PDE Multiple Select — 20 Questions

Question 1

You are using Cloud Workflows to orchestrate a series of API calls. You need to handle errors and retries. Which THREE features of Cloud Workflows can you use? (Choose THREE.)

Accepted Answer

Use try/except blocks to catch and handle errors.. Cloud Workflows supports steps with retry policies, try/except blocks for error handling, and conditional (if-else) logic for branching. It does not have built-in built-in step-level logging (logging is done via Cloud Logging), and there is no built-in load balancer integration.

Answer

Integrate with Cloud Load Balancing for high availability.

Answer

Enable automatic logging for each step.

Question 2

Which TWO are best practices for monitoring a deployed machine learning model in production on Vertex AI?

Accepted Answer

Enable Vertex AI Model Monitoring to track feature drift and skew. Vertex AI Model Monitoring automatically tracks feature drift and skew by comparing the serving data distribution against the training data distribution using statistical tests like the Kolmogorov-Smirnov test. This is a best practice for detecting data quality issues that can degrade model performance in production.

Answer

Set up a weekly retraining pipeline triggered by calendar schedule

Answer

Monitor the training job duration to detect anomalies

Answer

Monitor the model's file size to ensure it hasn't changed

Question 3

A data warehouse team uses Cloud BigQuery for analytics. They want to optimize query performance and reduce costs. Which three actions should they take? (Choose 3)

Accepted Answer

Use partitioned tables on time columns. Partitioning tables on time columns (e.g., DATE, TIMESTAMP) in BigQuery allows the query engine to perform partition pruning, scanning only the relevant partitions instead of the entire table. This directly reduces the amount of data read, lowering query costs and improving performance by limiting I/O to the necessary time range.

Answer

Use automatic reclustering

Answer

Use BI Engine for all queries

Question 4

Which TWO steps are required to deploy a custom scikit-learn model to Vertex AI for online predictions?

Accepted Answer

Save the model using joblib or pickle. Scikit-learn models must be serialized using joblib or pickle to be saved as a model artifact that can be uploaded to Vertex AI. Vertex AI's pre-built prediction containers for scikit-learn expect the model file to be in this format (typically model.joblib or model.pkl) to serve online predictions.

Answer

Write a custom prediction routine

Answer

Containerize the model using Docker

Answer

Create a Vertex AI Endpoint manually

Question 5

A company is migrating their on-premises Hadoop/Spark workloads to Google Cloud. They need a fully managed service that supports existing Spark jobs with minimal code changes, allows autoscaling, and provides integration with Cloud Storage and BigQuery. The team also wants to avoid managing cluster infrastructure and pay only for what they use. Which TWO services meet these requirements? (Choose two.)

Accepted Answer

Dataproc Serverless (Spark). Dataproc Serverless allows running Spark jobs without managing clusters, with autoscaling and pay-per-use pricing. Dataproc on GKE enables running Spark on Kubernetes with autoscaling and is fully managed. Standard Dataproc requires cluster management and is not serverless. Dataflow is for Beam, not Spark. Cloud Composer is for orchestration, not data processing.

Answer

Standard Dataproc cluster with preemptible workers

Answer

Cloud Composer with Spark

Answer

Dataflow with Spark Runner

Question 6

A company wants to track data lineage for their BigQuery tables to understand how data flows from source to derived tables. Which TWO Google Cloud services can be used to capture and visualize data lineage? (Choose 2.)

Accepted Answer

Dataplex Data Lineage. Dataplex provides a comprehensive Data Lineage feature that automatically captures lineage for BigQuery jobs. Additionally, the BigQuery Lineage API (part of Data Catalog) allows you to retrieve lineage programmatically.

Answer

Vertex AI Feature Store

Answer

Cloud Composer

Answer

Cloud Data Fusion

Question 7

A data pipeline processes sensitive customer data. You need to ensure that only authorised users can query the data in BigQuery, and that the data is encrypted at rest and in transit. Which THREE steps should you take? (Choose three.)

Accepted Answer

Create authorised views in BigQuery to restrict access to sensitive columns. Encryption at rest is enabled by default with CMEK or CSEK. In-transit encryption is default for BigQuery. Authorised views provide fine-grained access control. IAM roles control dataset access.

