PDE Practice Test 26 — 15 Questions

Question 1

Which THREE best practices should be followed when designing a Dataflow pipeline for real-time data processing?

Accepted Answer

Set up monitoring alerts for system lag and data freshness.. Option A is correct because monitoring system lag (the difference between event time and processing time) and data freshness is essential for detecting pipeline backlog and ensuring timely real-time results in Dataflow. Option C is correct because watermark estimation is how Dataflow tracks event-time completeness and determines when to fire window results while gracefully handling late-arriving data. Option E is correct because idempotent sinks allow retries and replays to produce the same result, which is required to achieve exactly-once processing semantics in streaming pipelines. Option B is not appropriate because static side inputs loaded once at pipeline start cannot reflect changing reference data in a real-time stream, where dynamic or periodically refreshed side inputs are needed. Option D is not a best practice because global windows with early triggers discard windowing structure and can produce incorrect or incomplete aggregations for unbounded data, whereas proper event-time windowing with allowed lateness is preferred.

Answer

Use static side inputs that are loaded once at pipeline start.

Answer

Use global windows with early triggers for low latency.

Question 2

A data engineer needs to schedule a recurring transfer of data from a partner's Amazon S3 bucket to a Cloud Storage bucket for further processing. Which THREE components or configurations are necessary? (Choose 3)

Accepted Answer

Specification of the source S3 bucket and destination GCS bucket. Storage Transfer Service is the Google Cloud service designed to move data from external sources like Amazon S3 into Cloud Storage, so the transfer job must be defined as a scheduled transfer job (option C), which controls the recurring execution. Every transfer job requires the source and destination locations to be identified, so specifying the source S3 bucket and destination GCS bucket (option B) is mandatory for the job to know what to copy and where to place it. Because the source is an external AWS S3 bucket, Storage Transfer Service must authenticate to AWS, which requires AWS credentials such as an access key ID and secret access key (option E). A VPC network configuration (option A) is not required because Storage Transfer Service is a fully managed service that does not need customer VPC networking for S3-to-GCS transfers. A Pub/Sub topic (option D) is optional, used only if you want notifications about transfer completion, not a necessary component for the transfer itself.

Answer

A VPC network configuration

Answer

A Pub/Sub topic to notify completion

Question 3

Your company uses Pub/Sub to ingest clickstream data. Messages must be processed in order for the same user_id. How should you configure the Pub/Sub subscription to guarantee ordering?

Accepted Answer

Use a pull subscription with enable_message_ordering=true. Pub/Sub message ordering is enabled per-subscription by setting enable_message_ordering=true, and ordering keys (such as user_id) must be set on published messages. With ordering enabled, Pub/Sub delivers messages with the same ordering key in publish order to a single subscriber, satisfying the per-user_id ordering requirement.

Answer

Use a pull subscription with exactly-once delivery enabled

Answer

Use a push subscription with acknowledgement deadline set to 600 seconds

Answer

Use a push subscription with a dead letter topic

Question 4

A streaming Dataflow pipeline needs to be updated without draining the existing pipeline. Which update strategy should be used?

Accepted Answer

Replace the job with a new job using the same pipeline name. Dataflow supports replacing a running streaming job with an updated one using the same pipeline name via the 'Replace job' (or update) mechanism, which performs an in-place upgrade that preserves the pipeline's state and does not require draining. This is the supported path for updating streaming pipelines without stopping data ingestion. Draining, cancelling, or stopping the job all interrupt processing and are not the recommended no-drain update strategy.

Answer

Drain the pipeline first, then start a new one

Answer

Use a different pipeline name and cancel the old one

Answer

Stop the job, update the code, and restart

Question 5

A company uses Cloud Build to deploy containerized applications. They want to ensure build and deployment quality. Which THREE steps should they include in their CI/CD pipeline? (Choose three.)

Accepted Answer

Scan container images for vulnerabilities using Container Analysis.. Option A is correct because integrating Container Analysis into the Cloud Build pipeline scans container images for known vulnerabilities (CVEs) before they are pushed or deployed, which directly enforces build and deployment quality by catching insecure dependencies early. Option D is correct because canary deployments with gradual traffic shifting let you release a new revision to a small percentage of users first and monitor error rates and latency, so a bad build can be rolled back before it affects all production traffic. Option E is correct because pinning base image digests in the Dockerfile (e.g., FROM image@sha256:...) makes builds reproducible and immutable, preventing an unexpected or compromised 'latest' base image from silently changing the artifact. Option B is wrong because unit tests must run before deployment, not after, to gate the pipeline on code correctness. Option C is wrong because deploying directly to production on every commit bypasses testing, review, and staged rollout, which is the opposite of ensuring quality.

