PDE Practice Test 19 — 15 Questions

Question 1

A healthcare startup is deploying a natural language processing (NLP) model for extracting medical entities from clinical notes. The model is a fine-tuned BERT model served on Vertex AI Prediction using a custom container. The team observes that prediction latency is around 500ms per request, but they need to handle up to 100 requests per second (QPS) with end-to-end latency under 200ms. The model currently runs on n1-standard-4 machines (4 vCPU, 15 GB memory). During load testing, CPU utilization reaches 90% and memory usage is 12 GB. The team is considering options to meet the requirements. Which action should they take?

Accepted Answer

Use a machine type with a GPU, such as n1-standard-4 with a NVIDIA Tesla T4 accelerator, and optimize the model with TensorRT.. Option A is correct because the bottleneck is CPU-bound inference (90% CPU utilization) with memory well within limits (12 GB of 15 GB). Adding a GPU (NVIDIA Tesla T4) and optimizing with TensorRT reduces per-request latency via hardware acceleration and graph optimizations, enabling sub-200ms inference at 100 QPS. This directly addresses the latency requirement without changing the machine family or scaling strategy.

Answer

Switch to n1-highmem-4 machines to provide more memory for the model.

Answer

Deploy the model using TensorFlow Serving with CPU-only nodes and increase the number of replicas.

Answer

Move the model to Cloud Run with automatic scaling to handle the QPS.

Question 2

A company runs a production Dataflow streaming pipeline that reads from Pub/Sub, groups events by customer ID, and writes to BigQuery. The pipeline uses global windows with triggers. After a recent code change, the pipeline started generating duplicate events in BigQuery for the same customer ID. The previous version did not have duplicates. The team reviews the code and sees that the trigger was changed from 'afterProcessingTime' to 'afterWatermark'. What is the most likely reason for duplicates?

Accepted Answer

Late-arriving events cause the afterWatermark trigger to fire additional panes for the same window. The change from `afterProcessingTime` to `afterWatermark` introduces a dependency on the watermark, which estimates event time progress. When late-arriving events (those with timestamps before the watermark) arrive after the watermark has advanced, the `afterWatermark` trigger fires an additional pane for the same window, causing duplicate writes to BigQuery. The previous trigger (`afterProcessingTime`) fired based on processing time, which does not react to late data in the same way, hence no duplicates.

Answer

The afterProcessingTime trigger fired multiple times for the same window

Answer

The pipeline is firing early and on-time panes for the same window

Answer

The pipeline uses accumulation mode which accumulates results across firings

Question 3

A retail company uses a Vertex AI endpoint to serve product recommendations. The model is a TensorFlow model deployed with a custom container. Recently, users have reported that recommendations are stale. The model is retrained daily using Vertex AI Pipelines. The pipeline completes successfully, but the endpoint continues to serve the old model. The team checks the pipeline logs and sees that the new model is uploaded to the Vertex AI Model Registry. The endpoint has traffic split set to 100% for the old model. The team needs to update the endpoint to serve the new model version. What should they do?

Accepted Answer

Update the endpoint to deploy the new model version from the registry and adjust traffic split. Option D is correct because the pipeline successfully uploaded the new model to the Vertex AI Model Registry, but the endpoint still has its traffic split configured to 100% for the old model. To serve the new model, the team must explicitly update the endpoint to deploy the new model version from the registry and adjust the traffic split to route 100% of traffic to it. This is a standard operational step in Vertex AI: uploading a model does not automatically update the endpoint's deployment or traffic allocation.

Answer

Check the pipeline for errors in the deployment step

Answer

Re-upload the model with a different version ID

Answer

Redeploy the same model to the endpoint

Question 4

A company uses Cloud Composer to orchestrate a daily ETL pipeline that includes multiple Dataproc jobs. The pipeline processes sensitive financial data. The security team requires that all data in transit be encrypted, and all Cloud Storage buckets used by the pipeline should have uniform bucket-level access enabled and VPC Service Controls. The pipeline currently uses a single Cloud Composer environment in us-east1. The Dataproc clusters are created using the standard image and use custom service accounts with minimal permissions. The pipeline runs successfully during testing, but in production, the Dataproc jobs fail with 'Access Denied' errors when trying to write to a Cloud Storage bucket. The bucket has uniform bucket-level access enabled and is inside a VPC Service Controls perimeter. The Dataproc service account has the Storage Object Admin role at the project level. What is the most likely cause of the access denied error?

Accepted Answer

The Dataproc cluster is not in the VPC Service Controls perimeter.. The Dataproc cluster is created outside the VPC Service Controls perimeter, so even though the service account has the Storage Object Admin role at the project level, requests from the cluster are blocked by the perimeter's ingress/egress rules. VPC Service Controls enforce a security boundary that prevents resources outside the perimeter from accessing protected services like Cloud Storage, regardless of IAM permissions. The 'Access Denied' error in production, despite successful testing, strongly indicates a perimeter configuration mismatch.

