Get Google Professional-Data-Engineer Dumps Questions Study Exam Guide Aug 17, 2026 [Q153-Q172]

4.5/5 - (2 votes)

Get Google Professional-Data-Engineer Dumps Questions Study Exam Guide Aug 17, 2026

Professional-Data-Engineer Premium Exam Engine – Download Free PDF Questions

Google Professional-Data-Engineer Exam Syllabus Topics:

Section	Weight	Objectives
Storing the data (~20% of the exam)	20%	– Planning for using a data warehouse 1. Mapping business requirements 2. Designing the data model 3. Deciding the degree of data normalization 4. Defining architecture to support data access patterns – Designing for a data platform 1. Building a data platform using Dataplex, Dataplex Catalog, BigQuery, Cloud Storage 2. Building a federated governance model for distributed data systems – Using a data lake 1. Processing data 2. Monitoring the data lake 3. Managing the lake (data discovery, access, cost controls) – Selecting storage systems 1. Lifecycle management of data 2. Analyzing data access patterns 3. Planning for storage costs and performance
Preparing and using data for analysis (~15% of the exam)	15%	– Sharing data securely 1. Data sharing and collaboration 2. Publishing datasets – Preparing data for visualization 1. Preparing data for reporting and dashboards 2. Connecting to Looker and other BI tools
Ingesting and processing the data (~20% of the exam)	20%	– Performing security considerations 1. Auditing, privacy, and compliance 2. Identity and Access Management (IAM) 3. Data encryption – Building and maintaining data structures and databases 1. Defining data lifecycle 2. Planning for analytical and operational use cases – Deploying and operationalizing the pipelines 1. CI/CD for data pipelines 2. Job automation and orchestration (Cloud Composer, Workflows)
Designing data processing systems (~30% of the exam)	30%	– Designing data processing resources 1. Compute options (Dataflow, Dataproc, Dataplex, Cloud Functions, Cloud Run) 2. Cost optimization 3. Cluster sizing and autoscaling – Selecting appropriate storage technologies 1. Choosing between BigQuery, Bigtable, Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore, AlloyDB 2. Mapping storage options to business requirements – Designing data pipelines 1. Streaming (e.g., windowing, late arriving data) 2. Processing logic 3. Batch processing 4. AI data enrichment 5. Integrating with new data sources 6. Data acquisition and import
Maintaining and automating data workloads (~15% of the exam)	15%	– Monitoring data pipelines and data processes 1. Logging, monitoring, and troubleshooting 2. Managing quotas and resource usage – Designing for reliability and fidelity 1. Recovering from failures 2. Planning for monitoring and alerting 3. Performing data quality and validation checks – Automating data processes 1. Workflow orchestration 2. Continuous integration and continuous deployment (CI/CD) 3. Scheduling jobs

QUESTION 153
You are developing a fraud detection model using BigQuery ML. You have a raw transaction dataset and need to create new features such as the average_transaction_amount_last_24_hours and time_since_last_transaction. These features require aggregation and time-window calculations on the existing data. The goal is to ensure that these features are consistently applied during both model training and prediction without manual intervention. You need to prepare these features efficiently for your model. What should you do?

Implement a Cloud Run function that triggers on new transactions, calculates the features, and inserts them into a feature store before model serving.

Export the BigQuery data to Cloud Storage, perform feature engineering using a custom Python script in a Dataflow job, and then re-import the engineered features into BigQuery.

Use the TRANSFORM clause within the CREATE MODEL statement, leveraging SQL functions for aggregations and time-based calculations.

Create a separate BigQuery table containing pre-computed features using complex SQL queries and join this table with the raw data during model training and serving.

QUESTION 154
An aerospace company uses a proprietary data format to store its night data. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming as few resources as possible. What should you do?

Use a standard Dataflow pipeline to store the raw data m BigQuery and then transform the format later when the data is used

Write a she script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source

Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format

Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format

QUESTION 155
You have some data, which is shown in the graphic below. The two dimensions are X and Y, and the shade of each dot represents what class it is. You want to classify this data accurately using a linear algorithm. To do this you need to add a synthetic feature. What should the value of that feature be?

X^2+Y^2

X^2

Y^2

cos(X)

QUESTION 156
You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

Create a table in BigQuery, and append the new samples for CPU and memory to the table

Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second

Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second

Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.

QUESTION 157
You want to store your team’s shared tables in a single dataset to make data easily accessible to various analysts. You want to make this data readable but unmodifiable by analysts. At the same time, you want to provide the analysts with individual workspaces in the same project, where they can create and store tables for their own use, without the tables being accessible by other analysts. What should you do?

Give analysts the BigQuery Data Viewer role at the project level Create one other dataset, and give the analysts the BigQuery Data Editor role on that dataset.

Give analysts the BigQuery Data Viewer role at the project level Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the project level.