Answer

Run unit tests after deployment.

Answer

Deploy directly to production on every commit.

Question 6

A Dataflow streaming pipeline that uses global windows and triggers every 5 seconds is experiencing increasing lag and high system latency. The pipeline reads from Pub/Sub, transforms data with a ParDo, and writes to BigQuery. Which action is most likely to reduce lag?

Accepted Answer

Replace the global window with a sliding window of 1 minute.. B is correct because sliding windows of 1 minute allow the pipeline to process data in overlapping fixed-size windows, which can reduce the buildup of data in memory compared to global windows. Global windows with frequent triggers (every 5 seconds) can cause unbounded state growth and high latency as the pipeline must maintain state for all elements until the trigger fires, whereas sliding windows naturally bound the data per window and enable more efficient watermark and trigger management in Dataflow.

Answer

Use a session window to group related events.

Answer

Change the trigger to processing time instead of event time.

Answer

Increase the number of workers manually.

Question 7

Drag and drop the steps to migrate an on-premises MySQL database to Cloud SQL using Database Migration Service into the correct order.

Accepted Answer

First, prepare the source database by enabling binary logging and creating a replication user. Second, create a target Cloud SQL instance. Third, create a connection profile in Database Migration Service. Fourth, create and start the migration job, then perform the final cutover (promote Cloud SQL).. Database Migration Service enables minimal-downtime migrations using replication.

Answer

First, create a target Cloud SQL instance. Second, prepare the source database by enabling binary logging and creating a replication user. Third, create a connection profile in Database Migration Service. Fourth, create and start the migration job, then perform the final cutover.

Answer

First, prepare the source database by enabling binary logging and creating a replication user. Second, create a connection profile in Database Migration Service. Third, create a target Cloud SQL instance. Fourth, create and start the migration job, then perform the final cutover.

Answer

First, prepare the source database by enabling binary logging and creating a replication user. Second, create and start the migration job, then perform the final cutover. Third, create a target Cloud SQL instance. Fourth, create a connection profile in Database Migration Service.

Question 8

You need to inspect a BigQuery table for sensitive data such as credit card numbers and apply masking. Which GCP service should you use to identify and de-identify the data?

Accepted Answer

Cloud Data Loss Prevention (DLP). Cloud Data Loss Prevention (DLP) is Google Cloud's service for discovering, classifying, and de-identifying sensitive data such as credit card numbers, SSNs, and emails. It provides infoType detectors (including CREDIT_CARD_NUMBER) and de-identification transforms like masking, tokenization, and format-preserving encryption. It integrates directly with BigQuery for scanning tables and applying masking at query or storage time.

Answer

IAM Recommender

Answer

Cloud KMS

Answer

Dataplex

Question 9

Your team wants to continuously monitor a deployed model's performance in production. They need to detect when the model's predictions become unreliable due to changes in the real world (e.g., new customer behavior). Which Vertex AI service should they use?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the correct choice because it is specifically designed to continuously track a deployed model's prediction quality over time, detecting issues like data drift, feature drift, and prediction skew that indicate the model's reliability is degrading due to changes in the real world. It automatically compares incoming prediction data against a baseline training dataset and alerts when statistical distributions shift beyond configurable thresholds, enabling proactive retraining or intervention.

Answer

Vertex AI Explainable AI

Answer

Vertex AI Experiments

Answer

Vertex AI Prediction

Question 10

A company is migrating ML workflows to Vertex AI Pipelines. They want to ensure best practices for pipeline reproducibility and debugging. Which THREE actions should they take? (Choose three.)

Accepted Answer

Set a random seed for all training components. Option A is correct because setting a random seed for all training components makes stochastic operations (weight initialization, data shuffling, dropout) deterministic, so identical inputs and parameters yield identical outputs, which is essential for reproducibility. Option B is correct because storing artifacts in Cloud Storage with versioned prefixes preserves immutable, uniquely addressable lineage for each pipeline run, allowing you to trace and compare outputs across executions for debugging. Option C is correct because pinning all dependencies (exact library versions in the training images) prevents silent behavioral changes from dependency drift, ensuring the same code produces the same results over time. Option D is not correct because dynamic pipeline parameters vary inputs per run and, by themselves, do not improve reproducibility or debugging; they are a flexibility feature, not a best practice for deterministic reruns. Option E is not correct because conditional execution based on previous component outputs changes the pipeline's control flow at runtime, which can make runs harder to reproduce and debug rather than more deterministic.

Answer

Use dynamic pipeline parameters for each run

Answer

Use conditional execution based on previous component outputs

Question 11

A team wants to enforce data quality rules on BigQuery tables using Dataplex. They need to run column-level checks for null values and row-level checks for value ranges on a schedule. Which Dataplex feature should they use?

