PDE Practice Test 3 — 30 Questions

Question 1

A data engineer deploys a TensorFlow model on Vertex AI using a custom container. After deployment, online prediction requests sometimes fail with a 500 error and the message 'Out of memory'. The model requires significant memory during inference. Which action should the engineer take to resolve this issue?

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

Accepted Answer

Increase the memory limit in the Vertex AI endpoint configuration.. Vertex AI endpoints allow you to configure a machine type with a specific memory limit. When a custom container runs out of memory during inference, increasing the memory allocation (e.g., by selecting a machine type with more RAM, such as n1-highmem-8) directly addresses the 'Out of memory' error. This ensures the container has sufficient resources to handle the model's inference workload without crashing.

Answer

Reduce the batch size of prediction requests sent to the endpoint.

Answer

Optimize the model by quantizing weights to reduce model size.

Answer

Use a machine type with higher CPU performance.

Question 2

You are optimizing a BigQuery query that runs on a large table (hundreds of TB). The table is partitioned by date and frequently queried with filters on a specific customer_id column and date range. Queries are slow even after partitioning. Which optimization should you apply?

Accepted Answer

Columnar clustering on customer_id. Clustering on customer_id within the partition improves query performance because BigQuery can prune blocks based on clustered columns. Partitioning alone doesn't help with non-date filters. Materialized views may help pre-aggregated queries but not ad-hoc customer_id filters. Denormalization is not an optimization. Increasing slots is expensive and doesn't address data structure.

Answer

Increase the number of BigQuery slots

Answer

Create materialized views for each customer

Answer

Denormalize the table to reduce joins

Question 3

An organization needs to transfer 50 TB of historical data from an on-premises Hadoop cluster to Google Cloud Storage. The network bandwidth is limited to 100 Mbps. Which transfer method is MOST cost-effective and time-efficient?

Accepted Answer

Transfer Appliance. Transfer Appliance is designed for petabyte-scale offline transfers, shipping physical devices to Google for upload, which is much faster than using limited network bandwidth for 50 TB.

Answer

Storage Transfer Service over the network

Answer

BigQuery Data Transfer Service for Hadoop

Answer

gsutil cp with parallel composite uploads

Question 4

A data scientist wants to train a custom TensorFlow model on Vertex AI using a managed Jupyter notebook. Which Vertex AI service should they use to set up a notebook environment with pre-installed deep learning frameworks?

Accepted Answer

Vertex AI Workbench. Vertex AI Workbench provides managed Jupyter notebooks with pre-installed deep learning frameworks (TensorFlow, PyTorch, etc.) and easy scaling options. Notebooks on Compute Engine would require manual setup. AI Platform Training is for training jobs, not interactive notebooks. Vertex AI Pipelines is for orchestrating ML workflows.

Answer

Compute Engine with Deep Learning VM

Answer

Vertex AI Training via custom job

Answer

Vertex AI Pipelines

Question 5

A retail company is using a machine learning model for inventory forecasting. They observe that the model's predictions become less accurate over time, especially during holiday seasons. Which monitoring metric should they prioritize?

Accepted Answer

Prediction drift (feature drift). Prediction drift (feature drift) is the correct metric because it directly measures changes in the input data distribution over time, which is the root cause of degrading model accuracy during holiday seasons. When customer behavior shifts (e.g., buying patterns during holidays), the features the model relies on drift, causing predictions to become less accurate. Monitoring prediction drift allows the team to detect when retraining or updating the model is necessary.

Answer

Model latency

Answer

Prediction counts

Answer

Resource utilization

Question 6

A company wants to automate model retraining and deployment whenever new training data becomes available. Which service should be used to orchestrate the end-to-end workflow?

Accepted Answer

Vertex AI Pipelines. Vertex AI Pipelines is the correct choice because it is a managed service specifically designed to orchestrate and automate end-to-end ML workflows, including model retraining and deployment triggered by new data. It allows you to define pipelines as a directed acyclic graph (DAG) of steps using the Kubeflow Pipelines SDK or pre-built components, and it integrates natively with other Vertex AI services for training, evaluation, and deployment.

Answer

Cloud Build

Answer

Cloud Scheduler

Answer

Cloud Composer

Question 7

A data scientist has trained an XGBoost model on Vertex AI and wants to deploy it to an endpoint with automatic scaling based on traffic. What is the recommended deployment approach?

Accepted Answer

Use Vertex AI Endpoints with automatic scaling enabled. Vertex AI Endpoints with automatic scaling enabled is the recommended approach because it directly supports deploying trained models (including XGBoost) as online prediction endpoints with built-in autoscaling based on incoming traffic. This service manages the underlying infrastructure, load balancing, and scaling policies, aligning with the requirement for automatic scaling without additional containerization or serverless overhead.

Answer

Export the model to a container and deploy on Cloud Run

Answer

Use AI Platform Prediction with batch prediction

Answer

Deploy the model as an API on App Engine

Question 8

A data science team is operationalizing a batch prediction job using Vertex AI Batch Prediction. The model uses a custom container that requires a specific GPU for inference. The job processes a large dataset stored in Cloud Storage. The team wants to minimize cost while ensuring the job completes within a 2-hour window. Which configuration should they choose?

Accepted Answer

Use a custom training job with a GPU worker pool and run the inference as a custom job.. Vertex AI Batch Prediction does not support custom containers with GPU accelerators; it only supports CPUs for batch prediction jobs. To run GPU-accelerated inference on a large dataset, the team must use a custom training job (which supports GPU worker pools) and run inference as a custom job. This approach allows them to leverage GPU hardware for the 2-hour window while minimizing cost by using preemptible VMs or choosing the smallest GPU instance that meets throughput requirements.

Answer

Use a custom machine type with a GPU accelerator in the batch prediction request.

Answer

Use a high-memory machine type (e.g., n1-highmem-32) without GPU to reduce cost.

Answer

Configure a Vertex AI endpoint with GPU and submit batch requests to the endpoint.

Question 9

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 10

A company uses Cloud Dataflow to process financial transactions from Pub/Sub to BigQuery. The pipeline must ensure exactly-once semantics. Recently, they noticed duplicate rows in BigQuery. The source publishes with at-least-once. The Dataflow pipeline uses idempotent writes. What is the most likely cause? Options: A. The pipeline uses GlobalWindows. B. The pipeline has autoscaling enabled. C. The pipeline uses file loads as a sink. D. The pipeline's watermark is misconfigured.

Accepted Answer

The pipeline's watermark is misconfigured. The most likely cause is a misconfigured watermark. In Dataflow, the watermark tracks event time progress and determines when to trigger window results. If the watermark is misconfigured (e.g., too aggressive or based on incorrect timestamps), late-arriving data may be processed in multiple windows, leading to duplicate rows even with idempotent writes. Since the source uses at-least-once delivery, late data can be re-published, and a faulty watermark can cause it to be written again.

Answer

The pipeline uses file loads as a sink

Answer

The pipeline uses GlobalWindows

Answer

The pipeline has autoscaling enabled

Question 11

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?

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 12

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 13

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 14

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

Answer

Cloud Data Loss Prevention

Answer

Cloud Logging

Question 15

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 16

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 17

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 18

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 19

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 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