PDE Practice Test 44 — 15 Questions

Question 1

A data engineer configures the above lifecycle rule on a Cloud Storage bucket that stores daily log files. After 60 days, they notice that files older than 30 days have been transitioned to Nearline, but files older than 90 days are still present. What is the most likely cause?

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

Accepted Answer

The delete rule's condition includes `matchesStorageClass`: `STANDARD`, which does not match the Nearline storage class of transitioned objects.. The lifecycle delete rule includes a `matchesStorageClass` condition set to `STANDARD`. Once objects are transitioned to Nearline (which is a different storage class), they no longer match the `STANDARD` condition, so the delete rule does not apply to them. As a result, files older than 90 days that were moved to Nearline remain in the bucket.

Answer

The delete rule is missing `isLive: true` condition, so it does not apply to live objects.

Answer

The `age` condition in the delete rule is calculated from the transition date, not creation date.

Answer

The bucket has object versioning enabled, and the delete rule only applies to non-current versions.

Question 2

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 3

A company needs a fully managed, PostgreSQL-compatible database that supports both transactional (OLTP) and analytical (OLAP) workloads with low latency. They want to minimize operational overhead. Which two Google Cloud services should they consider? (Choose two.)

Accepted Answer

AlloyDB with BigQuery as a federated source. AlloyDB is a fully managed PostgreSQL-compatible database service designed for both transactional (OLTP) and analytical (OLAP) workloads with low latency. By using BigQuery as a federated source, you can run analytical queries directly against AlloyDB data without moving it, combining operational and analytical capabilities while minimizing operational overhead.

Answer

Cloud SQL for PostgreSQL

Answer

Cloud Spanner

Answer

BigQuery

Question 4

Refer to the exhibit. This log entry was generated by Vertex AI Model Monitoring for a production model. What should the data engineer do to address this issue?

Accepted Answer

Retrain the model with more recent data. Vertex AI Model Monitoring detected a drift in the 'age' feature, indicating that the production data distribution has shifted from the training data. Retraining the model with more recent data aligns the model with the current data distribution, mitigating the drift and maintaining prediction accuracy. This is the standard remediation for model drift in production ML systems.

Answer

Increase the drift threshold to 0.9 to suppress alerts

Answer

Deploy a new model version trained on the original dataset

Answer

Disable monitoring for the 'age' feature

Question 5

A financial services company stream trades into Pub/Sub and processes with Dataflow. The pipeline must ensure exactly-once processing of each trade for regulatory compliance. However, Pub/Sub guarantees at-least-once delivery. Which combination of features should the Dataflow pipeline use to achieve exactly-once semantics?

Accepted Answer

Use Dataflow's exactly-once processing mode and implement idempotent writes in the sink. Dataflow's exactly-once sink combined with idempotent writes ensures exactly-once output. Pub/Sub cannot guarantee exactly-once delivery, but Dataflow can deduplicate using unique IDs. Idempotent writes prevent duplicates even if Dataflow retries.

Answer

Enable Pub/Sub message deduplication and use at-most-once delivery

Answer

Use global windowing and discard late data

Answer

Use Pub/Sub Lite with exactly-once delivery guarantee

Question 6

A team wants to use Cloud Pub/Sub Lite for a high-throughput, low-cost messaging system. They need exactly-once delivery to subscribers. What should they know about Pub/Sub Lite's delivery guarantees?

Accepted Answer

Pub/Sub Lite provides at-least-once delivery, same as standard Pub/Sub.. Pub/Sub Lite offers at-least-once delivery like standard Pub/Sub; exactly-once is not guaranteed.

Answer

Pub/Sub Lite provides exactly-once delivery when using push subscriptions.

Answer

Pub/Sub Lite provides exactly-once delivery when using pull subscriptions.

Answer

Pub/Sub Lite supports exactly-once delivery by default.

Question 7

You are designing a data pipeline for a financial services company that requires exactly-once processing semantics. Which TWO services or configurations provide exactly-once guarantees?

Accepted Answer

Pub/Sub with exactly-once delivery enabled. Pub/Sub with exactly-once delivery enabled provides exactly-once message delivery. Dataflow with exactly-once processing mode ensures each record is processed exactly once. Pub/Sub Lite and Dataproc do not provide exactly-once guarantees, and Cloud Storage is for storage.

Answer

Dataproc with checkpointing

Answer

Pub/Sub Lite with at-least-once delivery

Answer

Cloud Storage with object versioning

Question 8

A company needs a messaging service for event-driven applications that require low cost for high-throughput, but can tolerate occasional message loss. Which Pub/Sub product should they choose?

