Real Professional-Data-Engineer are Uploaded by TrainingQuiz provide 2026 Latest Professional-Data-Engineer Practice Tests Dumps [Q86-Q106]

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Real Professional-Data-Engineer are Uploaded by TrainingQuiz provide 2026 Latest Professional-Data-Engineer Practice Tests Dumps.

All Professional-Data-Engineer Dumps and Google Certified Professional Data Engineer Exam Training Courses Help candidates to study and pass the Google Certified Professional Data Engineer Exam Exams hassle-free!

Google Professional-Data-Engineer certification exam is designed for professionals seeking to demonstrate their expertise in designing, building, and managing data processing systems on the Google Cloud Platform. Professional-Data-Engineer exam is intended for data engineers, data architects, and data analysts who work with big data solutions. Google Certified Professional Data Engineer Exam certification validates the skills and knowledge required to design and build data processing systems, as well as manage and monitor them in a production environment.

Ensure Solution Quality

Ensure Portability & Flexibility: The considerations for this domain include the design for application and data portability, including data residency prerequisites and Multiple-Cloud. It also coves data staging, discovery, and cataloging, as well as mapping to future and current business prerequisites.
Ensure Fidelity & Reliability: The applicants should be able to carry out data preparation & quality control (such as Cloud Dataprep), verify and monitor, as well as plan, execute, and stress test data recovery (including rerunning failed jobs, fault tolerance, and retrospective re-analysis performance). Besides that, they should be able to choose between idempotent ACID and eventual consistent prerequisites;
Ensure Efficiency & Scalability: The potential candidates will be required to demonstrate their ability to build and run test suits as well as monitor pipeline, including Stackdriver. It also focuses on their skills related to assessing, improving, and troubleshooting data process infrastructure and data representations. This area will also require that the test takers demonstrate the capacity to resize and autoscale resources;
Design for Compliance & Security: The consideration for this topic includes identity & access management such as Cloud IAM. You should also know about data security (including key management and encryption) and privacy assurance (such as Data Loss Prevention API). This part also covers the skills needed in legal compliance, including Health Insurance Portability & Accountability Act, FedRAMP, Children’s Online Privacy Protection Act, and General Data Protection Regulation;

Introduction

Data engineers are responsible for finding trends in data sets and developing algorithms to help make raw data more useful to the enterprise. This IT role requires a significant set of technical skills, including a deep knowledge of SQL database design and multiple programming languages They collect, transform, and visualize data. The Data Engineer designs, builds, maintains, and troubleshoots data processing systems with a particular emphasis on the security, reliability, fault-tolerance,scalability, fidelity, and efficiency of such systems.

Q86. After migrating ETL jobs to run on BigQuery, you need to verify that the output of the migrated jobs is the same as the output of the original. You’ve loaded a table containing the output of the original job and want to compare the contents with output from the migrated job to show that they are identical. The tables do not contain a primary key column that would enable you to join them together for comparison.
What should you do?

Select random samples from the tables using the RAND() function and compare the samples.

Select random samples from the tables using the HASH() function and compare the samples.

Use a Dataproc cluster and the BigQuery Hadoop connector to read the data from each table and calculate a hash from non-timestamp columns of the table after sorting. Compare the hashes of each table.

Create stratified random samples using the OVER() function and compare equivalent samples from each table.

Q87. Your company’s on-premises Apache Hadoop servers are approaching end-of-life, and IT has decided to migrate the cluster to Google Cloud Dataproc. A like-for-like migration of the cluster would require 50 TB of Google Persistent Disk per node. The CIO is concerned about the cost of using that much block storage. You want to minimize the storage cost of the migration. What should you do?

Put the data into Google Cloud Storage.

Use preemptible virtual machines (VMs) for the Cloud Dataproc cluster.

Tune the Cloud Dataproc cluster so that there is just enough disk for all data.

Migrate some of the cold data into Google Cloud Storage, and keep only the hot data in Persistent Disk.

Q88. Which of the following are feature engineering techniques? (Select 2 answers)

Hidden feature layers

Feature prioritization

Crossed feature columns

Bucketization of a continuous feature

Q89. You are designing the architecture to process your data from Cloud Storage to BigQuery by using Dataflow.
The network team provided you with the Shared VPC network and subnetwork to be used by your pipelines.
You need to enable the deployment of the pipeline on the Shared VPC network. What should you do?

Assign the compute. networkUser role to the Dataflow service agent.

Assign the compute.networkUser role to the service account that executes the Dataflow pipeline.

