PDE Practice Test 14 — 15 Questions

Question 1

You are designing a Dataflow pipeline for processing real-time clickstream data. The pipeline must group events into 30-second windows and handle late data up to 5 minutes. You want to output partial results every 10 seconds for low-latency monitoring. Which THREE configurations should you use? (Choose three.)

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

Accepted Answer

Use fixed windows of 30 seconds. Option C is correct because fixed (tumbling) 30-second windows partition the clickstream into non-overlapping 30-second intervals, which is the required grouping for this scenario. Option D is correct because setting allowed lateness to 5 minutes lets the pipeline retain window state and accept events that arrive up to 5 minutes after the window closes, matching the late-data requirement. Option E is correct because a trigger with early firings every 10 seconds emits speculative partial results before the window closes, providing the requested low-latency monitoring output. Option A is not appropriate because sliding windows with a 10-second period would create overlapping 30-second windows and duplicate events across windows, which is not the specified grouping. Option B is not appropriate because a trigger that fires only after the end of the window would produce results only at window closure and would not deliver the required 10-second partial outputs.

Answer

Use sliding windows of 30 seconds with a 10-second period

Answer

Use a trigger that fires after the end of the window

Question 2

An application needs to store user profile data in a document database with flexible schema. The data is accessed frequently from a mobile app. Which Google Cloud database is BEST suited?

Accepted Answer

Cloud Firestore. Cloud Firestore is a NoSQL document database designed for mobile and web app development, offering flexible schema, real-time data synchronization, and automatic scaling. It directly supports frequent reads from mobile apps through its client SDKs and offline persistence, making it the best fit for storing user profile data with varying attributes.

Answer

Cloud Bigtable

Answer

BigQuery

Answer

Cloud Spanner

Question 3

Which BigQuery function can be used to retrieve the value of a column from the previous row within a partition, ordered by a timestamp?

Accepted Answer

LAG(). LAG() is a window function that returns the value of a column from a row that is a specified number of rows before the current row within the partition. LEAD() retrieves from a following row.

Answer

FIRST_VALUE()

Answer

ROW_NUMBER()

Answer

LEAD()

Question 4

A company is designing a data lake on Cloud Storage with BigLake tables for unified governance. Which TWO statements about BigLake are correct? (Choose 2.)

Accepted Answer

BigLake tables allow querying data in Cloud Storage using BigQuery without loading into BigQuery storage.. Option B is correct because BigLake tables are external tables that let BigQuery query data residing in Cloud Storage (Parquet, ORC, Avro, JSON, CSV, Iceberg, Delta) directly, without ingesting it into BigQuery's native storage. Option D is correct because BigLake extends BigQuery's fine-grained governance to external data, supporting row-level security and column-level security (via policy tags in Data Catalog) so access controls apply uniformly across the lakehouse. Option A is wrong because BigLake's defining feature is avoiding the load into BigQuery storage; that describes native BigQuery tables. Option C is wrong because BigLake handles structured and semi-structured formats in Cloud Storage, not only structured data in BigQuery storage. Option E is wrong because BigLake does not perform automatic format conversion of CSV to Parquet; it queries files in their existing format.

Answer

BigLake requires data to be loaded into BigQuery storage.

Answer

BigLake only supports structured data in BigQuery storage.

Answer

BigLake automatically converts CSV files to Parquet.

Question 5

A company wants to orchestrate a multi-step data processing workflow that includes calling a Cloud Run service, waiting for its completion, and then running a BigQuery query. The workflow should be serverless and integrate with Cloud Events. Which Google Cloud service should they use?

Accepted Answer

Cloud Workflows. Cloud Workflows is the correct choice because it is a serverless workflow orchestrator that can coordinate multi-step processes involving Cloud Run and BigQuery. It natively supports waiting for asynchronous operations (like Cloud Run job completion) via its 'call' and 'wait' steps, and it can trigger subsequent steps such as BigQuery queries. Additionally, Cloud Workflows can be triggered by Cloud Events, making it fully integrated with the event-driven architecture described.

