PDE Practice Test 5 — 25 Questions

Question 1

You are using Vertex AI Feature Store to serve features for online predictions. Your model requires features from multiple sources with low latency (<10ms). Which type of serving should you use?

Accepted Answer

Online serving with Bigtable. Vertex AI Feature Store online serving is designed for low-latency lookups at prediction time, and Bigtable is the underlying low-latency store used for online serving. Bigtable provides single-digit millisecond reads at scale, which satisfies the <10ms requirement. Offline serving (BigQuery) is optimized for batch/high-throughput reads, not sub-10ms online lookups.

Answer

Online serving with Cloud SQL

Answer

Offline serving with BigQuery

Answer

Offline serving with Cloud Storage

Question 2

You are building a multi-cloud analytics solution to join data from Google Cloud and AWS S3. You need to query the S3 data using BigQuery without moving it. Which Google Cloud service should you use?

Accepted Answer

BigQuery Omni. BigQuery Omni is the service that allows querying data in AWS S3 directly from BigQuery without moving it. It deploys BigQuery compute in AWS to process the data locally, returning results to Google Cloud.

Answer

Looker

Answer

Dataproc

Answer

BigQuery Data Transfer Service

Question 3

A company uses Cloud Logging to monitor application errors. They want to set up real-time notifications for critical errors. Which two actions are essential? (Choose two.)

Accepted Answer

Create a log-based metric for critical errors.. Option A is correct because a log-based metric is the mechanism that converts matching log entries (e.g., a filter for severity=ERROR or specific critical error text) into a numeric time series that Cloud Monitoring can evaluate; without it there is no metric for alerting. Option E is correct because a Cloud Monitoring alert policy is what actually triggers real-time notifications, and it must be based on the log-based metric created in A to fire when critical errors occur. Option B is not essential since exporting to BigQuery is for retrospective analysis, not real-time alerting. Option C is not essential because a Pub/Sub notification on a log sink is a separate log-routing feature and does not by itself create a Monitoring alert with notification channels. Option D is not essential because VPC Flow Logs capture network traffic metadata and are unrelated to application error alerting.

Answer

Export logs to BigQuery for later analysis.

Answer

Create a Cloud Pub/Sub notification directly on the log sink.

Answer

Enable VPC Flow Logs to capture network traffic.

Question 4

A company has a batch prediction job that runs daily using AI Platform Batch Prediction. The job uses a TensorFlow model and processes 10 GB of data. Recently, the job started failing with the error 'The replica worker 0 exited with a non-zero exit code: Out of memory'. Which action should the team take to resolve this without rewriting the model?

Accepted Answer

Use a machine type with more memory, such as n1-highmem-8.. The error 'Out of memory' on replica worker 0 indicates that the machine type assigned to the prediction job does not have enough RAM to load the model and process the 10 GB batch. Increasing the machine type to one with more memory (e.g., n1-highmem-8) directly addresses the memory constraint without requiring any code changes. This is the most straightforward fix because AI Platform Batch Prediction allows you to specify machine types in the job configuration, and the error is purely a resource allocation issue.

Answer

Increase the number of workers (parallelism) to distribute the data across more machines.

Answer

Reduce the batch size parameter in the prediction job configuration.

Answer

Optimize the model to use less memory by pruning or quantization.

Question 5

A data engineering team is operationalizing a machine learning model for real-time fraud detection. The model must process transactions with sub-100ms latency and be highly available. Which TWO strategies should the team implement?

Accepted Answer

Deploy the model to multiple Google Cloud regions for failover.. Option A is correct because deploying the model to multiple Google Cloud regions provides geographic redundancy and failover, which directly supports the high-availability requirement for a real-time fraud detection service. Option D is correct because pruning or quantizing the model reduces its size and computational cost, which helps achieve the sub-100ms latency target for real-time inference. Option B is incorrect because a single zone creates a single point of failure and does not meet the high-availability requirement, even if it reduces cross-zone latency. Option C is incorrect because Cloud Batch is designed for asynchronous, batch-oriented workloads, not real-time sub-100ms predictions. Option E is incorrect because loading the model from Cloud Storage on every request adds significant network and I/O latency, making the sub-100ms target impractical.

Answer

Deploy the model to a single zone to minimize cross-zone latency.

Answer

Use Cloud Batch for asynchronous prediction.

Answer

Store the model in Cloud Storage and load it on each request.

Question 6

You are designing a data quality pipeline that must inspect PII in BigQuery tables and de-identify sensitive columns before sharing with analysts. Which GCP service should you use?