Assign the dataflow, admin role to the Dataflow service agent.

Assign the dataflow, admin role to the service account that executes the Dataflow pipeline.

Q90. You work on a regression problem in a natural language processing domain, and you have 100M labeled exmaples in your dataset. You have randomly shuffled your data and split your dataset into train and test samples (in a 90/10 ratio). After you trained the neural network and evaluated your model on a test set, you discover that the root-mean-squared error (RMSE) of your model is twice as high on the train set as on the test set. How should you improve the performance of your model?

Increase the share of the test sample in the train-test split.

Try to collect more data and increase the size of your dataset.

Try out regularization techniques (e.g., dropout of batch normalization) to avoid overfitting.

Increase the complexity of your model by, e.g., introducing an additional layer or increase sizing the size of vocabularies or n-grams used.

Q91. Which of these statements about BigQuery caching is true?

By default, a query’s results are not cached.

BigQuery caches query results for 48 hours.

Query results are cached even if you specify a destination table.

There is no charge for a query that retrieves its results from cache.

Q92. Your neural network model is taking days to train. You want to increase the training speed. What can you do?

Subsample your test dataset.

Subsample your training dataset.

Increase the number of input features to your model.

Increase the number of layers in your neural network.

Q93. You are managing a Cloud Dataproc cluster. You need to make a job run faster while minimizing costs, without losing work in progress on your clusters. What should you do?

Increase the cluster size with more non-preemptible workers.

Increase the cluster size with preemptible worker nodes, and configure them to forcefully decommission.

Increase the cluster size with preemptible worker nodes, and use Cloud Stackdriver to trigger a script to preserve work.

Increase the cluster size with preemptible worker nodes, and configure them to use graceful decommissioning.

Q94. Your globally distributed auction application allows users to bid on items. Occasionally, users place identical bids at nearly identical times, and different application servers process those bids. Each bid event contains the item, amount, user, and timestamp. You want to collate those bid events into a single location in real time to determine which user bid first. What should you do?

Create a file on a shared file and have the application servers write all bid events to that file. Process the file with Apache Hadoop to identify which user bid first.

Have each application server write the bid events to Cloud Pub/Sub as they occur. Push the events from Cloud Pub/Sub to a custom endpoint that writes the bid event information into Cloud SQL.

Set up a MySQL database for each application server to write bid events into. Periodically query each of those distributed MySQL databases and update a master MySQL database with bid event information.

Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull

Q95. You are designing a data mesh on Google Cloud with multiple distinct data engineering teams building data products. The typical data curation design pattern consists of landing files in Cloud Storage, transforming raw data in Cloud Storage and BigQuery datasets. and storing the final curated data product in BigQuery datasets You need to configure Dataplex to ensure that each team can access only the assets needed to build their data products. You also need to ensure that teams can easily share the curated data product. What should you do?

1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data.
2 Provide each data engineering team access to the virtual lake.

1 Create a single Dataplex virtual lake and create a single zone to contain landing, raw. and curated data. 2 Build separate assets for each data product within the zone.
3. Assign permissions to the data engineering teams at the zone level.

1 Create a Dataplex virtual lake for each data product, and create a single zone to contain landing, raw, and curated data.
2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.

1 Create a Dataplex virtual lake for each data product, and create multiple zones for landing, raw. and curated data.
2. Provide the data engineering teams with full access to the virtual lake assigned to their data product.

Q96. You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples. Which two characteristic support this method? (Choose two.)

There are very few occurrences of mutations relative to normal samples.

There are roughly equal occurrences of both normal and mutated samples in the database.

You expect future mutations to have different features from the mutated samples in the database.

You expect future mutations to have similar features to the mutated samples in the database.

You already have labels for which samples are mutated and which are normal in the database.

Q97. Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

BETWEEN

WHERE

SELECT

LIMIT

Q98. You want to rebuild your batch pipeline for structured data on Google Cloud You are using PySpark to conduct data transformations at scale, but your pipelines are taking over twelve hours to run To expedite development and pipeline run time, you want to use a serverless tool and SQL syntax You have already moved your raw data into Cloud Storage How should you build the pipeline on Google Cloud while meeting speed and processing requirements?

Convert your PySpark commands into SparkSQL queries to transform the data; and then run your pipeline on Dataproc to write the data into BigQuery

Ingest your data into Cloud SQL, convert your PySpark commands into SparkSQL queries to transform the data, and then use federated queries from BigQuery for machine learning.