Answer

Eventarc

Answer

Cloud Composer

Answer

Cloud Dataflow

Question 6

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 7

A company is building a data pipeline that ingests streaming data from Pub/Sub, transforms it with Dataflow, and loads it into BigQuery. They want to handle malformed messages that cannot be parsed. Which TWO actions should they implement for error handling? (Choose 2)

Accepted Answer

Use a dead letter sink to write malformed messages to Cloud Storage or Pub/Sub for later analysis. Option C is correct because a dead letter sink (often implemented in Dataflow via a tagged output or a separate Pub/Sub topic/Cloud Storage path) captures unparseable records so they can be inspected and reprocessed without losing data or halting the pipeline. Option E is correct because logging the parsing error and continuing lets the pipeline keep processing valid messages, which is essential for a resilient streaming pipeline where one bad record should not stop the flow. Option A is not appropriate because silently dropping malformed messages loses data and provides no visibility for debugging or remediation. Option B is not the right pattern because a side input is used to supply supplementary data to a DoFn, not to filter or quarantine malformed records. Option D is wrong because raising an exception in the DoFn would fail the pipeline immediately, causing unnecessary downtime and blocking valid messages from being processed.

Answer

Configure the pipeline to drop malformed messages silently

Answer

Use a side input to filter out malformed messages

Answer

Raise an exception in the DoFn to fail the pipeline immediately

Question 8

You deployed a model on Vertex AI Endpoints using a custom container. The model serves predictions but the latency is higher than expected. You suspect the container is not making full use of the CPU resources. What should you do to reduce latency?

Accepted Answer

Modify the container to use multi-threading or increase the number of workers in the prediction server (e.g., Gunicorn workers).. High latency in a CPU-based custom container often stems from underutilizing available CPU cores. By increasing the number of workers (e.g., Gunicorn workers) or enabling multi-threading, you allow the prediction server to handle multiple requests concurrently, reducing queue time and improving throughput. This directly addresses the symptom of the container not making full use of CPU resources.

Answer

Enable response caching on the endpoint.

Answer

Change the machine type to a GPU-accelerated machine.

Answer

Increase the number of nodes by adjusting autoscaling limits.

Question 9

Refer to the exhibit. What is the cause of this error?

Accepted Answer

The machine type flag is only used during model deployment, not endpoint creation. The error occurs because the `machine_type` flag is only valid during model deployment (when creating a deployment in Vertex AI), not during endpoint creation. When creating an endpoint, you specify the endpoint name and region, but the machine type is configured later when deploying a model to that endpoint. Attempting to set `machine_type` during endpoint creation causes a validation error because the API does not accept that parameter at that stage.

Answer

The endpoint name already exists

Answer

The user must specify a model name

Answer

The region is missing

Question 10

Your streaming Dataflow pipeline reads from Pub/Sub, enriches data with a side input, and writes to BigQuery. You need to update the enrichment logic without draining the pipeline, to minimize data loss and maintain exactly-once semantics. What should you do?

Accepted Answer

Use the Dataflow job update mechanism to replace the pipeline with a new version.. The Dataflow job update mechanism allows you to replace a running pipeline's code with a new version without draining or stopping it, preserving the existing state and minimizing data loss. This mechanism supports exactly-once semantics by ensuring that all in-flight elements are processed exactly once, even after the update, by maintaining the pipeline's checkpoint and watermark state.

Answer

Cancel the pipeline and create a new one with the updated code.

Answer

Stop the pipeline, update the code, and restart from the latest snapshot.

Answer

Drain the pipeline, update the code, and restart with the same job ID.

Question 11

A data engineer needs to process data in a Dataflow pipeline that reads from a Pub/Sub topic. The pipeline must group events into 5-minute windows and compute the average value per key. Which Beam transform should they use after windowing?

