PDE Multiple Select — 10 Questions

Question 1

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

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Containerize the model using Docker

Answer

Create a Vertex AI Endpoint manually

Question 2

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 3

A company uses Cloud Dataproc for ephemeral clusters to run batch jobs. They want to ensure job reliability and data quality. Which two configuration options should they use? (Choose two.)

Accepted Answer

Use initialization actions for cluster setup.. Option B is correct because initialization actions let you run scripts on every node at cluster creation, so you can install monitoring/validation agents, configure logging, and enforce consistent setup that supports job reliability and data quality across ephemeral clusters. Option E is correct because graceful decommissioning (set via yarn:yarn.resourcemanager.decommissioning.timeout or Dataproc's gracefulDecommissionTimeout) lets running tasks finish before workers are removed, preventing partial job failures and data corruption when nodes are scaled down or preempted. Option A is not appropriate here because preemptible VMs reduce cost but can be reclaimed at any time, which undermines reliability rather than improving it. Option C is not appropriate because idle timeout only deletes idle clusters to save cost; it does not improve job reliability or data quality. Option D is not appropriate because custom machine types tune CPU/memory performance but do not by themselves ensure reliable job completion or data quality.

Answer

Enable preemptible VMs for cost savings.

Answer

Enable idle timeout to automatically delete clusters.

Answer

Use custom machine types for better performance.

Question 4

You are designing a data processing architecture on Google Cloud. You need to ingest data from multiple sources, including streaming events and batch files, and process them to produce a unified dataset for analytics. The solution must support both real-time and historical processing with the same codebase, and be able to handle late-arriving data. Which two Google Cloud services should you use together to achieve this? (Choose two.)

Accepted Answer

Cloud Pub/Sub. To achieve unified batch and stream processing with the same codebase and handle late data, you need a processing engine that supports Apache Beam, such as Cloud Dataflow, and a scalable ingestion service for streaming data, such as Cloud Pub/Sub. Dataflow's Beam model allows you to write one pipeline that works for both batch and streaming, with built-in support for event-time windowing and late data. Pub/Sub provides reliable, scalable ingestion for streaming events.

Answer

Cloud Composer

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Cloud Dataproc

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BigQuery

Question 5

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 6

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

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Cloud Dataflow

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Cloud Functions

Question 7

An organization is using BigQuery for analytics. They have a table that is 500 GB and is frequently queried by 'date' and 'region'. They want to optimize query performance and reduce costs. Which TWO actions should they take?

Accepted Answer

Cluster the table by region. Option E is correct because partitioning the table by date means BigQuery only scans the partitions that match the query's date filter, drastically reducing bytes processed and cost for a 500 GB table frequently filtered by date. Option D is correct because clustering the table by region physically sorts data within each partition by region, so queries filtering on region benefit from block pruning and scan less data. Together, partitioning by date and clustering by region match the two most common query predicates and are the standard BigQuery optimization pattern. Option A (authorized view) only controls access to data and does not improve query performance or reduce scan costs. Option B (wildcard table) is used to query multiple similarly named tables with a UNION-like syntax and does not optimize a single table's scans. Option C (materialized views) can help some workloads but is not the primary optimization for a frequently filtered base table and adds storage and refresh overhead, so it is not one of the two best actions here.

Answer

Use an authorized view

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Use a wildcard table

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Use materialized views

Question 8

A data engineer is designing a batch processing system using Cloud Dataproc. Which TWO practices improve performance and reduce costs? (Choose TWO.)

Accepted Answer

Set autoscaling policies based on YARN memory.. Autoscaling policies based on YARN memory allow the cluster to dynamically add or remove worker nodes in response to actual resource demand from running jobs. This prevents over-provisioning (reducing costs) and ensures sufficient resources for job completion (improving performance), as Cloud Dataproc directly monitors YARN memory metrics to trigger scaling actions.

Answer

Always use persistent disks for all nodes.

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Store intermediate data in HDFS.

Answer

Use the largest machine types for master nodes.

Question 9

A company uses Workflows to orchestrate a multi-step data pipeline. One step calls an HTTP endpoint that may take up to 10 minutes, but the default Workflows timeout is too short. They also need to handle transient errors with retries. Which TWO configurations should they apply? (Choose 2)

Accepted Answer

Set a step timeout of 600 seconds for the HTTP call step. Option A is correct because Workflows steps support a per-step timeout, and setting it to 600 seconds (10 minutes) ensures the HTTP call step is allowed to run for the full duration instead of being cut off by the shorter default step timeout. Option E is correct because transient errors (such as 5xx responses or connection resets) should be handled by attaching a retry policy to the step with conditions that match those transient failures, so the step is retried automatically. Option B is not appropriate because Workflows does not use dead letter queues for failed steps; failures are surfaced through execution errors and logs. Option C is wrong because relying on the default retry policy does not let them target transient errors with appropriate conditions, and the default may not retry at all. Option D is wrong because setting the workflow execution timeout to 600 seconds would cap the entire workflow at 10 minutes, which is not the same as giving the HTTP step enough time and could still cause premature termination.

Answer

Configure a dead letter queue for failed steps

Answer

Use the default retry policy on the step

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Set the workflow execution timeout to 600 seconds

Question 10

Which TWO are benefits of using Vertex AI Endpoints for model serving?

Accepted Answer

Integrated monitoring for prediction latency and error rates.. Option B is correct because Vertex AI Endpoints provide integrated monitoring that surfaces prediction latency and error rates, letting you track online serving health and performance directly in the console. Option C is correct because Vertex AI Endpoints support automatic scaling (autoscaling) based on traffic, adjusting the number of deployed model replicas to match request load. Options A, D, and E are not benefits of Endpoints: batch prediction is a separate Vertex AI feature (Batch Prediction jobs) rather than an Endpoint capability, automatic retraining on drift is not a built-in Endpoint function, and A/B testing requires explicit traffic-split configuration rather than working with no additional setup.

Answer

Batch prediction support out of the box.

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Automatic model retraining when drift is detected.

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Built-in support for A/B testing without any additional configuration.