PDE Practice Test 36 — 15 Questions

Question 1

A data engineer wants to create a data lake on Google Cloud for storing raw streaming data, then transform it into curated and processed zones for analytics. The data is in Avro format and will be queried by BigQuery. Which two services are MOST suitable as the primary storage and query interface?

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

Accepted Answer

Cloud Storage and BigQuery. Cloud Storage is the most suitable primary storage for a data lake because it provides scalable, durable, and cost-effective object storage for raw Avro data. BigQuery is the ideal query interface because it can directly query Avro files stored in Cloud Storage using external tables, and it supports serverless analytics without needing to manage infrastructure.

Answer

Cloud Storage and Dataproc

Answer

Cloud Storage and Cloud SQL

Answer

Cloud Storage and Firestore

Question 2

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.. Deploying the model to multiple Google Cloud regions ensures high availability and failover capability. If one region becomes unavailable, traffic can be routed to another region, maintaining sub-100ms latency by using regional load balancing and Cloud DNS. This aligns with the requirement for a highly available, low-latency fraud detection system.

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 3

Refer to the exhibit. A data engineer sees these metrics from Cloud Monitoring for a deployed Vertex AI Endpoint. What is the most effective action to reduce latency?

Accepted Answer

Increase the number of replicas. The metrics show high CPU utilization and increasing latency, indicating the current instance is overloaded. Increasing the number of replicas distributes the inference requests across multiple instances, reducing per-replica load and lowering response times. This is the most direct way to scale horizontally and address latency caused by resource saturation.

Answer

Switch to batch prediction

Answer

Reduce the machine type

Answer

Enable model quantization

Question 4

A data engineer needs to implement data quality rules and governance policies across multiple data lakes in GCP. They want to automatically discover and catalog data assets, and enforce row-level security. Which two services should they use? (Select TWO)

Accepted Answer

Dataplex. Dataplex (B) provides unified data management including data quality, governance policies, and row-level security via BigQuery. Data Catalog (E) enables automated discovery and cataloging of data assets across multiple data lakes. Together, they satisfy all requirements.

Answer

Security Command Center

Answer

Cloud DLP

Answer

Dataflow

Question 5

Your Dataflow streaming pipeline is experiencing increasing system lag over time. You have enabled autoscaling and the pipeline is using the default streaming engine. Which metric should you monitor in Cloud Monitoring to determine if the pipeline is falling behind due to slow processing or due to a bottleneck in the output sink?

Accepted Answer

Data freshness. In Dataflow streaming pipelines, 'System lag' measures the maximum time an item waits to be processed, but it does not by itself distinguish between a processing bottleneck and a sink bottleneck. To differentiate, monitor 'Data freshness' (the time since the last output was written). If Data freshness is high while System lag is also high, the sink is likely the bottleneck. If System lag is high but Data freshness is low (recent output), the bottleneck is processing. Therefore, the metric that helps determine whether the pipeline is falling behind due to slow processing or a sink bottleneck is Data freshness.

Answer

Worker CPU utilization

Answer

System lag

Answer

Element count

Question 6

A data engineer needs to build a Dataflow pipeline that reads JSON messages from Pub/Sub, transforms them (including filtering, grouping, and enrichment), and writes the results to BigQuery. The pipeline must handle schema evolution in the input messages and minimize data loss. Which THREE settings or features should the engineer use? (Choose THREE.)

Accepted Answer

Use side inputs to enrich the data with reference data from BigQuery. Side inputs allow the pipeline to enrich streaming data with reference data from BigQuery, which is a common requirement for handling schema evolution and enrichment. Option C is correct: a dead letter queue captures messages that fail to parse or validate, preventing data loss and enabling reprocessing of failed messages. Option D is correct: autoscaling adjusts the number of workers dynamically to handle spikes in message volume, ensuring no data is lost due to backpressure. Option B is incorrect: setting `withAllowedLateness` to 0 does not help with schema evolution or minimize data loss; it simply drops late data, which could cause data loss. Option E is incorrect: Streaming Engine improves checkpoint performance but is not directly related to schema evolution or data loss minimization.

Answer

Set the `withAllowedLateness` to 0 for windowing to minimize latency

Answer

Enable Streaming Engine to reduce checkpoint size

Question 7

A payment processing company needs to detect fraudulent transactions in real time. The system must have sub-second latency for high-value transactions and use a machine learning model. Which two components should be part of the architecture? (Choose TWO.)

Accepted Answer

Bigtable to store user profiles and transaction history for fast lookups. Bigtable is a fully managed, scalable NoSQL database that provides consistent sub-10ms latency for high-throughput read/write operations, making it ideal for real-time lookups of user profiles and transaction history in fraud detection. Its ability to handle large volumes of data with low latency supports the sub-second requirement for high-value transactions.

Answer

Cloud Storage for transaction logs

Answer

Cloud SQL to store reference data

Answer

Cloud Functions for long-running batch model training

Question 8

Which TWO are common causes of prediction bias in a deployed machine learning model in production?