Accepted Answer

Cloud DLP. Cloud DLP (Data Loss Prevention) is the correct choice because it is purpose-built for inspecting, classifying, and de-identifying sensitive data such as PII. It integrates natively with BigQuery via inspection jobs and de-identification templates, allowing you to scan tables for over 150 built-in infoTypes (e.g., email, SSN) and apply transformations like masking, tokenization, or encryption before sharing data with analysts.

Answer

Dataplex

Answer

Cloud Data Catalog

Answer

Dataflow

Question 7

You configured a model deployment monitor on your Vertex AI endpoint as shown. What will happen when the feature 'age' has a skew of 0.4?

Accepted Answer

An alert will be sent to admin@example.com. The monitoring configuration shows an alert threshold of 0.3 for the feature 'age', and a skew of 0.4 exceeds that threshold. Vertex AI Model Monitoring will trigger the configured alert action, which in this case is sending an email to admin@example.com. The alert is based on the specific feature's threshold, not on any other feature's threshold.

Answer

The endpoint will automatically roll back to a previous model version

Answer

No alert will be sent because the skew threshold is 0.2 for income

Answer

An alert will be sent only if both features exceed their thresholds

Question 8

Refer to the exhibit. A Dataflow streaming pipeline subscribes to this Pub/Sub subscription. The pipeline occasionally takes more than 10 seconds to process a message. Which behavior will occur?

Accepted Answer

The message will be redelivered after 10 seconds if not acknowledged.. Pub/Sub delivery requires an acknowledgment within the configurable `ackDeadlineSeconds` (default 10 seconds). If the pipeline takes longer than the ack deadline to process a message, Pub/Sub considers the message unacknowledged and redelivers it. This is the standard behavior for at-least-once delivery in Google Cloud Pub/Sub.

Answer

The message will be sent to the dead letter topic immediately.

Answer

The message will be retried with exponential backoff as per retry policy.

Answer

The message will be dropped after 10 seconds due to expiration policy.

Question 9

A company wants to use Cloud Data Fusion for ETL pipelines. They need to integrate with custom transformations not available in the marketplace. What should they do?

Accepted Answer

Write a custom plugin using the CDAP SDK and deploy it.. Cloud Data Fusion allows extending its capabilities by writing custom plugins using the CDAP SDK, which can then be deployed to the Data Fusion instance. This enables integration of transformations not available in the marketplace, providing full flexibility for custom ETL logic.

Answer

Switch to Dataproc and write a Spark job.

Answer

Use the Data Fusion Hub to download a custom plugin.

Answer

Use Dataprep to create the transformation.

Question 10

A data engineer needs to process streaming data from thousands of IoT devices and generate real-time dashboards. The data volume is low but requires exactly-once processing semantics. Which Google Cloud service combination should they use?

Accepted Answer

Cloud Pub/Sub + Cloud Dataflow. Dataflow supports exactly-once processing via its streaming engine and checkpointing. Pub/Sub is the ingest service. Together they provide the required semantics.

Answer

Cloud Pub/Sub + Cloud Data Fusion

Answer

Cloud Pub/Sub + Cloud Dataproc

Answer

Cloud Pub/Sub + Cloud Dataprep

Question 11

A company needs to process real-time clickstream data and store it in a data warehouse for SQL-based analytics. The data volume is moderate. Which combination of Google Cloud services is most cost-effective?

Accepted Answer

Cloud Pub/Sub, Cloud Dataflow, BigQuery. Cloud Pub/Sub ingests real-time clickstream data, Cloud Dataflow processes it with low latency, and BigQuery provides a serverless, SQL-based data warehouse that is cost-effective for moderate data volumes due to its pay-per-query pricing and automatic scaling. This combination avoids the overhead of managing clusters (Dataproc) or expensive storage (Cloud Spanner) while directly supporting SQL analytics.

Answer

Cloud Pub/Sub, Cloud Dataproc, Cloud Storage

Answer

Cloud Pub/Sub, Cloud Dataflow, Cloud Spanner

Answer

Cloud Pub/Sub, Cloud Dataflow, Cloud Storage

Question 12

Which Dataflow feature automatically scales the number of workers based on the pipeline's current workload, and also selects the optimal machine type for each worker based on the pipeline's resource requirements?

Accepted Answer

Dataflow Prime. Dataflow Prime is the correct answer because it is the only Dataflow feature that provides both automatic worker scaling (horizontal autoscaling) and intelligent machine type selection (vertical autoscaling). It dynamically adjusts the number of workers based on the pipeline's current workload and selects the optimal machine type (e.g., CPU, memory, or accelerator-optimized) for each worker based on the pipeline's resource requirements, such as CPU utilization, memory pressure, or shuffle throughput.

