PDE Practice Test 8 — 10 Questions

Question 1

Your team uses a CI/CD pipeline with Cloud Build to train and deploy ML models on Vertex AI. You want to ensure that only models that pass validation checks (e.g., accuracy threshold, fairness metrics) are promoted to production. What is the best way to implement this?

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

Accepted Answer

In the Cloud Build pipeline, after training, run validation scripts. If validation passes, deploy to a staging endpoint for manual approval, then promote to production.. It integrates validation directly into the CI/CD pipeline using Cloud Build, ensuring that only models passing specific checks (e.g., accuracy threshold, fairness metrics) are promoted. By running validation scripts after training and requiring manual approval before production promotion, this approach provides both automated gatekeeping and human oversight, aligning with MLOps best practices for safe model deployment.

Answer

Use Cloud Scheduler to trigger retraining and only deploy if the new model outperforms the previous one on a holdout set.

Answer

Use Vertex AI Model Registry's automatic promotion feature that moves models to production based on evaluation results.

Answer

Configure Cloud Functions to re-evaluate the model daily and promote if it passes.

Question 2

A financial services company uses a Dataflow streaming pipeline to process real-time stock trades. The pipeline reads from Pub/Sub, enriches with reference data from Cloud Bigtable, and writes to BigQuery. Recently, they noticed an increase in processing latency during market open hours. Investigation shows that the pipeline is data-skewed: a few stock symbols generate 90% of the traffic. The team wants to reduce latency without changing the pipeline structure. What should they do?

Accepted Answer

Enable Dataflow Streaming Engine to dynamically repartition work. Dataflow Streaming Engine can dynamically repartition work, which directly addresses data skew by redistributing the processing load of hot keys (e.g., high-volume stock symbols) across available workers. This reduces latency without altering the pipeline structure, as it uses a shuffle service that separates the compute from the storage, allowing for more efficient handling of skewed data.

Answer

Increase the Pub/Sub subscription flow control to buffer less data

Answer

Use event-time windows based on trade timestamp to spread data

Answer

Increase the number of workers and use more CPU

Question 3

A company uses a custom container image for model serving. The image is large (10 GB). During deployment, they get timeouts. What should they do?

Accepted Answer

Use a smaller base image. Using a smaller base image directly addresses the root cause of the timeout: the 10 GB image takes too long to download from the container registry during pod startup. By reducing the image size (e.g., using a slim or distroless base image), the pull time decreases, avoiding the default kubelet image pull timeout (typically 5 minutes) without requiring infrastructure changes.

Answer

Pre-pull the image on all nodes

Answer

Increase the timeout in the deployment config

Answer

Switch to a larger machine type

Question 4

A company has deployed a classification model on Vertex AI. They want to detect data drift in real-time for the model's input features. Which service should they use?

Accepted Answer

Vertex AI Model Monitoring. Vertex AI Model Monitoring is the correct service because it is specifically designed to detect data drift and feature skew for models deployed on Vertex AI. It continuously monitors input features against a baseline distribution and alerts when drift exceeds a configured threshold, enabling real-time detection without requiring custom code.

Answer

Cloud Monitoring

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Cloud Data Loss Prevention

Answer

Cloud Logging

Question 5

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 6

A startup is building a mobile app that needs to sync user data across devices in real time. They expect millions of concurrent users and need a NoSQL database with offline support and automatic multi-region replication. Which Google Cloud service meets these requirements?

Accepted Answer

Firestore. Firestore is a NoSQL, serverless document database that provides real-time synchronization, offline support via local persistence, and automatic multi-region replication. It is designed for mobile and web apps with millions of concurrent users, making it the ideal choice for this use case.

Answer

Cloud Bigtable

Answer

Cloud Spanner

Answer

Cloud SQL

Question 7

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 8

A data analyst needs to transform nested and repeated fields in BigQuery. They have a table with a column of type ARRAY>. Which SQL function should they use to flatten the array into individual rows for analysis?

Accepted Answer

UNNEST. UNNEST is used to flatten arrays into rows. STRUCT is used to group fields. CAST is for type conversion. REPLACE is for string replacement.

Answer

STRUCT

Answer

CAST

Answer

REPLACE

Question 9

A data engineer needs to create a BigQuery table that is partitioned by ingestion time and clustered by customer_id and transaction_date. They also want to limit access so that only users from a specific domain can query the table. Which approach should they use?

Accepted Answer

Create the table with partitioning and clustering, then create an authorized view on the table and grant the view access to the domain users. Authorized views allow sharing query results with specific users/groups without giving direct table access. Clustering and partitioning are defined at table creation. IAM roles at dataset level are too broad. Row-level security filters rows but doesn't restrict domain.

Answer

Create the table with partitioning only, then use a materialized view to restrict access

Answer

Create the table without clustering, use row-level security to filter by domain, and grant access to the table

Answer

Create the table with partitioning and clustering, then grant bigquery.dataViewer to the domain via IAM at the dataset level

Question 10

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