PDE Practice Test 37 — 15 Questions

Question 1

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 2

A data pipeline ingests real-time events from Cloud Pub/Sub into BigQuery using Dataflow. The pipeline uses a sliding window of 5 minutes with a 1-minute period to aggregate event counts. Recently, the pipeline started failing with 'The worker failed to provide a heartbeat.' The Dataflow logs show high CPU usage on the workers. What is the best course of action to resolve the issue?

Accepted Answer

Increase the number of workers and enable autoscaling to distribute the load.. The 'worker failed to provide a heartbeat' error combined with high CPU usage indicates that workers are overloaded and cannot process data fast enough to maintain their heartbeat to the Dataflow service. Increasing the number of workers and enabling autoscaling distributes the computational load across more machines, reducing per-worker CPU pressure and allowing heartbeats to be sent on time. This directly addresses the root cause of resource exhaustion.

Answer

Reduce the number of workers to minimize coordination overhead.

Answer

Use a global window with a trigger to reduce state size.

Answer

Change the windowing to a fixed 5-minute window to reduce computations.

Question 3

You are building a Dataflow pipeline that reads from Pub/Sub, applies transformations, and writes to BigQuery. The pipeline must handle late-arriving data and ensure that the windowing and triggering are correct. Which THREE configurations should you consider? (Choose 3)

Accepted Answer

Use the BigQuery Storage Write API with committed mode to ensure exactly-once writes.. Option C is correct because the BigQuery Storage Write API in committed mode (exactly-once semantics) uses write streams with offsets and client-side deduplication, which is the recommended way to guarantee exactly-once writes from a Dataflow streaming pipeline into BigQuery. Option D is correct because setting an allowed lateness duration (via withAllowedLateness) on the windowing strategy tells Dataflow how long to retain window state and keep accepting late-arriving elements after the watermark passes the window end, which is exactly the requirement for handling late data. Option E is correct because configuring a triggering frequency (for example, AfterProcessingTime.pastFirstElementInPane().plusDelayOf(...) or a repeated trigger) controls how often intermediate or final results are emitted from each window, which directly governs the windowing/triggering behavior described in the scenario. Option A does not belong because Dataflow Streaming Engine is a runner feature that offloads state and windowing execution to the service for scalability and cost benefits; it does not itself provide exactly-once processing, which comes from the pipeline's sources, sinks, and deduplication logic. Option B does not belong because side inputs are used to enrich streaming records with additional (often static or slowly changing) data, which is unrelated to handling late-arriving data or to windowing and triggering correctness.

Answer

Enable Dataflow Streaming Engine for exactly-once processing.

Answer

Use side inputs to enrich streaming data with static data.

Question 4

A data science team deploys a TensorFlow image classification model to Vertex AI Prediction. The model performs well in offline evaluation but shows a 15% drop in accuracy in production. The production data distribution has shifted compared to the training data. The team needs to continuously monitor and retrain the model. Which solution is most appropriate for detecting drift and triggering retraining?

Accepted Answer

Enable Vertex AI Model Monitoring for feature drift; configure alerts to trigger a Vertex AI Pipelines retraining run.. Vertex AI Model Monitoring is purpose-built for detecting feature drift in production ML models by comparing live inference data against a baseline distribution. When drift is detected, it can directly trigger a Vertex AI Pipelines retraining run, creating an automated, end-to-end MLOps loop that addresses the production accuracy drop without manual intervention.

Answer

Export production predictions to Cloud Logging, then use Log Analytics to compare distributions.

Answer

Store predictions in BigQuery and run scheduled SQL queries to detect drift; trigger retraining via Cloud Functions.

Answer

Use Cloud Monitoring to track prediction latency and error rates; manually retrain when errors increase.

Question 5

A media company processes video metadata using a Dataflow pipeline. They need to join two streaming sources: user activity (Pub/Sub) and video catalog updates (Pub/Sub). Which THREE transforms should be used in the pipeline?

Accepted Answer

ParDo to process each element individually. To join two streaming Pub/Sub sources in Dataflow, you need to: (1) Use ParDo to extract the key from each element (e.g., video_id), (2) Window both PCollections into a common window (e.g., fixed 1-minute) to align the data, and (3) Use CoGroupByKey to join on the common key. GroupByKey separately is not required because CoGroupByKey internally groups by key. Flatten is used to combine PCollections of the same type, which is not applicable here.