Answer

Dataflow Shuffle

Answer

Dataflow Streaming Engine

Answer

Dataflow Flex Templates

Question 13

A data pipeline ingests streaming data from Pub/Sub into BigQuery via Dataflow. Recently, the pipeline has been failing with 'deadline exceeded' errors. What is the most likely cause?

Accepted Answer

The Pub/Sub subscription's acknowledgement deadline is too short for the processing time.. 'deadline exceeded' errors in a Dataflow pipeline reading from Pub/Sub indicate that the subscriber is taking longer to process messages than the acknowledgement deadline allows. When the deadline expires, Pub/Sub redelivers the message, causing duplicate processing and eventual pipeline failure. This is a common issue when processing time exceeds the default 10-second acknowledgement deadline.

Answer

The BigQuery streaming quota is exceeded.

Answer

Dataflow workers are underutilized due to batch size settings.

Answer

Dataflow autoscaling is disabled.

Question 14

You need to ingest Google Ads performance data into BigQuery on a daily basis for reporting. Which service should you use?

Accepted Answer

BigQuery Data Transfer Service for Google Ads. The BigQuery Data Transfer Service for Google Ads is the correct choice because it provides a fully managed, scheduled connector that automatically ingests Google Ads performance data into BigQuery on a daily basis without requiring any custom code. It handles authentication, schema mapping, and incremental loads, making it the simplest and most reliable solution for this specific use case.

Answer

Cloud Scheduler to call Google Ads API and load to BigQuery

Answer

Pub/Sub with a Google Ads subscriber

Answer

Storage Transfer Service for Google Ads

Question 15

A company is building a real-time streaming pipeline to ingest clickstream events from web servers, enrich them with user profile data from Cloud Bigtable, and aggregate metrics into BigQuery. The expected throughput is 10,000 events per second with occasional spikes up to 50,000. The data must be processed with low latency (seconds) and exactly-once semantics. Which Google Cloud service should be the core processing engine?

Accepted Answer

Cloud Dataflow (Apache Beam runner). Cloud Dataflow, as a managed Apache Beam runner, is the correct choice because it provides exactly-once processing semantics, low-latency streaming (sub-second to seconds), and autoscaling to handle throughput spikes from 10,000 to 50,000 events per second. Its unified batch and streaming model allows you to enrich clickstream events with user profile data from Cloud Bigtable via side inputs or asynchronous lookups, and write aggregated metrics to BigQuery with exactly-once guarantees using the Beam BigQuery I/O connector.

Answer

Cloud Pub/Sub with Cloud Functions

Answer

Cloud Dataproc with Apache Spark Streaming

Answer

Cloud Data Fusion

Question 16

Your team is running a Dataflow streaming pipeline that reads from Pub/Sub, transforms data, and writes to BigQuery. You notice that the pipeline's backlog is growing and the processing latency has increased from seconds to minutes. You need to diagnose and resolve the issue. Which TWO actions should you take? (Choose two.)

Accepted Answer

Increase the maximum number of workers in the pipeline's autoscaling configuration to allow more compute resources.. Option E is correct because the Dataflow monitoring dashboard exposes streaming-specific metrics such as system lag, data freshness, and worker throughput, which are exactly the signals needed to determine whether the growing backlog is caused by insufficient compute, a hot key, or a slow sink. Option D is correct because a streaming pipeline with a growing backlog and rising latency is typically under-provisioned, and raising the maximum number of workers in the autoscaling configuration lets Dataflow horizontally scale the worker pool to absorb the increased load. Option A is not appropriate because stopping and restarting the pipeline interrupts streaming state and is unnecessary when autoscaling can be adjusted on a running job. Option B is not appropriate because increasing the WriteToBigQuery batch size does not address a Pub/Sub backlog and can actually increase latency and memory pressure. Option C is not appropriate because a dead-letter queue for failed messages does not relieve the reprocessing load of successfully processed but backlogged messages.

Answer

Stop the pipeline, increase the number of workers in the streaming engine configuration, and restart it.

Answer

Increase the batch size in the WriteToBigQuery transform to reduce I/O operations.

Answer

Configure a dead-letter queue in Cloud Storage for failed messages to reduce reprocessing load.