Answer

The service account does not have the Storage Object Admin role on the bucket.

Answer

Data in transit encryption is not enabled for the Cloud Storage bucket.

Answer

Uniform bucket-level access prevents writes from service accounts.

Question 5

A company runs a critical real-time data pipeline using Dataflow that ingests events from Cloud Pub/Sub, performs aggregations using sliding windows, and writes results to BigQuery. The pipeline is deployed in us-central1. The pipeline's latency has increased recently, and the Dataflow monitoring shows that the 'system lag' metric is consistently above 5 minutes. The pipeline is using Streaming Engine and has 10 workers with 4 vCPUs each. The pipeline processes approximately 100,000 events per second. The team has verified that the source Pub/Sub topic has sufficient publish throughput and the BigQuery table has no quota issues. The pipeline logs show that some workers are experiencing GC overhead limit exceeded errors. The pipeline code uses stateful processing with a custom keyed state for deduplication. What is the most likely cause of the increased latency?

Accepted Answer

The stateful processing is causing large state sizes that lead to GC overhead; use a more efficient state backend or increase worker memory.. The GC overhead limit exceeded errors indicate that workers are spending too much time garbage collecting, which is a classic symptom of excessive heap memory usage. Stateful processing with custom keyed state for deduplication can cause large per-key state sizes, especially with sliding windows that maintain overlapping state for each key. This forces the JVM to constantly garbage collect, increasing system lag beyond 5 minutes. Using a more efficient state backend (e.g., reducing state size or using Dataflow's built-in deduplication) or increasing worker memory directly addresses the root cause.

Answer

The number of workers is insufficient; increasing to 20 workers will reduce latency.

Answer

The sliding window duration is too long; reducing it to 1 minute will improve performance.

Answer

The deduplication logic is causing a bottleneck; removing it will reduce latency.

Question 6

A company runs a real-time anomaly detection system on Google Cloud. Streaming data from IoT devices is ingested via Pub/Sub, processed by Dataflow (Apache Beam), and results are written to Bigtable for low-latency serving. Recently, the system has been experiencing increased latency and occasional data loss. The Dataflow pipeline shows high system lag and backlog in Pub/Sub. The Bigtable cluster has 3 nodes and is reporting high CPU utilization (over 90%). The team suspects the issue is with the pipeline configuration. They have already verified that there are no errors in the pipeline code and no network issues. Which action should they take to resolve the issue?

Accepted Answer

Increase the number of Bigtable nodes to handle the write throughput.. The high CPU utilization on Bigtable (over 90%) indicates that the cluster is saturated and cannot keep up with the write throughput from Dataflow. This causes backpressure in the pipeline, leading to increased system lag and backlog in Pub/Sub, and eventually data loss when Pub/Sub messages expire. Increasing the number of Bigtable nodes directly addresses the bottleneck by distributing the write load and reducing CPU pressure, which allows the pipeline to drain the backlog and reduce latency.

Answer

Change the Dataflow worker machine type to n2-standard-8.

Answer

Decrease the batch size in the Dataflow pipeline to reduce latency.

Answer

Increase the number of Dataflow workers to process messages faster.

Question 7

A company uses Cloud Composer to orchestrate data pipelines. They have a DAG that runs hourly and processes files from Cloud Storage. The DAG is triggered by a Pub/Sub message sent from a Cloud Storage bucket notification. Recently, some DAG runs are not starting even though the Pub/Sub messages are published. Which two likely causes should the team investigate? (Choose TWO.)

Accepted Answer

The Cloud Storage bucket notification is not sending messages to the correct Pub/Sub topic, or the subscription's ack deadline is too short.. Option A is correct because if the Cloud Storage bucket notification is misconfigured to send messages to the wrong Pub/Sub topic, the Pub/Sub sensor in the DAG will never receive the trigger message, causing DAG runs to not start. Additionally, if the subscription's ack deadline is too short, the message may be acknowledged before the sensor processes it, leading to message loss and missed triggers. Both issues directly prevent the DAG from being triggered by Pub/Sub messages.

Answer

The DAG's start_date is set in the past and catchup is set to False, so DAG runs are only triggered on schedule.

Answer

The total number of DAGs in the environment exceeds the maximum limit of 100, causing DAG processing to stop.

Answer

The Cloud Composer environment is using a pull subscription instead of a push subscription for the Pub/Sub sensor.

Question 8

Drag and drop the steps to create a Cloud Storage bucket with uniform bucket-level access into the correct order.

Question 9

Drag and drop the steps to create a Cloud Bigtable instance and table using the CLI into the correct order.

Question 10

Drag and drop the steps to set up a BigQuery dataset with a scheduled query into the correct order.

Question 11

Match each Google Cloud data service to its primary use case.

Question 12

Match each BigQuery feature to its description.

Question 13

Match each Google Cloud service to its data processing capability.

Question 14

Match each machine learning term to its description.

Question 15

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