Answer

Flatten to combine the two PCollections

Answer

GroupByKey on each PCollection separately before joining

Question 6

A company uses Vertex AI Feature Store for serving features. They have a high-throughput online serving requirement. Which configuration should they use?

Accepted Answer

Vertex AI Feature Store with online serving enabled. Vertex AI Feature Store with online serving enabled is the correct choice because it is specifically designed for low-latency, high-throughput retrieval of feature values for online predictions. It uses a managed Bigtable backend optimized for real-time serving, ensuring consistent performance under high request loads without requiring manual infrastructure management.

Answer

Cloud Storage with high-memory instances

Answer

Bigtable as serving source

Answer

Firestore

Question 7

Which Google Cloud service is a serverless, highly scalable data warehouse for analytical queries, supporting SQL and integration with BI tools?

Accepted Answer

BigQuery. BigQuery is a serverless, highly scalable data warehouse designed for analytical queries over large datasets. It supports standard SQL and integrates seamlessly with BI tools like Looker and Tableau, making it the correct choice for this use case.

Answer

Firestore

Answer

Cloud SQL

Answer

Cloud Spanner

Question 8

A company needs to continuously synchronize customer data changes from an on-premises Oracle database to BigQuery for near-real-time analytics. The Oracle database has Change Data Capture (CDC) enabled. Which Google Cloud service should be used to stream these changes with minimal latency and schema evolution support?

Accepted Answer

Use Datastream to stream CDC changes from Oracle to BigQuery. Datastream is Google Cloud's fully managed, serverless CDC and replication service that natively supports Oracle sources (via LogMiner or XStream) and BigQuery destinations. It streams changes with low latency, handles schema evolution automatically, and requires no pipeline code to maintain. This matches the requirement for minimal latency and schema evolution support.

Answer

Deploy a Dataflow pipeline with a JDBC source and Pub/Sub

Answer

Use Cloud SQL with a read replica and enable binary logging

Answer

Use Transfer Appliance to copy Oracle data periodically

Question 9

A company needs to serve predictions for a model that runs an expensive computation on each request. The model is used by a batch job that processes millions of records each night, and also by a real-time API for a few thousand queries per hour. Which prediction strategy minimizes cost and latency for both use cases?

Accepted Answer

Use Vertex AI batch prediction for the nightly job and a separate online endpoint with auto-scaling for the real-time API.. It separates the batch and online workloads to optimize cost and latency. Vertex AI batch prediction is designed for high-throughput, asynchronous processing of large datasets at lower cost, while a separate online endpoint with auto-scaling ensures low-latency responses for real-time API queries by scaling resources based on demand. This avoids over-provisioning for the batch job and prevents the batch workload from interfering with the latency-sensitive API.

Answer

Deploy two identical models, one on a Compute Engine VM for batch, one on Vertex AI for online, and synchronize updates.

Answer

Use Vertex AI batch prediction for both workloads.

Answer

Use a single online Vertex AI endpoint with auto-scaling to handle both workloads.

Question 10

Match each Google Cloud monitoring/logging service to its function.

Accepted Answer

Cloud Monitoring: Monitors the performance, uptime, and health of cloud applications and services.. Cloud Monitoring provides metrics and dashboards; Cloud Logging manages logs; Error Reporting aggregates errors; Cloud Trace provides tracing. Common mistakes include mixing up Logging with Trace and Monitoring with Error Reporting.

Answer

Cloud Logging: Provides distributed tracing and latency analysis.

Answer

Cloud Monitoring: Counts, analyzes, and aggregates errors.

Question 11

A financial services company receives real-time stock trade data via Pub/Sub. They need to enrich each trade with reference data from a Cloud SQL table and write the results to BigQuery for real-time analytics. The enrichment must handle late-arriving data and ensure exactly-once processing. Which Dataflow streaming pipeline configuration should be used?

