PDE Practice Test 7 — 10 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.

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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 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

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Create materialized views for each customer

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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.

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Storage Transfer Service over the network

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BigQuery Data Transfer Service for Hadoop

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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.

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Compute Engine with Deep Learning VM

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Vertex AI Training via custom job

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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.

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Model latency

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Prediction counts

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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

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Cloud Scheduler

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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

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Use AI Platform Prediction with batch prediction

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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.

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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.

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Cloud Dataproc

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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.

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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