Answer

Use a Cloud VPN to encrypt data in transit between on-premises and Google Cloud

Answer

Grant the bigquery.dataViewer role at the dataset level to all users

Question 8

A data engineer is preparing a dataset for ML training in Vertex AI. The dataset includes a timestamp column, a categorical column with high cardinality (1000 distinct values), and a numerical column with outliers. Which two preprocessing steps should they apply? (Choose TWO)

Accepted Answer

Label encode the categorical column. One-hot encoding for high cardinality may be too sparse; label encoding (ordinal encoder) is more common. Winsorizing clips outliers.

Answer

Drop the timestamp column

Answer

Normalize the numerical column using Z-score

Answer

One-hot encode the categorical column

Question 9

A data engineer is building a Cloud Workflows workflow that orchestrates multiple Cloud Functions and API calls. The workflow should handle transient failures with retries and send a notification to a Pub/Sub topic if the workflow ultimately fails. Which THREE steps should the engineer include in the workflow definition?

Accepted Answer

Use the 'googleapis.pubsub.v1.projects.topics.publish' connector to send a failure notification.. Workflows supports retry policies via 'retry' blocks, conditional steps using 'switch', and Pub/Sub publishing via the 'googleapis.pubsub.v1.projects.topics.publish' connector. The 'try/catch' is not a Workflows construct; instead, use 'step' with 'retry' and 'on_error' for failure handling. 'for' loops are for iteration, not error handling.

Answer

Use a 'for' loop to iterate over retries.

Answer

Use a 'try' / 'catch' block to handle exceptions and route to a failure step.

Question 10

A company uses Cloud Composer (Airflow) to orchestrate pipelines. They want to implement a pattern where a task polls for a file arrival in Cloud Storage and then triggers subsequent tasks. Which THREE Airflow concepts are essential? (Choose 3)

Accepted Answer

Sensors (e.g., GCSObjectExistenceSensor). A is correct because Sensors are a specialized type of operator designed to wait for a specific condition, such as file arrival in Cloud Storage. The `GCSObjectExistenceSensor` in Cloud Composer (Airflow) polls Google Cloud Storage at a configurable interval until the target file exists, making it the precise tool for this file-arrival polling pattern.

Answer

Operators (e.g., PythonOperator)

Answer

SubDAGs for grouping tasks

Question 11

Which THREE of the following are best practices when designing a Cloud Dataflow pipeline for batch processing? (Choose three.)

Accepted Answer

Use side inputs to hold a large lookup table that is read in every element.. Side inputs in Cloud Dataflow are designed to efficiently broadcast a read-only dataset (like a lookup table) to all parallel workers. When the side input is a large but static dataset, Dataflow can cache it in memory or on disk across workers, avoiding repeated external lookups and reducing per-element processing overhead. This pattern is especially effective for batch processing where the side input is read once and reused across all elements.

Answer

Use mutable state within ParDo to track running totals.

Answer

Always insert a Reshuffle transform after every GroupByKey to redistribute data.

Question 12

Which TWO best practices should be followed when managing multiple model versions on Vertex AI Endpoints for a production system?

Accepted Answer

Use traffic splitting to gradually shift traffic to a new version while monitoring performance.. Vertex AI Endpoints support traffic splitting, allowing you to route a percentage of inference requests to a new model version while the rest goes to the existing version. This enables gradual rollout, monitoring of performance metrics (e.g., latency, error rate), and safe rollback without downtime. It is a best practice for production systems to validate a new version before fully cutting over.

Answer

Always keep all historical versions deployed to enable fast rollback.

Answer

If two versions share the same endpoint, they must have exactly the same machine type.

Answer

Use the same endpoint for multiple versions and adjust min_replica_count, max_replica_count for each version.

Question 13

You are designing a data pipeline that ingests streaming data from Pub/Sub, processes it with Dataflow, and writes to BigQuery. You need to ensure that schema changes in the incoming data (new fields) are handled without pipeline failure. Which THREE steps should you take? (Choose THREE.)

Accepted Answer

Set the BigQuery table schema to allow automatic addition of new fields by using 'ignore_unknown_values'.. To handle schema drift, BigQuery allows schema relaxation (adding nullable columns) via DDL. Dataflow can update the destination table schema using BigQuery APIs (e.g., set the schema to include unknown fields). Alternatively, using BigQuery's automatic schema detection with 'ignore_unknown_values' can allow new fields to be added automatically. Setting all fields to NULLABLE in advance is not practical. Setting the table to require all fields causes failures on unknown fields.

Answer

Configure the BigQuery table to require all fields, causing the pipeline to fail on unknown fields.

