PDE Practice Test 2 — 25 Questions

Question 1

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 2

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 3

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 4

You need to create a Looker model that defines a 'sales' view based on a BigQuery table, with a measure for total revenue. Which LookML object defines the table and dimensions?

Accepted Answer

view. In LookML, a view defines the underlying database table and contains its dimensions (columns) and measures (aggregations like total revenue). The view is the fundamental building block that maps a physical table to a logical set of fields, and it is referenced by explores to make those fields queryable.

Answer

explore

Answer

model

Answer

dimension

Question 5

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

Answer

Use a wildcard table

Answer

Use materialized views

Question 6

A company wants to trigger a Cloud Run service whenever a new file is uploaded to a specific Cloud Storage bucket. Which event-driven solution should they use?

Accepted Answer

Eventarc with Cloud Storage trigger and Cloud Run destination. Eventarc is the recommended service for routing events from Cloud Storage to Cloud Run because it provides a fully managed, event-driven architecture with built-in filtering and retry logic. When a new file is uploaded, Cloud Storage emits a notification that Eventarc captures and delivers directly to the Cloud Run service as an HTTP request, enabling serverless processing without polling or additional infrastructure.

Answer

Cloud Scheduler to periodically poll the bucket

Answer

Cloud Functions triggered by Cloud Storage

Answer

Pub/Sub with a push subscription to Cloud Run

Question 7

A data engineer needs to transfer 5 PB of historical data from an on-premises Hadoop cluster to Cloud Storage. The network bandwidth is limited to 1 Gbps, and the transfer must complete within 30 days. Which transfer method should they use?

Accepted Answer

Transfer Appliance. The Transfer Appliance is a physical device designed for large-scale data transfers when network bandwidth is insufficient. With 5 PB of data and a 1 Gbps link, the theoretical maximum transfer time is over 500 days (5 PB × 8 bits/byte / 1 Gbps / 86400 seconds/day), far exceeding the 30-day window. The Transfer Appliance bypasses network constraints by shipping data physically to Google Cloud. Option A (gsutil rsync over the internet) is incorrect because it relies on the same 1 Gbps bandwidth, making it impossible to transfer 5 PB within 30 days. Option B (BigQuery Data Transfer Service) is designed for transferring data into BigQuery from cloud sources, not for large on-premises data transfers. Option C (Storage Transfer Service for on-premises) is intended for smaller or incremental transfers over a network, not for a 5 PB initial load within 30 days.

Answer

gsutil rsync over the internet

Answer

BigQuery Data Transfer Service

Answer

Storage Transfer Service for on-premises

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.

Answer

Store intermediate data in HDFS.

Answer

Use the largest machine types for master nodes.

Question 9

A company uses Google Ads and wants to automatically load their advertising data into BigQuery daily. They also need to transform the data with SQL and schedule a recurring query. Which combination of services meets these requirements with minimal operational overhead?

Accepted Answer

BigQuery Data Transfer Service for Google Ads and scheduled queries. BigQuery Data Transfer Service has a built-in Google Ads connector that automatically loads advertising data into BigQuery on a schedule, and BigQuery scheduled queries let you transform that data with SQL on a recurring basis — both fully managed with no infrastructure. This combination meets the daily load and SQL transformation requirements with minimal operational overhead.

Answer

Cloud Functions triggered by Cloud Scheduler to call Google Ads API and load into BigQuery

Answer

Cloud Composer to extract Google Ads API and Dataflow to transform

Answer

Storage Transfer Service to move CSV files to GCS, then load into BigQuery

Question 10

An organization wants to automate their batch data processing pipeline using Cloud Composer. The pipeline consists of multiple tasks: extract from Cloud Storage, transform with Dataflow, and load into BigQuery. Which Airflow operator should be used to run Dataflow jobs?

Accepted Answer

DataflowCreatePythonJobOperator. B is correct because the DataflowCreatePythonJobOperator is specifically designed to submit and manage Apache Beam pipelines written in Python as Dataflow jobs in Google Cloud. This operator handles the creation of a Dataflow job from a Python file, which aligns with the requirement to run Dataflow transformations within a Cloud Composer DAG.

Answer

BigQueryInsertJobOperator

Answer

GCSToBigQueryOperator

Answer

DataprocSubmitJobOperator

Question 11

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

Answer

Set the workflow execution timeout to 600 seconds

Question 12

A data team uses Looker Studio to create a report that combines data from two different BigQuery tables: one with sales transactions and another with customer demographics. They need to join these tables in the report without writing SQL. Which feature should they use?

Accepted Answer

Data blending. Looker Studio's data blending feature allows combining data from multiple sources (including BigQuery tables) using a common key, without writing SQL. It provides a graphical interface to define joins. Creating a custom query requires SQL. Looker Studio reports support multiple charts, but blending is the specific feature for joining data. Calculated fields transform data within a single source.

Answer

Creating a report with multiple charts

Answer

Custom query in BigQuery connector

Answer

Calculated fields

Question 13

A data engineer needs to alert when Pub/Sub subscription has messages older than 1 hour. Which Cloud Monitoring metric and filter should they use?

