PDE Practice Questions 2 — 50 Questions

Question 1

You are a data engineer at a financial services company that uses Vertex AI to train and deploy models for credit risk assessment. The company has strict governance requirements: every model version must be approved by the risk committee before going to production. The approval process can take several days. Currently, the team trains a new model weekly and manually deploys it to a staging endpoint for review, then manually promotes to production after approval. This process is error-prone and slow. You want to automate the pipeline: training should trigger automatically when new data arrives, the model should be automatically deployed to a staging endpoint for review, and after manual approval, it should be promoted to production. Additionally, you need to ensure that if a model in staging performs poorly (e.g., low accuracy), it should not be promoted even if approved. What should you do?

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

Accepted Answer

Create a Vertex AI Pipeline that: (1) Triggers on new data, (2) Trains model, (3) Evaluates and stores metrics in the model registry, (4) Deploys to staging endpoint as a new model version. Then use a manual approval step (e.g., via Cloud Build approval or external system) to trigger a second pipeline that checks the stored metrics and, if acceptable, deploys to production endpoint.. The best approach uses Vertex AI Pipelines to automatically train and deploy to a staging endpoint. After manual approval, a separate pipeline step checks model performance metrics (which were stored during training/evaluation) and if they meet a threshold, promotes to production. This enforces governance and automation.

Answer

Use Vertex AI Experiments to track model versions, then manually deploy from the Experiments UI.

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Use Cloud Scheduler to run training weekly, then use Cloud Functions to deploy to staging, and after manual approval, use another Cloud Function to check performance and deploy to production.

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Train models on Vertex AI Workbench and use a CI/CD tool like Cloud Build to deploy to staging. Use a Cloud Build approval step to promote to production after manual check.

Question 2

A company wants to analyze server logs stored in Cloud Storage using SQL. They need to get results in seconds without setting up any clusters. Which service should they use?

Accepted Answer

BigQuery. BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse designed for business agility. It allows you to analyze petabytes of data using standard SQL without needing to provision or manage any clusters, making it ideal for querying server logs stored in Cloud Storage directly via external tables or loading data into BigQuery for sub-second query performance.

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Cloud Dataflow

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Cloud Logging

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Cloud Dataproc

Question 3

A data science team wants to deploy a model that requires a custom container with specific NVIDIA CUDA version. They build the image and push to Artifact Registry. When deploying to Vertex AI, the model fails to load with an error: 'Failed to start container: invalid ELF header'. What is the most likely cause?

Accepted Answer

The container image was built for a different CPU architecture (e.g., ARM64) than the Vertex AI machine (x86_64). The image was built for the wrong architecture (e.g., building on an ARM Mac for a x86 deployment). Option B (CUDA version mismatch) would cause a different error. Option C (container permissions) would cause a permission denied error. Option D (model file format) would cause loading errors but not container startup failure.

Answer

The model file (saved as .pkl) is corrupted

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The CUDA version in the container is incompatible with the GPU on the machine

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The container does not have the necessary permissions to access the model file in Cloud Storage

Question 4

A data engineer needs to move 500 TB of archival data from an on-premises Hadoop cluster to Cloud Storage. The network bandwidth is limited to 100 Mbps, and the transfer must complete within 30 days. Which method is most cost-effective and reliable?

Accepted Answer

Use Transfer Appliance to ship the data offline. With 100 Mbps, transferring 500 TB over the network would take > 500 days, exceeding the 30-day window. Transfer Appliance is designed for petabyte-scale offline transfers, making it the only feasible option.

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Use Dataproc to copy data in parallel

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Use a VPN and gsutil rsync

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Use Storage Transfer Service over the internet

Question 5

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 6

A company uses BigQuery flat-rate pricing with 500 slots purchased as a committed use discount. During peak hours, they need additional capacity but do not want to buy more committed slots. They have a secondary project used for ad-hoc queries by analysts. How can they provide burst capacity to the primary project during peak times without increasing committed spend?

Accepted Answer

Create flex slots in the secondary project, create a reservation in the secondary project, and assign the reservation to the primary project.. Flex slots provide temporary capacity without long-term commitment. By creating flex slots in a secondary project and assigning the reservation to the primary project, burst capacity is added during peak times without increasing committed spend. Option B is incorrect because autoscaling can be used with committed use reservations, but it does not provide capacity beyond the purchased slots without incurring additional costs; autoscaling on committed use still uses flex pricing. Option C is incorrect because upgrading to Enterprise Plus does not inherently provide burst capacity without additional slots. Option D is incorrect because purchasing additional committed use slots increases committed spend.

Answer

Enable autoscaling slot management in the primary project's reservation, allowing slots to scale up based on demand.

Answer

Upgrade the primary project's edition to Enterprise Plus to allow bursting.

Answer

Purchase additional committed use slots in the primary project and apply them to the reservation.

Question 7

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 8

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)

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Use high-memory machine types

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Use Streaming Engine

Question 9

Match each data encryption concept to its description.

Accepted Answer

Customer-Managed Encryption Keys (CMEK): You manage key material in Cloud KMS and use it to protect data.. The correct matches are: CMEK involves managing keys in Cloud KMS; CSEK involves supplying your own keys; default encryption uses Google-managed keys; encryption in transit uses TLS. Common confusions include swapping CMEK and CSEK.

Answer

Customer-Managed Encryption Keys (CMEK): You supply your own encryption keys.

Answer

Default Encryption at Rest: You manage keys in Cloud KMS.

Question 10

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 11

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?

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.

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

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Optimize the model by quantizing weights to reduce model size.

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Use a machine type with higher CPU performance.

Question 12

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

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Denormalize the table to reduce joins

Question 13

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 14

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

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Vertex AI Training via custom job

Answer

Vertex AI Pipelines

Question 15

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

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Prediction counts

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Resource utilization

Question 16

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

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Cloud Composer

Question 17

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 18

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 19

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 20

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

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The pipeline uses GlobalWindows

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The pipeline has autoscaling enabled