Answer

Set all columns in the BigQuery table as NULLABLE from the start.

Question 14

Your company is building a data processing system that ingests sensor data from millions of devices, processes it in near real-time to detect anomalies, and stores raw and processed data for long-term analytics. The system must meet a 99.9% uptime SLA and minimize data loss. Which THREE design choices are best? (Choose three.)

Accepted Answer

Use Cloud Pub/Sub as the ingestion layer with a dead-letter topic to capture unprocessed messages.. Cloud Pub/Sub with a dead-letter topic ensures that messages that cannot be processed are captured and not lost, directly supporting the requirement to minimize data loss. The dead-letter topic allows for later reprocessing or analysis of failed messages, which is critical for meeting a 99.9% uptime SLA by preventing message backlogs from blocking the ingestion pipeline.

Answer

Store raw data in Cloud Bigtable and processed data in Cloud Storage.

Answer

Use a global Cloud Load Balancer in front of the Dataflow workers.

Question 15

Which TWO options can help reduce costs for a Dataflow batch pipeline that processes 100 GB of data daily from Cloud Storage? (Choose 2)

Accepted Answer

Use FlexRS (Flexible Resource Scheduling). FlexRS (Flexible Resource Scheduling) allows you to run batch workloads on a discounted, flexible schedule. It reduces costs by offering lower prices in exchange for the job being able to wait up to 6 hours for resources to become available. This is ideal for a daily 100 GB batch pipeline that can tolerate some scheduling delay.

Answer

Use Dataflow Prime (now Dataflow Runner v2)

Answer

Use high-memory machine types

Answer

Use Streaming Engine

Question 16

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.. Container Analysis (now part of Artifact Registry) scans container images for known vulnerabilities (CVEs) in OS packages and application dependencies. Integrating this scan into the CI/CD pipeline ensures that only compliant images proceed to deployment, preventing vulnerable code from reaching production. This directly supports the 'Ensuring solution quality' domain by enforcing security gates before release.

Answer

Run unit tests after deployment.

Answer

Deploy directly to production on every commit.

Question 17

A company is planning to migrate a legacy batch ETL pipeline to Google Cloud. The pipeline involves reading from a relational database, transforming data, and writing to a data warehouse. Which three Google Cloud services can be used as the orchestration layer? (Choose three.)

Accepted Answer

Cloud Scheduler. Cloud Scheduler is a fully managed cron job service that can trigger orchestration workflows on a schedule. It is correct because it can initiate batch ETL pipelines by sending HTTP requests to Cloud Run, Cloud Functions, or Pub/Sub, making it a lightweight orchestration trigger for scheduled batch jobs.

Answer

Cloud Dataproc

Answer

Cloud Dataflow

Question 18

A company wants to implement a robust MLOps lifecycle on Google Cloud. Which THREE components are essential?

Accepted Answer

Vertex AI Model Registry for versioning. Vertex AI Model Registry is essential for versioning because it provides a centralized repository to track, manage, and deploy different versions of trained ML models. This ensures reproducibility, auditability, and the ability to roll back to previous versions, which is critical for a robust MLOps lifecycle.

Answer

Pub/Sub for event-driven retraining

Answer

Cloud SQL for model metadata

Question 19

A data scientist needs to perform feature engineering for a machine learning model using Vertex AI. They want to preprocess data using a pipeline that includes scaling, one-hot encoding, and handling missing values. Which TWO services can they use to define and execute this preprocessing pipeline? (Choose 2.)

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines is the recommended service for building and running ML pipelines, including preprocessing steps. Alternatively, you can use BigQuery SQL for feature engineering directly on the data, then export the processed data for training. Cloud Dataflow is an option for batch/streaming data processing but is not specific to ML pipelines. Cloud Functions and Dataproc are less suitable for this purpose.

Answer

Cloud Dataproc

Answer

Cloud Dataflow

Answer

Cloud Functions

Question 20

A data engineering team is operationalizing a machine learning model for real-time inference. They need to monitor the model's performance in production. Which THREE types of monitoring should they implement? (Choose three.)

Accepted Answer

Model accuracy decay. Model accuracy decay (A) is critical because in production, the model's predictive performance can degrade over time due to changes in the underlying data distribution or business logic. Monitoring accuracy decay allows the team to detect when the model no longer meets its performance baseline, triggering retraining or rollback. This is a standard practice in MLOps for maintaining model reliability.

Answer

Model re-training frequency

Answer

Training pipeline failures