Accepted Answer

Combine.perKey. Combine.perKey is the correct transform because it performs a per-key aggregation (here, computing the average value) after windowing, combining elements within each key and window efficiently. It is a fused Combine operation that reduces data before shuffling, making it more efficient than GroupByKey followed by a separate aggregation. Since the requirement is to compute an average per key within 5-minute windows, Combine.perKey directly expresses that intent.

Answer

ParDo

Answer

GroupByKey

Answer

CoGroupByKey

Question 12

A company is designing a data processing system that must handle both batch and streaming workloads with unified pipeline code. Which two Google Cloud services are most suitable for implementing a unified batch and streaming pipeline? (Choose TWO.)

Accepted Answer

Apache Beam SDK. Apache Beam SDK (C) provides a unified programming model that allows developers to write a single pipeline that can execute in both batch and streaming modes without code changes. It abstracts the underlying execution engine, making it the correct choice for unified pipeline code.

Answer

Cloud Data Fusion

Answer

BigQuery

Answer

Cloud Dataproc

Question 13

A Dataflow streaming pipeline reads from Pub/Sub, applies a ParDo that uses a side input from a BigQuery table (refreshed hourly), and writes to BigQuery. The side input is large and causes increased latency and worker OOM errors. Which design change solves this?

Accepted Answer

Use a stateful ParDo and store the lookup data in an external cache like Cloud Bigtable, performing lookups per element.. Moving the large lookup data to an external cache like Cloud Bigtable offloads memory pressure from workers, eliminating OOM errors. The side input broadcast approach keeps the entire dataset in each worker's memory, which causes OOM when the data is large. Using an external cache allows per-element lookups without storing the entire dataset in memory, reducing latency by avoiding broadcast overhead.

Answer

Increase the side input broadcast frequency to update more often.

Answer

Split the pipeline into two: one to load the side input, the other to process main input.

Answer

Use smaller worker machine types to distribute memory across more workers.

Question 14

You need to ingest streaming data from a custom application into BigQuery with exactly-once semantics and low latency. The data volume is up to 10 MB/s. Which TWO services should you combine?

Accepted Answer

Pub/Sub. Option A (Pub/Sub) is correct because it provides a highly scalable, low-latency messaging service that decouples the custom application from downstream processing, ingesting up to 10 MB/s and beyond while buffering the stream reliably. Option D (Dataflow with Storage Write API) is correct because Dataflow can read from Pub/Sub and write to BigQuery using the Storage Write API, which supports exactly-once semantics via its exactly-once delivery mode and provides low-latency, high-throughput ingestion. Together, Pub/Sub plus Dataflow with the Storage Write API form the recommended architecture for exactly-once streaming ingestion into BigQuery. Option B (Cloud Functions) is not appropriate here because it is event-driven and not designed for sustained high-throughput streaming pipelines with exactly-once guarantees. Option C (BigQuery legacy streaming inserts) does not provide exactly-once semantics and is being deprecated in favor of the Storage Write API. Option E (Datastream) is a change data capture (CDC) and replication service for databases, not a general-purpose streaming ingestion path for custom application data.

Answer

Cloud Functions

Answer

BigQuery legacy streaming inserts

Answer

Datastream

Question 15

A data engineer uses Cloud Composer to orchestrate a daily batch pipeline. A downstream task should only start after an upstream BigQuery load job finishes successfully and a specific file appears in Cloud Storage. Which combination of operators should the engineer use in the Airflow DAG?

Accepted Answer

BigQueryInsertJobOperator and GCSObjectExistenceSensor with upstream dependency. The engineer needs a BigQuery load job to finish successfully and a specific file to appear in Cloud Storage before a downstream task starts. The correct combination is BigQueryInsertJobOperator to run and wait for the BigQuery job, and GCSObjectExistenceSensor to check for the file, with the sensor set as an upstream dependency of the downstream task. This ensures both conditions are met before proceeding.

Answer

BigQueryInsertJobOperator with wait_for_downstream=True

Answer

DataflowPythonOperator and GCSObjectExistenceSensor

Answer

BigQueryOperator and FileSensor with downstream dependency