Ingest your data into BigQuery from Cloud Storage, convert your PySpark commands into BigQuery SQL queries to transform the data, and then write the transformations to a new table

Use Apache Beam Python SDK to build the transformation pipelines, and write the data into BigQuery

Q99. You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients’ personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

Create an authorized view in BigQuery to restrict access to tables with sensitive data.

Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.

Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.

Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.

Q100. Which Java SDK class can you use to run your Dataflow programs locally?

LocalRunner

DirectPipelineRunner

MachineRunner

LocalPipelineRunner

Q101. Your company is performing data preprocessing for a learning algorithm in Google Cloud Dataflow.
Numerous data logs are being are being generated during this step, and the team wants to analyze them. Due to the dynamic nature of the campaign, the data is growing exponentially every hour.
The data scientists have written the following code to read the data for a new key features in the logs.
BigQueryIO.Read
.named(“ReadLogData”)
.from(“clouddataflow-readonly:samples.log_data”)
You want to improve the performance of this data read. What should you do?

Specify the TableReference object in the code.

Use .fromQuery operation to read specific fields from the table.

Use of both the Google BigQuery TableSchema and TableFieldSchema classes.

Call a transform that returns TableRow objects, where each element in the PCollection represents a single row in the table.

Q102. Your company is implementing a data warehouse using BigQuery, and you have been tasked with designing the data model You move your on-premises sales data warehouse with a star data schema to BigQuery but notice performance issues when querying the data of the past 30 days Based on Google’s recommended practices, what should you do to speed up the query without increasing storage costs?

Denormalize the data

Shard the data by customer ID

Materialize the dimensional data in views

Partition the data by transaction date

Q103. You are migrating a large number of files from a public HTTPS endpoint to Cloud Storage. The files are protected from unauthorized access using signed URLs. You created a TSV file that contains the list of object URLs and started a transfer job by using Storage Transfer Service. You notice that the job has run for a long time and eventually failed Checking the logs of the transfer job reveals that the job was running fine until one point, and then it failed due to HTTP 403 errors on the remaining files You verified that there were no changes to the source system You need to fix the problem to resume the migration process. What should you do?

Set up Cloud Storage FUSE, and mount the Cloud Storage bucket on a Compute Engine Instance Remove the completed files from the TSV file Use a shell script to iterate through the TSV file and download the remaining URLs to the FUSE mount point.

Update the file checksums in the TSV file from using MD5 to SHA256. Remove the completed files from the TSV file and rerun the Storage Transfer Service job.

Renew the TLS certificate of the HTTPS endpoint Remove the completed files from the TSV file and rerun the Storage Transfer Service job.

Create a new TSV file for the remaining files by generating signed URLs with a longer validity period. Split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel.

Q104. You are deploying a new storage system for your mobile application, which is a media streaming service.
You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity ‘Movie’the property ‘actors’and the property ‘tags’ have multiple values but the property ‘date released’ does not. A typical query would ask for all movies with actor=<actorname>ordered by date_releasedor all movies with tag=Comedyordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

C: Set the following in your entity options: exclude_from_indexes = ‘actors, tags’ D: Set the following in your entity options: exclude_from_indexes = ‘date_published’

Option A

Option B.

Option C

Option D

Q105. Which of the following statements is NOT true regarding Bigtable access roles?

Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.

To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.

You can configure access control only at the project level.

To give a user access to only one table in a project, you must configure access through your application.

Q106. You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

Transform text files to compressed Avro using Cloud Dataflow. Use BigQuery for storage and query.

Transform text files to compressed Avro using Cloud Dataflow. Use Cloud Storage and BigQuery permanent linked tables for query.

Compress text files to gzip using the Grid Computing Tools. Use BigQuery for storage and query.

Compress text files to gzip using the Grid Computing Tools. Use Cloud Storage, and then import into Cloud Bigtable for query.

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Quiz Training Materials

Real Professional-Data-Engineer are Uploaded by TrainingQuiz provide 2026 Latest Professional-Data-Engineer Practice Tests Dumps [Q86-Q106]

Ensure Solution Quality

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Quiz Training Materials

Real Professional-Data-Engineer are Uploaded by TrainingQuiz provide 2026 Latest Professional-Data-Engineer Practice Tests Dumps [Q86-Q106]

Ensure Solution Quality

Introduction

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2022 Professional-Cloud-Architect dumps review – Professional Quiz Study Materials [Q143-Q162]

[2024] Pass Professional-Cloud-Network-Engineer Exam – Real Questions & Answers [Q92-Q111]

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