PDE Practice Test 17 — 15 Questions

Question 1

An organization is moving on-premises Hadoop workloads to Google Cloud. They need to minimize code changes and manage transient clusters for cost savings. Which two Google Cloud services should they consider? (Choose TWO.)

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

Accepted Answer

Dataproc on GKE. Cloud Dataproc (option D) is a managed service for running Spark and Hadoop clusters. It supports transient clusters that can be created on-demand and deleted when idle, minimizing costs. It also allows direct migration of on-premises Hadoop code with minimal changes because it supports standard Hadoop/Spark APIs. Dataproc on GKE (option C) provides similar benefits but runs containerized workloads on GKE, offering additional ephemeral cluster capabilities and integration with Kubernetes. Both options minimize code changes and enable transient clusters for cost savings, while BigQuery (option B) requires rewriting SQL queries and Cloud Dataflow (option E) requires converting to Beam pipelines. Compute Engine with self-managed Hadoop (option A) does not provide transient cluster management by default.

Answer

Compute Engine with self-managed Hadoop

Answer

BigQuery

Answer

Cloud Dataflow

Question 2

An organization needs to prevent data exfiltration from BigQuery by ensuring all traffic to BigQuery APIs goes through VPC boundaries and is restricted to a specific service perimeter. Which Google Cloud security control should they use?

Accepted Answer

VPC Service Controls. VPC Service Controls (D) is the correct answer because it allows you to define a service perimeter around BigQuery APIs, ensuring that all traffic to BigQuery must originate from within the defined VPC boundaries. This prevents data exfiltration by blocking unauthorized access from outside the perimeter, even if valid credentials are used. It works by enforcing context-aware access policies at the Google Cloud network edge, not at the application layer.

Answer

Access Transparency

Answer

IAM conditions on BigQuery roles

Answer

Cloud Armor

Question 3

A company uses Cloud Pub/Sub for a real-time data pipeline. The subscription has a backlog of millions of messages that are not being processed quickly enough. In Cloud Monitoring, you observe that the 'subscription/num_undelivered_messages' metric is high and growing, while 'subscription/oldest_unacked_message_age' is also increasing. Which action is MOST likely to reduce the backlog?

Accepted Answer

Increase the number of subscribers or the throughput capacity of the existing subscribers.. The backlog indicates that subscribers cannot keep up with the message flow. Increasing the number of subscribers or scaling their throughput capacity directly addresses the processing bottleneck, allowing messages to be pulled and acknowledged faster. Cloud Pub/Sub scales horizontally, so adding more pull subscribers or increasing the resources of existing ones (e.g., more worker threads, higher CPU/memory) reduces the backlog.

Answer

Delete the subscription and recreate it with a larger message retention duration.

Answer

Reduce the acknowledgment deadline to force faster processing.

Answer

Change the subscription type from push to pull.

Question 4

A payment processing company needs to detect fraudulent transactions in real time. The system must have sub-second latency for high-value transactions and use a machine learning model. Which two components should be part of the architecture? (Choose TWO.)

Accepted Answer

Bigtable to store user profiles and transaction history for fast lookups. Bigtable is a fully managed, scalable NoSQL database that provides consistent sub-10ms latency for high-throughput read/write operations, making it ideal for real-time lookups of user profiles and transaction history in fraud detection. Its ability to handle large volumes of data with low latency supports the sub-second requirement for high-value transactions.

Answer

Cloud Storage for transaction logs

Answer

Cloud SQL to store reference data

Answer

Cloud Functions for long-running batch model training

Question 5

Which Google Cloud service provides a serverless Spark environment where you can run Spark jobs without provisioning or managing a cluster?

Accepted Answer

Dataproc Serverless. Dataproc Serverless is a Google Cloud service that allows you to run Spark jobs without provisioning or managing a cluster. It automatically scales resources and charges only for the duration of the job, making it ideal for serverless Spark workloads. This matches the requirement exactly.

Answer

Dataflow

Answer

Dataprep

Answer

Cloud Data Fusion

Question 6

A company wants to use BigQuery materialized views to accelerate queries on a table that is updated every hour. Which statement about materialized views is true?

Accepted Answer

Materialized views are automatically updated when the base table changes.. BigQuery materialized views are automatically refreshed when the base table changes, within a bounded staleness window, so queries against them return fresh results without manual intervention. This automatic refresh is a core property that makes them suitable for accelerating queries on hourly-updated tables.

Answer

Materialized views cannot be clustered.

Answer

Materialized views must be manually refreshed by the user.

Answer

Materialized views can only be created on ingestion-time partitioned tables.

Question 7

A company needs a fully managed, globally distributed relational database with strong consistency, external consistency, and 99.999% SLA for a financial transaction processing system. Which Google Cloud service should they use?

Accepted Answer

Cloud Spanner. Cloud Spanner is the correct choice because it is a fully managed, globally distributed relational database service that provides strong consistency, external consistency (true serializable transactions across regions), and a 99.999% SLA. These features are essential for a financial transaction processing system that requires ACID compliance and global scalability without sacrificing consistency.

Answer

Firestore

Answer

Bigtable

Answer

Cloud SQL

Question 8

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 9

Your team uses Cloud Dataproc for Spark ML training jobs. You want to reduce costs for non-critical, fault-tolerant training jobs. Which Dataproc feature should you use for worker nodes?