Accepted Answer

Dataplex Data Quality Tasks. Dataplex Data Quality Tasks allow you to define and run data quality rules on BigQuery tables, including column-level checks (e.g., null checks) and row-level checks (e.g., value ranges). These tasks can be scheduled to run periodically, and they generate results that can be monitored. This is the native Dataplex feature for enforcing data quality.

Answer

Dataplex Data Profiling

Answer

BigQuery stored procedures with scheduled queries

Answer

Cloud DLP inspection jobs

Question 12

A data engineer needs to design a schema in BigQuery for a dataset that contains customer orders. Each order has a header and multiple line items. Queries frequently need to retrieve the entire order including line items. Which schema design is MOST performant and cost-effective?

Accepted Answer

Use nested and repeated fields (orders table with line items as REPEATED RECORD). BigQuery is optimized for denormalized schemas using nested and repeated fields (REPEATED RECORD). Storing line items as a repeated record within the orders table avoids expensive JOIN operations, reduces data shuffling, and allows BigQuery to scan only the necessary columns, making queries that retrieve entire orders with line items both faster and more cost-effective.

Answer

Store all data in a flat table with repeated order info per line item

Answer

Normalize into separate orders and line_items tables, join on order_id

Answer

Use a partitioned table on order date

Question 13

A company is migrating an on-premises Hadoop cluster to Google Cloud. They need to run existing Spark jobs with minimal modification. Which THREE strategies should they consider? (Choose THREE.)

Accepted Answer

Use Cloud Dataproc with Spark and Hive components.. Option B is correct because Cloud Dataproc is a managed Hadoop/Spark service that natively runs Apache Spark and Hive, so existing Spark jobs can be migrated with minimal code changes. Option C is correct because Dataproc jobs commonly read and write data in Cloud Storage (gs://) instead of HDFS, which is the standard cloud-native replacement for on-premises HDFS storage and requires only path changes. Option E is correct because the Dataproc Jobs API lets you submit Spark jobs programmatically to a cluster, preserving the existing job-submission workflow with minimal modification. Option A is not appropriate because migrating everything to BigQuery would require rewriting analytics logic and does not run existing Spark jobs as-is. Option D is not appropriate because rewriting Spark jobs as Dataflow pipelines is a significant code and framework change, contradicting the minimal-modification requirement.

Answer

Migrate to BigQuery for all analytics.

Answer

Rewrite Spark jobs as Dataflow pipelines.

Question 14

A company wants to use Eventarc to trigger a Cloud Run service when new objects are created in a GCS bucket. They also need to filter events for a specific bucket and object prefix. Which THREE resources must exist or be created?

Accepted Answer

Cloud Storage bucket. Option A (Cloud Storage bucket) is correct because Eventarc's Cloud Storage events originate from an actual GCS bucket, and the trigger's bucket filter must reference that existing bucket where objects are created. Option D (Eventarc trigger) is correct because the trigger is the resource that connects the event source to the destination, defines the event type (e.g., google.cloud.storage.object.v1.finalized), and applies the bucket and object-prefix attribute filters. Option E (Cloud Run service) is correct because it is the event destination that Eventarc invokes when a matching object-creation event fires. Option B (Pub/Sub topic) is not required because Eventarc manages its own internal Pub/Sub transport for Cloud Storage events; you do not create or specify a topic. Option C (Cloud Scheduler job) is not required because scheduling is unrelated to event-driven object-creation triggers.

Answer

Pub/Sub topic

Answer

Cloud Scheduler job

Question 15

A team is deploying a complex model with multiple preprocessing steps. They want to ensure consistent preprocessing during training and serving. Which three approaches can achieve this? (Select 3)

Accepted Answer

Store preprocessing logic in a shared Python module. Option A is correct because placing preprocessing logic in a shared Python module lets both the training code and the serving code import and execute the exact same transformation functions, eliminating drift between environments. Option D is correct because Vertex AI Feature Transform Engine lets you define transformations (e.g., in a feature store or as part of a Vertex AI pipeline) that are applied consistently at both training and online/batch serving time. Option E is correct because embedding preprocessing logic directly in the model graph (for example, as tf.keras preprocessing layers or a tf.transform graph) ensures the saved model performs the same transformations during inference as were applied during training. Option B is not appropriate because a separate preprocessing service introduces an extra network hop, latency, and a potential point of failure, and it does not by itself guarantee identical logic between training and serving. Option C is incorrect because maintaining two separate pipelines for training and serving is precisely the practice that causes training-serving skew.

Answer

Use a separate preprocessing service called from the model

Answer

Use two separate pipelines for training and serving