Accepted Answer

Pub/Sub Lite. Pub/Sub Lite is designed for cost-sensitive workloads with relaxed durability. Standard Pub/Sub offers at-least-once delivery and high durability. Push vs pull is irrelevant to cost.

Answer

Pub/Sub with pull subscriptions

Answer

Pub/Sub with dead letter topics

Answer

Pub/Sub with push subscriptions

Question 9

An engineer needs to create a Pub/Sub subscription that sends messages to an HTTPS endpoint. The endpoint must be able to acknowledge messages individually. Which type of subscription should they use?

Accepted Answer

Push subscription. Push subscriptions deliver messages to a configured HTTPS endpoint. The endpoint can acknowledge by returning a 200 status.

Answer

Pull subscription

Answer

BigQuery subscription

Answer

Cloud Storage subscription

Question 10

A financial services company uses a custom container on Vertex AI Prediction to serve a fraud detection model. The container runs a Flask app that loads a large feature engineering library (~2 GB) at startup. The model is updated weekly. For the past two weeks, the new model version has been failing health checks and showing 'Container failed to start' errors in the logs. The previous versions worked fine. You inspect the container image and confirm it is built correctly using Cloud Build. The only change in the latest build is an updated version of the feature engineering library. What is the most likely cause and how should you fix it?

Accepted Answer

The new library version increased memory consumption during startup, exceeding the health check timeout. Increase the startup probe initial delay.

Answer

The Cloud Build step that pushes the image is misconfigured. Rebuild using a different approach.

Answer

The Vertex AI endpoint machine type is too small for the new container. Upgrade to a larger machine type.

Answer

The new library has a dependency conflict that causes the Flask app to crash. Roll back to the previous library version.

Question 11

A team has set up a push subscription to an HTTPS endpoint. They notice that messages are not being acknowledged and are resent every 10 seconds. What is the most likely issue?

Accepted Answer

The push endpoint is returning HTTP 500. In Google Cloud Pub/Sub push subscriptions, the subscriber must acknowledge messages by returning an HTTP 200 status code. If the endpoint returns HTTP 500, Pub/Sub interprets this as a failure and will retry delivery with exponential backoff, but the default minimum retry interval is 10 seconds. This matches the observed behavior of messages being resent every 10 seconds without acknowledgment.

Answer

The push endpoint is returning HTTP 200 but taking too long to process

Answer

The push endpoint is returning HTTP 200 with 'ack' in the body

Answer

The push endpoint is returning HTTP 400

Question 12

A company uses dbt on BigQuery to transform data. They want to run dbt models on a schedule and manage environments (dev, prod). Which GCP service should they use to run dbt jobs?

Accepted Answer

Cloud Composer. Cloud Composer is an Apache Airflow managed service that can schedule dbt runs.

Answer

Dataflow

Answer

Cloud Scheduler

Answer

Cloud Build

Question 13

A company runs a Dataproc cluster with 10 worker nodes for a Spark streaming job that processes data from Pub/Sub (via Pub/Sub Lite) and writes to Cloud Storage. They observe that the job is producing many small files in Cloud Storage, leading to high costs and performance issues in downstream batch pipelines. The team wants to consolidate output files while maintaining low latency. What is the best solution?

Accepted Answer

Use windowed streaming with a longer window duration and Spark's file size configuration. Using a longer window duration in Spark Streaming allows more data to accumulate before writing, and combining this with Spark's file size configuration (e.g., `spark.sql.files.maxRecordsPerFile` or `spark.hadoop.mapreduce.fileoutputcommitter.algorithm.version=2`) ensures that output files are consolidated into larger sizes. This reduces the number of small files in Cloud Storage while maintaining low latency by avoiding an extra compaction job or reducing parallelism.

Answer

Run a separate compaction job that periodically merges small files into larger ones

Answer

Reduce the number of workers to force more data per task

Answer

Switch from Dataproc to Dataflow, which has built-in file sharding optimization

Question 14

Which Google Cloud service is designed to replicate data from MySQL, PostgreSQL, and Oracle databases to BigQuery or Cloud Storage in near real-time?

Accepted Answer

Datastream. Datastream is a serverless CDC service that ingests change data from relational databases into GCS or BigQuery.

Answer

Cloud Data Fusion

Answer

Dataflow

Answer

Pub/Sub

Question 15

A team wants to transfer data from an on-premises Hadoop cluster to Cloud Storage for processing. The cluster is located in a remote area with limited bandwidth. They need to transfer 500 TB of data. Which service should they use?

Accepted Answer

Transfer Appliance. Transfer Appliance is designed for petabyte-scale offline transfers when bandwidth is limited.

Answer

BigQuery Data Transfer Service

Answer

Storage Transfer Service

Answer

Dataproc with gsutil