[2026] New Professional-Data-Engineer exam dumps Use Updated Google Exam
Verified Professional-Data-Engineer Dumps Q&As - Professional-Data-Engineer Test Engine with Correct Answers
Google Professional-Data-Engineer certification exam is designed to validate the skills and knowledge of individuals working in the field of data engineering. Google Certified Professional Data Engineer Exam certification is intended for those professionals who have expertise in designing, building, and maintaining data processing systems using Google Cloud Platform services. Professional-Data-Engineer exam evaluates the candidates' ability to design, implement, and manage data processing systems, as well as their understanding of data analysis and machine learning concepts.
The Professional-Data-Engineer certification is designed for data professionals who are interested in working with large-scale data processing systems on the Google Cloud Platform. Google Certified Professional Data Engineer Exam certification is ideal for individuals who have experience working with data processing systems and are looking to advance their careers. Google Certified Professional Data Engineer Exam certification is also suitable for individuals who are interested in starting a career in data engineering and want to gain the skills and knowledge necessary to succeed in this field.
NEW QUESTION # 89
Your organization has two Google Cloud projects, project A and project B.
In project A, you have a Pub/Sub topic that receives data from confidential sources. Only the resources in project A should be able to access the data in that topic. You want to ensure that project B and any future project cannot access data in the project A topic. What should you do?
- A. Add firewall rules in project A so only traffic from the VPC in project A is permitted.
- B. Configure VPC Service Controls in the organization with a perimeter around the VPC of project A.
- C. Use Identity and Access Management conditions to ensure that only users and service accounts in project A can access resources in project.
- D. Configure VPC Service Controls in the organization with a perimeter around project A.
Answer: D
Explanation:
Identity and Access Management (IAM) is the recommended way to control access to Pub/Sub resources, such as topics and subscriptions. IAM allows you to grant roles and permissions to users and service accounts at the project level or the individual resource level. You can also use IAM conditions to specify additional attributes for granting or denying access, such as time, date, or origin. By using IAM conditions, you can ensure that only the resources in project A can access the data in the project A topic, regardless of the network configuration or the VPC Service Controls. You can also prevent project B and any future project from accessing the data in the project A topic by not granting them any roles or permissions on the topic.
Option A is not a good solution, as VPC Service Controls are designed to prevent data exfiltration from Google Cloud resources to the public internet, not to control access between Google Cloud projects. VPC Service Controls create a perimeter around the resources of one or more projects, and restrict the communication with resources outside the perimeter. However, VPC Service Controls do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, configuring VPC Service Controls with a perimeter around the VPC of project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions.
Option B is not a good solution, as firewall rules are used to control the ingress and egress traffic to and from the VPC network of a project. Firewall rules do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, adding firewall rules in project A to only permit traffic from the VPC in project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions.
Option C is not a good solution, as VPC Service Controls are designed to prevent data exfiltration from Google Cloud resources to the public internet, not to control access between Google Cloud projects. VPC Service Controls create a perimeter around the resources of one or more projects, and restrict the communication with resources outside the perimeter. However, VPC Service Controls do not apply to Pub/Sub, as Pub/Sub is not associated with any specific IP address or VPC network. Therefore, configuring VPC Service Controls with a perimeter around project A would not prevent project B or any future project from accessing the data in the project A topic, if they have the necessary IAM roles and permissions. Reference: Access control with IAM | Cloud Pub/Sub Documentation | Google Cloud, [Using IAM Conditions | Cloud IAM Documentation | Google Cloud], [VPC Service Controls overview | Google Cloud], [Using VPC Service Controls | Google Cloud], [Pub/Sub tier capabilities | Memorystore for Redis | Google Cloud].
NEW QUESTION # 90
You work for a large financial institution that is planning to use Dialogflow to create a chatbot for the company's mobile app You have reviewed old chat logs and lagged each conversation for intent based on each customer's stated intention for contacting customer service About 70% of customer requests are simple requests that are solved within 10 intents The remaining 30% of inquiries require much longer, more complicated requests Which intents should you automate first?
- A. Automate intents in places where common words such as "payment" appear only once so the software isn't confused
- B. Automate a blend of the shortest and longest intents to be representative of all intents
- C. Automate the 10 intents that cover 70% of the requests so that live agents can handle more complicated requests
- D. Automate the more complicated requests first because those require more of the agents' time
Answer: C
NEW QUESTION # 91
You are using BigQuery's ML.GENERATE_TEXT function to write marketing materials for a new product launch. The problem is, the AI is generating random text that does not always relate to the product. You want the simplest way to improve the output to generate a marketing copy. What should you do?
- A. Experiment with different values for the top_p setting in ML.GENERATE_TEXT.
- B. Seed the prompt with examples of product descriptions for similar products.
- C. Fine-tune a more specialized language model on a dataset of product features.
- D. Experiment with different values for the temperature setting in ML.GENERATE_TEXT.
Answer: D
Explanation:
The temperature parameter is the primary control for randomness and "creativity" in Large Language Models (LLMs) used within BigQuery ML.
* Randomness vs. Focus: A high temperature (e.g., closer to 1.0) leads to more random, diverse, and sometimes irrelevant output because the model is more likely to choose lower-probability tokens. If the AI is generating "random text," lowering the temperature (e.g., to 0.2 or 0.1) makes the model more deterministic and focused on the most likely next tokens related to the input prompt.
* Simplest Way: Adjusting a single parameter in a SQL function is the "simplest" approach compared to gathering datasets for few-shot prompting (A) or the high complexity and cost of fine-tuning (C).
* temperature vs. top_p: While top_p (nucleus sampling) also affects randomness, temperature is the standard first-line control for the overall "entropy" of the model's responses. Official Google documentation often suggests adjusting temperature first to curb hallucination or excessive randomness.