Accepted Answer

Metric: subscription/oldest_unacked_message_age; filter: subscription_id. The metric subscription/oldest_unacked_message_age directly reports the age of the oldest unacknowledged message in a subscription, which is exactly what's needed to alert when messages are older than 1 hour. The filter subscription_id scopes the metric to the specific subscription. This metric is designed for monitoring message backlog age and is the correct choice for this requirement.

Answer

Metric: topic/send_message_operation_count; filter: topic_id

Answer

Metric: subscription/ack_message_count; filter: subscription_id

Answer

Metric: subscription/num_undelivered_messages; filter: subscription_id

Question 14

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.

Answer

Automatic model retraining when drift is detected.

Answer

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

Question 15

Match each machine learning term to its description.

Accepted Answer

Supervised Learning: Model trained on labeled data to predict output. In machine learning, supervised learning uses labeled data (inputs mapped to known outputs), unsupervised learning finds patterns in unlabeled data, features are input variables, and labels are the target outputs. Common confusions include mixing supervised/unsupervised definitions or swapping features and labels.

Answer

Supervised Learning: Model finds patterns without labeled data

Answer

Feature: The output variable the model predicts

Question 16

A company wants to implement a data lake on Google Cloud to store raw sensor data (unstructured binary files) and allow data scientists to run SQL queries on processed data. They expect to store terabytes of data and have different access patterns. Which combination of GCP services best meets these requirements?

Accepted Answer

Cloud Storage for raw data and BigQuery for processed data. Cloud Storage is the ideal service for storing raw, unstructured binary sensor data at petabyte scale, offering low-cost, durable object storage with multiple access tiers. BigQuery is a serverless, highly scalable data warehouse that allows data scientists to run SQL queries on processed data, with features like columnar storage and automatic optimization for analytical workloads. This combination directly addresses the need for raw storage and SQL-based analytics on processed data.

Answer

Bigtable for raw data and Cloud Spanner for processed data

Answer

Cloud Storage for both raw and processed data

Answer

Cloud SQL for raw data and Cloud Dataproc for processing

Question 17

You are monitoring a Dataproc cluster and notice that the cluster utilisation is high, but jobs are running slowly. The cluster uses preemptible workers for cost savings. What is the most likely cause of the performance degradation?

Accepted Answer

The preemptible workers are being preempted frequently, causing task retries and slowdowns.. Preemptible (spot) workers in Dataproc can be reclaimed by Compute Engine at any time, and when they are preempted mid-task, the task must be retried on remaining workers. Frequent preemption causes repeated task retries, stragglers, and overall job slowdown even though the cluster appears highly utilized. This is the classic symptom of relying heavily on preemptible workers.

Answer

The primary workers are using standard disks instead of SSDs.

Answer

The cluster is under-provisioned; increase the number of preemptible workers.

Answer

The cluster is using an older image version; upgrade to the latest.

Question 18

Drag and drop the steps to create a Cloud Bigtable instance and table using the CLI into the correct order.

Accepted Answer

Create Cloud Bigtable instance → Create table inside the instance → Add column families to the table. Bigtable instances contain clusters; tables are created within instances and must have column families.

Answer

Create table → Create Cloud Bigtable instance → Add column families to the table

Answer

Create Cloud Bigtable instance → Add column families → Create table inside the instance

Answer

Add column families → Create Cloud Bigtable instance → Create table inside the instance

Question 19

A company uses Looker Studio to create dashboards from BigQuery data. They notice that dashboard queries take several seconds to load. They want to improve performance without changing the underlying data or creating materialized views. Which option should they use?

Accepted Answer

Enable BigQuery BI Engine for the project. BigQuery BI Engine is an in-memory analysis service that accelerates queries from Looker Studio (and other BI tools) by caching data in memory, significantly reducing latency. Replicating data to Cloud SQL would add complexity and may not handle the volume. Using Looker instead of Looker Studio doesn't inherently speed up queries. Increasing BigQuery slots would help but is more expensive and not as targeted for BI tools.

Answer

Increase the number of BigQuery slots

Answer

Switch to Looker instead of Looker Studio

Answer

Replicate the data to Cloud SQL for faster queries

Question 20

A Dataflow pipeline reads log files from Cloud Storage, parses them into LogEvent objects, and writes to BigQuery. The pipeline fails with the above errors. What is the most likely cause?

Accepted Answer

The LogEvent class does not have a no-argument constructor.. Apache Beam's SDK requires that custom types used as PCollection elements (like LogEvent) have a no-argument constructor so that the framework can deserialize objects during distributed processing, especially when using the Dataflow runner. Without it, the pipeline fails at runtime with a serialization error because Beam's default coder (e.g., SerializableCoder) cannot reconstruct the object.

Answer

The pipeline is missing required import statements for LogEvent.

Answer

The BigQuery table schema does not match the LogEvent fields.

Answer

The log files are not in the expected format, causing parsing failures.