Accepted Answer

Use preemptible instances for worker nodes.. Preemptible instances are short-lived, lower-cost VMs that Cloud Dataproc can use for worker nodes. Because the training jobs are non-critical and fault-tolerant (e.g., they can handle node failures via Spark's built-in resilience), preemptible instances significantly reduce costs while still completing the workload. This directly addresses the requirement to reduce costs for fault-tolerant jobs.

Answer

Use custom machine types with more memory.

Answer

Use SSDs instead of HDDs for persistent disks.

Answer

Use committed use discounts for 1-year or 3-year terms.

Question 10

A company has a Cloud SQL for PostgreSQL instance that must be highly available across zones with automatic failover. They also need a read replica for reporting workloads. Which configuration should they use?

Accepted Answer

Enable high availability (regional) and create a read replica. Cloud SQL for PostgreSQL supports regional high availability (HA) with synchronous replication across two zones, ensuring automatic failover without data loss. Additionally, you can create a read replica in a different zone or region to offload reporting workloads without impacting the primary instance's performance. This combination meets both the high availability and read replica requirements.

Answer

Deploy Cloud SQL in multi-region mode

Answer

Enable automatic backups and point-in-time recovery

Answer

Create a cross-region replica and use it for failover

Question 11

You have a BigQuery table with sales data and want to pivot product categories into columns. Which SQL clause should you use?

Accepted Answer

PIVOT. BigQuery supports the PIVOT operator, which rotates rows into columns by aggregating values for each distinct pivot key. It is used in the FROM clause after the source table or subquery, with an aggregate function and a list of pivot values. This is the standard SQL way to turn product categories into columns in a sales table.

Answer

UNPIVOT

Answer

ARRAY_AGG with CROSS JOIN

Answer

STRUCT

Question 12

A financial services company uses Cloud Pub/Sub with ordering keys to process transactions in order. Some messages are failing processing and getting stuck. The team wants to ensure that if a message fails, it can be reprocessed later without blocking subsequent messages. What should they implement?

Accepted Answer

Configure a dead letter topic and handle the failed message separately. A dead letter topic (DLT) allows failed messages to be moved aside after exhausting retry attempts, so they do not block the processing of subsequent ordered messages. In Cloud Pub/Sub, ordering keys require messages with the same key to be delivered in order; if a message fails and is not acknowledged, it blocks all later messages with the same key. By configuring a dead letter topic, the failed message is automatically forwarded to the DLT after a maximum of 5 delivery attempts (default), and the original subscription can continue processing the next messages in order. The team can then reprocess the failed message from the DLT separately, without affecting the order of other messages.

Answer

Create multiple subscriptions for the same topic

Answer

Use a pull subscription with flow control settings

Answer

Increase the acknowledgment deadline to 600 seconds

Question 13

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 14

A retail company ingests point-of-sale clickstream events into Cloud Pub/Sub at roughly 200,000 messages per second during flash sales. Analysts need near-real-time dashboards that aggregate revenue by product category over sliding 5-minute windows, with results visible in BigQuery within 30 seconds of the event. The pipeline must handle occasional bursts up to 3x the normal rate without dropping messages, and the team wants to minimize operational overhead. Which design should the data engineer use?

Accepted Answer

Create a Cloud Dataflow streaming pipeline in the Apache Beam Java SDK using Pub/Sub as the source, apply windowing with a 5-minute sliding window and a 30-second allowed lateness, and write aggregated results to BigQuery using the Storage Write API.. The requirement for sliding 5-minute windows with sub-30-second BigQuery visibility under bursty load points to a managed stream processor with native windowing and autoscaling. Dataflow's sliding windows, allowed lateness, and BigQuery Storage Write API together meet latency, correctness, and operational-overhead constraints. Cluster-based and file-based approaches add latency or management burden that the scenario rules out.

Answer

Use Cloud Pub/Sub push subscriptions delivering events directly to a Cloud Run service that aggregates in memory and calls the BigQuery streaming insert API once per minute.

Answer

Deploy a Dataproc cluster with Spark Structured Streaming reading from Pub/Sub and writing micro-batches to BigQuery every 10 seconds, sizing the cluster for the 3x burst rate.

Answer

Load raw events into Cloud Storage using a Pub/Sub subscription with a Cloud Storage sink, then run a BigQuery scheduled query every 5 minutes that reads the new files and computes the sliding window aggregates.

Question 15

Your company wants to analyze real-time user clickstream data from a website. The data arrives as JSON messages via an HTTP endpoint. The pipeline should be able to handle spikes in traffic, provide low-latency insights, and store the raw data in a data lake for historical analysis. Which Google Cloud service should you use to ingest and process the streaming data?

Accepted Answer

Cloud Pub/Sub combined with Dataflow. Cloud Pub/Sub is the correct ingestion service because it provides a highly scalable, fully managed message queue that can handle traffic spikes by decoupling producers from consumers. Dataflow (Apache Beam) then processes the streaming data with low latency, supports exactly-once semantics, and can write raw data to a data lake like Cloud Storage for historical analysis. This combination meets all requirements: spike handling, low-latency insights, and raw data storage.

Answer

Cloud Dataproc

Answer

Cloud Functions

Answer

Cloud IoT Core