Give analysts the BigQuery Data Viewer role on the shared dataset. Create a dataset for each analyst, and give each analyst the BigQuery Data Editor role at the dataset level for their assigned dataset

Give analysts the BigQuery Data Viewer role on the shared dataset Create one other dataset and give the analysts the BigQuery Data Editor role on that dataset.

QUESTION 158
You are planning to load some of your existing on-premises data into BigQuery on Google Cloud. You want to either stream or batch-load data, depending on your use case. Additionally, you want to mask some sensitive data before loading into BigQuery. You need to do this in a programmatic way while keeping costs to a minimum. What should you do?

Use the BigQuery Data Transfer Service to schedule your migration. After the data is populated in BigQuery. use the connection to the Cloud Data Loss Prevention {Cloud DLP} API to de-identify the necessary data.

Create your pipeline with Dataflow through the Apache Beam SDK for Python, customizing separate options within your code for streaming.
batch processing, and Cloud DLP Select BigQuery as your data sink.

Use Cloud Data Fusion to design your pipeline, use the Cloud DLP plug-in to de-identify data within your pipeline, and then move the data into BigQuery.

Set up Datastream to replicate your on-premise data on BigQuery.

QUESTION 159
Case Study 2 – MJTelco
Company Overview
MJTelco is a startup that plans to build networks in rapidly growing, underserved markets around the world. The company has patents for innovative optical communications hardware. Based on these patents, they can create many reliable, high-speed backbone links with inexpensive hardware.
Company Background
Founded by experienced telecom executives, MJTelco uses technologies originally developed to overcome communications challenges in space. Fundamental to their operation, they need to create a distributed data infrastructure that drives real-time analysis and incorporates machine learning to continuously optimize their topologies. Because their hardware is inexpensive, they plan to overdeploy the network allowing them to account for the impact of dynamic regional politics on location availability and cost.
Their management and operations teams are situated all around the globe creating many-to- many relationship between data consumers and provides in their system. After careful consideration, they decided public cloud is the perfect environment to support their needs.
Solution Concept
MJTelco is running a successful proof-of-concept (PoC) project in its labs. They have two primary needs:
* Scale and harden their PoC to support significantly more data flows generated when they ramp to more than 50,000 installations.
* Refine their machine-learning cycles to verify and improve the dynamic models they use to control topology definition.
MJTelco will also use three separate operating environments – development/test, staging, and production – to meet the needs of running experiments, deploying new features, and serving production customers.
Business Requirements
* Scale up their production environment with minimal cost, instantiating resources when and where needed in an unpredictable, distributed telecom user community.
* Ensure security of their proprietary data to protect their leading-edge machine learning and analysis.
* Provide reliable and timely access to data for analysis from distributed research workers
* Maintain isolated environments that support rapid iteration of their machine-learning models without affecting their customers.
Technical Requirements
* Ensure secure and efficient transport and storage of telemetry data
* Rapidly scale instances to support between 10,000 and 100,000 data providers with multiple flows each.
* Allow analysis and presentation against data tables tracking up to 2 years of data storing approximately 100m records/day
* Support rapid iteration of monitoring infrastructure focused on awareness of data pipeline problems both in telemetry flows and in production learning cycles.
CEO Statement
Our business model relies on our patents, analytics and dynamic machine learning. Our inexpensive hardware is organized to be highly reliable, which gives us cost advantages. We need to quickly stabilize our large distributed data pipelines to meet our reliability and capacity commitments.
CTO Statement
Our public cloud services must operate as advertised. We need resources that scale and keep our data secure. We also need environments in which our data scientists can carefully study and quickly adapt our models. Because we rely on automation to process our data, we also need our development and test environments to work as we iterate.
CFO Statement
The project is too large for us to maintain the hardware and software required for the data and analysis. Also, we cannot afford to staff an operations team to monitor so many data feeds, so we will rely on automation and infrastructure. Google Cloud’s machine learning will allow our quantitative researchers to work on our high-value problems instead of problems with our data pipelines.
Given the record streams MJTelco is interested in ingesting per day, they are concerned about the cost of Google BigQuery increasing. MJTelco asks you to provide a design solution. They require a single large data table called tracking_table. Additionally, they want to minimize the cost of daily queries while performing fine-grained analysis of each day’s events. They also want to use streaming ingestion. What should you do?

Create a table called tracking_table and include a DATE column.

Create a partitioned table called tracking_table and include a TIMESTAMP column.

Create sharded tables for each day following the pattern tracking_table_YYYYMMDD.

Create a table called tracking_table with a TIMESTAMP column to represent the day.

QUESTION 160
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)

The wide model is used for memorization, while the deep model is used for generalization.

A good use for the wide and deep model is a recommender system.

The wide model is used for generalization, while the deep model is used for memorization.

A good use for the wide and deep model is a small-scale linear regression problem.

QUESTION 161
Your company is using WHILECARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got “-” at [4:11]
SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod
WHERE
age != 99
AND_TABLE_SUFFIX = `1929′
ORDER BY
age DESC
Which table name will make the SQL statement work correctly?

