the pipeline fails due to insufficient resources (memory and CPU) in the default Dataflow worker configuration. By increasing the worker machine type (e.g., using a custom machine type with more vCPUs and memory), the Dataflow job can handle the feature engineering workload without hitting resource limits, reducing transient failures. This directly addresses the root cause identified in the logs, unlike retries or parallelism changes.

Answer

Increase the number of Dataflow workers to improve parallelism

Answer

Increase the number of retries in the pipeline to 5

Answer

Replace Dataflow with Dataproc to run the feature engineering step

Question 13

A company wants to implement a near-real-time lake architecture using Cloud Storage and BigQuery. They need to enable queries on data within 5 minutes of arrival. Which approach meets the requirement with minimal operational overhead?

Accepted Answer

Use BigQuery Omni with external tables pointing to Cloud Storage. BigQuery Omni allows querying data directly in Cloud Storage via external tables without moving or loading it, enabling near-real-time queries on data as it arrives. This approach minimizes operational overhead because it eliminates the need for load jobs, streaming infrastructure, or compute clusters, and data becomes queryable within minutes of being written to Cloud Storage.

Answer

Set up a Cloud Function to trigger BigQuery load jobs every 5 minutes

Answer

Use Cloud Storage FUSE to mount buckets and query with Spark on Dataproc

Answer

Stream data into a BigQuery table via streaming inserts, then use a scheduled query to merge into the main table

Question 14

A company stores sensitive data in BigQuery and needs to encrypt certain columns with customer-managed encryption keys (CMEK) while using BigQuery's analytics capabilities. What should they do?

Accepted Answer

Use the AEAD encryption functions in BigQuery to encrypt specific columns during query time.. Option D is correct because BigQuery's AEAD encryption functions allow you to encrypt specific columns at query time using customer-managed keys, while still leveraging BigQuery's full analytics capabilities on the unencrypted portions of the data. This approach meets the requirement of encrypting certain columns with CMEK without losing the ability to run analytical queries on the rest of the table.

Answer

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

Answer

Use BigQuery column-level security with data classification.

Answer

Create a BigQuery table with CMEK enabled; it will automatically encrypt all columns.

Question 15

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 16

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, meeting the fintech company's requirements for strong consistency and PostgreSQL compatibility.

Answer

Cloud SQL

Answer

Cloud Bigtable

Answer

BigQuery

Question 17

A mobile app needs an offline-first NoSQL database that syncs data across devices when connectivity is available. Which Google Cloud database meets these requirements?

Accepted Answer

Firestore. Firestore is a NoSQL, serverless, offline-first database that automatically syncs data across devices when connectivity is restored. It provides built-in offline persistence and real-time synchronization, making it ideal for mobile apps that need to work offline and sync later.

Answer

Memorystore

Answer

Cloud SQL

Answer

Bigtable

Question 18

A company wants to use BigQuery to query data stored in Parquet files in Cloud Storage without loading the data into BigQuery. Which BigQuery 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 feature uses a federated query engine that reads the data on the fly, supporting formats like Parquet, Avro, ORC, CSV, and JSON. Option C is correct because it directly addresses the requirement to query Parquet files in Cloud Storage without ingestion.

Answer

BigQuery Omni

Answer

BigQuery ML

Answer

BigQuery BI Engine

Question 19

A company uses Cloud Dataflow to process streaming data. They notice that the pipeline's throughput is lower than expected and the system is experiencing high latency. What is the most likely cause?

Accepted Answer

Too few workers. In Cloud Dataflow, streaming pipelines require sufficient worker resources to handle the incoming data rate and maintain low latency. When too few workers are provisioned, the pipeline cannot process data quickly enough, leading to increased backlog and higher latency. This is the most likely cause of reduced throughput and high latency in a streaming pipeline.

Answer

Using batch mode instead of streaming mode

Answer

Too many workers

Answer

Incorrect watermark setting

Question 20

A financial services company uses a Dataflow streaming pipeline to process real-time stock trades. The pipeline reads from Pub/Sub, enriches with reference data from Cloud Bigtable, and writes to BigQuery. Recently, they noticed an increase in processing latency during market open hours. Investigation shows that the pipeline is data-skewed: a few stock symbols generate 90% of the traffic. The team wants to reduce latency without changing the pipeline structure. What should they do?

Accepted Answer

Enable Dataflow Streaming Engine to dynamically repartition work. Option C is correct because Dataflow Streaming Engine can dynamically repartition work, which directly addresses data skew by redistributing the processing load of hot keys (e.g., high-volume stock symbols) across available workers. This reduces latency without altering the pipeline structure, as it uses a shuffle service that separates the compute from the storage, allowing for more efficient handling of skewed data.

Answer

Increase the Pub/Sub subscription flow control to buffer less data

Answer

Use event-time windows based on trade timestamp to spread data

Answer

Increase the number of workers and use more CPU