PDE Practice Test 11 — 15 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