Accepted Answer

Build a custom Dataflow pipeline using the Storage Write API with exactly-once semantics and a side input from Cloud SQL. The BigQuery Storage Write API with exactly-once semantics is the only option that satisfies both the exactly-once requirement and the need for enrichment with Cloud SQL reference data. A custom Dataflow pipeline can use the Storage Write API's exactly-once mode while applying a side input (or CoGroupByKey) to join the streaming trades with the Cloud SQL reference table. This combination handles late-arriving data via allowed lateness and windowing while guaranteeing no duplicate writes to BigQuery.

Answer

Use a Dataflow Flex Template that reads from Pub/Sub, joins in memory, and writes to BigQuery using legacy streaming inserts

Answer

Use Pub/Sub to BigQuery template with streaming inserts and a side input from Cloud SQL

Answer

Deploy a Dataproc Spark Streaming job that reads from Pub/Sub, enriches via JDBC, and writes to BigQuery

Question 12

Refer to the exhibit. A data scientist deploys a model using this configuration. Users report that after a few hours of inactivity, the first prediction request takes over 30 seconds. What is the most likely cause?

Accepted Answer

The automatic scaling configuration allows scaling down to zero replicas, causing a cold start on the first request.. The automatic scaling configuration that allows scaling down to zero replicas means that after a period of inactivity, all model replicas are terminated. When a new prediction request arrives, the endpoint must provision a new replica from scratch, which involves loading the model artifacts, initializing the inference container, and performing health checks. This cold start process typically takes 30 seconds or more, matching the reported behavior.

Answer

The network latency between the client and the endpoint is high due to regional distance.

Answer

The endpoint is misconfigured with the wrong regional endpoint.

Answer

The model is too large and exceeds the instance memory.

Question 13

A data scientist is training a binary classification model on an imbalanced dataset (95% negative, 5% positive) using AutoML Tables. Which strategy should they use to handle the class imbalance?

Accepted Answer

Specify a weight column with higher weights for positive examples in the dataset.. AutoML Tables supports a weight column that lets you assign higher importance to specific rows during training. By giving positive examples (the 5% minority class) higher weights, the model's loss function penalizes misclassification of the minority class more heavily, effectively rebalancing the learning signal without altering the dataset. This is the native, supported mechanism in AutoML Tables for handling class imbalance.

Answer

Set the budget to a higher value to allow more training on minority class.

Answer

Use SMOTE in a Dataflow pipeline before importing the data to AutoML Tables.

Answer

Create duplicate copies of the positive class rows to balance the dataset.

Question 14

A company wants to stream real-time clickstream data from a website into BigQuery for near-real-time analytics. They expect peaks of 10,000 events per second. Which combination of services is most suitable for ingestion?

Accepted Answer

Pub/Sub → Dataflow → BigQuery (Storage Write API). For high-throughput, real-time streaming into BigQuery, the combination of Pub/Sub for ingestion, Dataflow for stream processing, and BigQuery's Storage Write API for writing is the most suitable. Pub/Sub handles the 10,000 events per second peaks reliably, Dataflow provides scalable stream processing, and the Storage Write API offers exactly-once semantics and better performance than legacy streaming inserts.

Answer

Cloud Storage → Cloud Functions → BigQuery

Answer

Direct Web → Dataflow → BigQuery

Answer

Pub/Sub → Dataflow → BigQuery (legacy streaming inserts)

Question 15

Your data engineering team needs to process a continuous stream of clickstream events from a website and update a real-time dashboard showing user activity over the last hour. The pipeline should have minimal operational overhead and support exactly-once processing semantics. Which Google Cloud service should you use?

Accepted Answer

Cloud Dataflow with Apache Beam. Cloud Dataflow with Apache Beam is Google's fully managed, serverless stream and batch processing service, and Beam provides built-in exactly-once semantics for streaming pipelines. It integrates natively with Pub/Sub for ingestion and supports windowing, triggers, and state for real-time aggregations like a rolling 1-hour user activity view. Because Dataflow is fully managed, it meets the 'minimal operational overhead' requirement without cluster management.

Answer

Cloud Dataproc with Apache Spark Streaming

Answer

Cloud Data Fusion with batch pipelines

Answer

Cloud Pub/Sub Lite with push subscriptions