Accepted Answer

Data drift between training and serving data distributions.. Data drift refers to changes in the statistical properties of the input features between the training and serving environments. When the distribution of real-world data shifts (e.g., seasonal trends, user behavior changes), the model's predictions become biased even if the model itself hasn't changed. This is a primary cause of prediction bias in production ML systems.

Answer

Model accuracy is too high.

Answer

Model is overfitted to training data.

Answer

Low latency predictions.

Question 9

Your team uses dbt to transform data in BigQuery. You need to schedule dbt runs to refresh materialized tables and views every hour. The transformations include both full refreshes and incremental models. What is the most efficient way to orchestrate these dbt runs on Google Cloud?

Accepted Answer

Use Cloud Composer (Airflow) to schedule and run dbt commands.. Cloud Composer (Airflow) is the recommended orchestration tool for complex workflows like dbt runs, supporting dependencies, retries, and scheduling. Cloud Scheduler alone cannot run dbt directly; it can trigger a Cloud Function to run dbt, but that is less maintainable. Cloud Build is CI/CD, not scheduling. Using a cron job on Compute Engine is possible but not managed.

Answer

Use Cloud Build with a trigger to run dbt every hour.

Answer

Use Cloud Scheduler to trigger a Cloud Function that runs dbt.

Answer

Set up a cron job on a Compute Engine instance to run dbt.

Question 10

A company uses Cloud Composer (Airflow) to orchestrate a daily batch job that runs a custom Python script on a Compute Engine instance. The process is slow because the instance takes 2 minutes to boot. How can you reduce the total runtime?

Accepted Answer

Create a custom image with the script and dependencies pre-installed. Creating a custom image with the script and dependencies pre-installed eliminates the need to install packages or configure the environment at boot time. In Cloud Composer, when a Compute Engine instance is provisioned via a BashOperator or SSHOperator, the boot process includes OS initialization and package installation. A custom image bypasses these steps, reducing boot time from minutes to seconds, directly addressing the 2-minute boot delay.

Answer

Switch to Dataproc Serverless to avoid VM boot time

Answer

Use a larger machine type for faster provisioning

Answer

Use a GPU-accelerated instance to speed up the script

Question 11

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.. 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. Option D is correct because setting the DAG's schedule interval too frequently can overwhelm the executor queue. When the queue is full, new DAG runs are skipped or delayed, which can cause runs to not start even if the Pub/Sub trigger is received.

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 12

A mobile app needs a NoSQL database that supports offline synchronization when the device goes offline and later reconnects. Which Google Cloud database should be used?

Accepted Answer

Firestore. Firestore provides offline persistence for mobile and web apps. It caches data locally and syncs when online. Cloud SQL and Spanner are relational; Bigtable does not have offline sync.

Answer

Cloud Bigtable

Answer

Cloud Spanner

Answer

Cloud SQL

Question 13

A startup wants to build a data lake on Google Cloud using Cloud Storage. They need to store raw data in its original format for future analysis. Which storage class should they use to optimize for cost given that data will be accessed occasionally after the first month?

Accepted Answer

Nearline storage class. Nearline storage class is the optimal choice because it offers low-cost storage for data accessed less than once a month, with a 30-day minimum storage duration. Since the data is accessed occasionally after the first month, Nearline provides significant cost savings over Standard while still offering low-latency access (milliseconds) suitable for analytics. Coldline and Archive have lower storage costs but impose higher retrieval fees and minimum storage durations (90 and 365 days respectively), making them more expensive for data that is accessed occasionally within the first year.

Answer

Coldline storage class

Answer

Standard storage class

Answer

Archive storage class

Question 14

A real-time recommendation system uses a custom container deployed on AI Platform Prediction. The model requires a large in-memory embedding lookup table that is loaded from Cloud Storage at startup. The current startup time is over 5 minutes, causing prediction requests to timeout. Which strategy would most effectively reduce startup time?

Accepted Answer

Reduce the size of the embedding table by using a smaller embedding dimension or fewer categories.. The root cause of the startup timeout is the time required to load the large embedding table from Cloud Storage into memory. Reducing the embedding dimension or the number of categories directly shrinks the data size, which proportionally reduces the load time and avoids the 5-minute startup bottleneck. This is a model architecture change that addresses the fundamental performance constraint without relying on infrastructure workarounds.

Answer

Increase the machine type to one with more memory and CPU.

Answer

Preload the embedding table into a persistent disk and attach it to the container.

Answer

Use a faster storage class for the Cloud Storage bucket, such as Standard instead of Nearline.

Question 15

Refer to the exhibit. The feature store 'my_fs' responds to offline queries but online serving requests fail. What is the most likely cause?

Accepted Answer

Update the existing feature store to enable online serving. The feature store 'my_fs' responds to offline queries but not online serving requests, which indicates that online serving is not enabled for the feature store. In Vertex AI Feature Store, online serving requires a dedicated endpoint and underlying infrastructure (e.g., Bigtable) to serve low-latency requests. Updating the existing feature store to enable online serving (option C) is the correct fix, as it activates the necessary serving resources without recreating the store.

Answer

Create a new feature store with online serving enabled

Answer

Use Cloud Bigtable directly

Answer

Re-import features into a new store