`bigquery-public-data.noaa_gsod.gsod`

bigquery-public-data.noaa_gsod.gsod*

`bigquery-public-data.noaa_gsod.gsod’*

`bigquery-public-data.noaa_gsod.gsod*`

QUESTION 162
You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your data. You want to continue through the model development lifecycle. What should you do next?

Use your model to run predictions on fresh customer input data.

Monitor your model performance, and make any adjustments needed.

Delineate what data will be used for testing and what will be used for training the model.

Test and evaluate your model on your curated data to determine how well the model performs.

QUESTION 163
Why do you need to split a machine learning dataset into training data and test data?

So you can try two different sets of features

To make sure your model is generalized for more than just the training data

To allow you to create unit tests in your code

So you can use one dataset for a wide model and one for a deep model

QUESTION 164
You are running a Dataflow streaming pipeline, with Streaming Engine and Horizontal Autoscaling enabled. You have set the maximum number of workers to 1000. The input of your pipeline is Pub/Sub messages with notifications from Cloud Storage. One of the pipeline transforms reads CSV files and emits an element for every CSV line. The job performance is low, the pipeline is using only 10 workers, and you notice that the autoscaler is not spinning up additional workers. What should you do to improve performance?

Enable Vertical Autoscaling to let the pipeline use larger workers.

Change the pipeline code, and introduce a Reshuffle step to prevent fusion.

Update the job to increase the maximum number of workers.

Use Dataflow Prime, and enable Right Fitting to increase the worker resources.

QUESTION 165
The data analyst team at your company uses BigQuery for ad-hoc queries and scheduled SQL pipelines in a Google Cloud project with a slot reservation of 2000 slots. However, with the recent introduction of hundreds of new non time-sensitive SQL pipelines, the team is encountering frequent quota errors. You examine the logs and notice that approximately 1500 queries are being triggered concurrently during peak time. You need to resolve the concurrency issue. What should you do?

Update SQL pipelines and ad-hoc queries to run as interactive query jobs.

Increase the slot capacity of the project with baseline as 0 and maximum reservation size as 3000.

Update SOL pipelines to run as a batch query, and run ad-hoc queries as interactive query jobs.

Increase the slot capacity of the project with baseline as 2000 and maximum reservation size as 3000.

QUESTION 166
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight dat
a. How can you adjust your application design?

Re-write the application to load accumulated data every 2 minutes.

Convert the streaming insert code to batch load for individual messages.

Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.

Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

QUESTION 167
The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster ____.

application node

conditional node

master node

worker node

QUESTION 168
Your team is responsible for developing and maintaining ETLs in your company. One of your Dataflow jobs is failing because of some errors in the input data, and you need to improve reliability of the pipeline (incl.being able to reprocess all failing data). What should you do?

Add a filtering step to skip these types of errors in the future, extract erroneous rows from logs.

Add a try… catch block to your DoFn that transforms the data, extract erroneous rows from logs.

Add a try… catch block to your DoFn that transforms the data, write erroneous rows to PubSub directly from the DoFn.

Add a try… catch block to your DoFn that transforms the data, use a sideOutput to create a PCollection that can be stored to PubSub later.

QUESTION 169
In order to securely transfer web traffic data from your computer’s web browser to the Cloud Dataproc cluster you should use a(n) _____.

VPN connection

Special browser

SSH tunnel

FTP connection

QUESTION 170
You want to build a managed Hadoop system as your data lake. The data transformation process is composed of a series of Hadoop jobs executed in sequence. To accomplish the design of separating storage from compute, you decided to use the Cloud Storage connector to store all input data, output data, and intermediary data. However, you noticed that one Hadoop job runs very slowly with Cloud Dataproc, when compared with the on-premises bare-metal Hadoop environment (8-core nodes with 100-GB RAM). Analysis shows that this particular Hadoop job is disk I/O intensive. You want to resolve the issue. What should you do?

Allocate sufficient memory to the Hadoop cluster, so that the intermediary data of that particular Hadoop job can be held in memory

Allocate sufficient persistent disk space to the Hadoop cluster, and store the intermediate data of that particular Hadoop job on native HDFS

Allocate more CPU cores of the virtual machine instances of the Hadoop cluster so that the networking bandwidth for each instance can scale up

Allocate additional network interface card (NIC), and configure link aggregation in the operating system to use the combined throughput when working with Cloud Storage

QUESTION 171
You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings.
Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight data. How can you adjust your application design?

Re-write the application to load accumulated data every 2 minutes.

Convert the streaming insert code to batch load for individual messages.

Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.

Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

QUESTION 172
You are collecting loT sensor data from millions of devices across the world and storing the data in BigQuery.
Your access pattern is based on recent data tittered by location_id and device_version with the following query:
You want to optimize your queries for cost and performance. How should you structure your data?

Partition table data by create_date, location_id and device_version

Partition table data by create_date cluster table data by tocation_id and device_version

Cluster table data by create_date location_id and device_version

Cluster table data by create_date, partition by location and device_version