Question 17

You are a data engineer at a global e-commerce company. Your team manages a real-time recommendation system that ingests user clickstream events from a Pub/Sub topic (topic-clickstream). The pipeline uses Dataflow to read events, join with user profile data from Cloud Bigtable, compute recommendations using a machine learning model hosted on Cloud Run, and write results to a BigQuery table for analytics. The pipeline has been running smoothly for months, but recently the Dataflow job started failing with the error: "Workflow failed. Causes: S01:ReadPubSub/Read+Transform/ParDo(ExtractUserID)+ ... (5a3b2c1d) The job failed because a worker encountered an out-of-memory error." The Dataflow job uses the Streaming Engine feature with a worker type of n2-standard-8 (8 vCPU, 32 GB memory) and autoscaling from 2 to 20 workers. The clickstream event rate has increased from 500 events/second to 5000 events/second over the past week. The user profile data in Bigtable has also grown, with average row size increasing from 1 KB to 10 KB due to additional fields. You need to resolve the out-of-memory errors without completely redesigning the pipeline. What should you do?

Accepted Answer

Change the worker machine type to n2-highmem-8 (8 vCPU, 64 GB memory) in the Dataflow job configuration.. The out-of-memory error is caused by the increased per-worker memory load from larger Bigtable rows (1 KB to 10 KB) and higher event throughput (500 to 5000 events/sec). Switching to n2-highmem-8 doubles the memory from 32 GB to 64 GB, giving each worker more headroom to cache user profiles and process larger batches without OOM. This directly addresses the root cause without redesigning the pipeline.

Answer

Increase the maximum number of workers in autoscaling from 20 to 50.

Answer

Reduce the batch size in the Dataflow pipeline by setting the `max_batch_size` parameter to a lower value.

Answer

Increase the number of Bigtable nodes to improve read throughput.

Question 18

You are building a real-time fraud detection system using Dataflow. Events from Pub/Sub need to be grouped by user_id within a 5-minute window to detect suspicious patterns. Some events may be delayed by up to 2 minutes. How should you configure the window and trigger to balance accuracy and latency?

Accepted Answer

Fixed window of 5 minutes with allowed lateness of 2 minutes and early trigger every 1 minute. It directly addresses both the accuracy requirement (handling up to 2 minutes of late data) and the latency requirement (emitting early results every 1 minute). A fixed 5-minute window groups events into non-overlapping intervals, which is appropriate for per-user fraud pattern detection. Setting allowed lateness to 2 minutes ensures that events delayed by up to 2 minutes are still included in the correct window, and an early trigger (e.g., repeating every 1 minute) provides low-latency partial results before the window closes. This combination balances completeness and timeliness.

Answer

Sliding window of 5 minutes with a 1-minute period and no allowed lateness

Answer

Session window with a gap duration of 5 minutes

Answer

Fixed window of 5 minutes with no allowed lateness and default trigger

Question 19

Which THREE steps are required to set up a continuous training pipeline on Google Cloud using Vertex AI?

Accepted Answer

Create a Vertex AI Pipeline to orchestrate data preprocessing, training, and model evaluation.. Option B is correct because a Vertex AI Pipeline is the native orchestration mechanism that chains the preprocessing, training, and evaluation components into a repeatable, versioned workflow, which is the backbone of a continuous training pipeline. Option C is correct because continuous training requires an automated trigger — Cloud Scheduler for time-based runs or Cloud Build (often via Eventarc/pub-sub on new data) — to invoke the pipeline without manual intervention. Option E is correct because continuous training must include automated model evaluation and promotion logic, such as comparing accuracy against a threshold and deploying to a Vertex AI endpoint only when the gate passes, enabling CI/CD-style model rollout. Option A is not appropriate because a single Compute Engine VM with cron is a manual, non-managed approach that bypasses Vertex AI's managed pipelines, scaling, and metadata tracking. Option D is not appropriate because manually uploading models to the Vertex AI Model Registry after each run defeats automation; the pipeline should register models programmatically as a step.

Answer

Run training on a single Compute Engine VM with a cron job.

Answer

Manually upload the model to Vertex AI Model Registry after each training run.

Question 20

A data engineer needs to split time-series data for training a forecasting model. The data is sorted by timestamp. The engineer wants to avoid leakage where future data influences training. Which data splitting approach should they use?

Accepted Answer

Use a time-series aware split: first 80% of data by timestamp for training, last 20% for testing. Time-series data has a temporal order, so training must only use data that precedes the test data to prevent future information from leaking into the model. Option D holds out the last 20% of records by timestamp for testing and trains on the earlier 80%, which mirrors real forecasting conditions where the model predicts unseen future values. This preserves causality and gives a realistic estimate of out-of-sample performance.

Answer

Use k-fold cross-validation with random assignment

Answer

Use stratified splitting on the target variable

Answer

Perform a random 80/20 split on the entire dataset