Reference: Google Cloud Documentation on ML.GENERATE_TEXT:
"temperature: A value in the range [0.0, 1.0]... It controls the degree of randomness in token selection.
Lower temperature values are good for prompts that require a more deterministic and less open-ended response, while higher temperature values can lead to more diverse or creative results." (Source: ML.
GENERATE_TEXT arguments)
"To get more predictable responses from the model, use a lower temperature." (Source: BigQuery ML generative AI overview)
NEW QUESTION # 92
You need (o give new website users a globally unique identifier (GUID) using a service that takes in data points and returns a GUID This data is sourced from both internal and external systems via HTTP calls that you will make via microservices within your pipeline There will be tens of thousands of messages per second and that can be multithreaded, and you worry about the backpressure on the system How should you design your pipeline to minimize that backpressure?
- A. Batch the job into ten-second increments
- B. Create a new object in the startBundle method of DoFn
- C. Call out to the service via HTTP
- D. Create the pipeline statically in the class definition
Answer: C
NEW QUESTION # 93
You are developing a new deep teaming model that predicts a customer's likelihood to buy on your ecommerce site. Alter running an evaluation of the model against both the original training data and new test data, you find that your model is overfitting the data. You want to improve the accuracy of the model when predicting new data. What should you do?
- A. Increase the size of the training dataset, and decrease the number of input features.
- B. Increase the size of the training dataset, and increase the number of input features.
- C. Reduce the size of the training dataset, and decrease the number of input features.
- D. Reduce the size of the training dataset, and increase the number of input features.
Answer: A
Explanation:
https://machinelearningmastery.com/impact-of-dataset-size-on-deep-learning-model-skill-and-performance-estim
NEW QUESTION # 94
You are working on a sensitive project involving private user dat
- A. Grant the consultant the Cloud Dataflow Developer role on the project.
- B. Grant the consultant the Viewer role on the project.
- C. You have set up a project on Google Cloud Platform to house your work internally. An external consultant is going to assist with coding a complex transformation in a Google Cloud Dataflow pipeline for your project. How should you maintain users' privacy?
- D. Create a service account and allow the consultant to log on with it.
- E. Create an anonymized sample of the data for the consultant to work with in a different project.
Answer: A
NEW QUESTION # 95
A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL `dataset.model', table . How should you create the ML pipeline?
user_features)
- A. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
- B. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.
- C. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query.
Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable. - D. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
Answer: C
NEW QUESTION # 96
How can you get a neural network to learn about relationships between categories in a categorical feature?
- A. Create a multi-hot column
- B. Create a hash bucket
- C. Create an embedding column
- D. Create a one-hot column
Answer: C
Explanation:
There are two problems with one-hot encoding. First, it has high dimensionality, meaning that instead of having just one value, like a continuous feature, it has many values, or dimensions. This makes computation more time-consuming, especially if a feature has a very large number of categories. The second problem is that it doesn't encode any relationships between the categories. They are completely independent from each other, so the network has no way of knowing which ones are similar to each other.
Both of these problems can be solved by representing a categorical feature with an embedding column.
The idea is that each category has a smaller vector with, let's say, 5 values in it. But unlike a one-hot vector, the values are not usually 0. The values are weights, similar to the weights that are used for basic features in a neural network. The difference is that each category has a set of weights (5 of them in this case).
You can think of each value in the embedding vector as a feature of the category. So, if two categories are very similar to each other, then their embedding vectors should be very similar too.
Reference: https://cloudacademy.com/google/introduction-to-google-cloud-machine-learning-engine- course/a-wide-and-deep-model.html
NEW QUESTION # 97
You work for a manufacturing plant that batches application log files together into a single log file once a
day at 2:00 AM. You have written a Google Cloud Dataflow job to process that log file. You need to make
sure the log file in processed once per day as inexpensively as possible. What should you do?
- A. Change the processing job to use Google Cloud Dataproc instead.
- B. Create a cron job with Google App Engine Cron Service to run the Cloud Dataflow job.
- C. Configure the Cloud Dataflow job as a streaming job so that it processes the log data immediately.
- D. Manually start the Cloud Dataflow job each morning when you get into the office.
Answer: B
NEW QUESTION # 98
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?
- A. 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. - B. 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. - C. 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. - D. 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.
Answer: D
Explanation:
This option is the best way to configure Dataplex for a data mesh architecture, as it allows each data engineering team to have full ownership and control over their data products, while also enabling easy discovery and sharing of the curated data across the organization12. By creating a Dataplex virtual lake for each data product, you can isolate the data assets and resources for each domain, and avoid conflicts and dependencies between different teams3. By creating multiple zones for landing, raw, and curated data, you can enforce different security and governance policies for each stage of the data curation process, and ensure that only authorized users can access the data assets45. By providing the data engineering teams with full access to the virtual lake assigned to their data product, you can empower them to manage and monitor their data products, and leverage the Dataplex features such as tagging, quality, and lineage.
Option A is not suitable, as it creates a single point of failure and a bottleneck for the data mesh, and does not allow for fine-grained access control and governance for different data products2. Option B is also not suitable, as it does not isolate the data assets and resources for each data product, and assigns permissions at the zone level, which may not reflect the different roles and responsibilities of the data engineering teams34. Option C is better than option A and B, but it does not create multiple zones for landing, raw, and curated data, which may compromise the security and quality of the data products5. References:
* 1: Building a data mesh on Google Cloud using BigQuery and Dataplex | Google Cloud Blog
* 2: Data Mesh - 7 Effective Practices to Get Started - Confluent
* 3: Best practices | Dataplex | Google Cloud
* 4: Secure your lake | Dataplex | Google Cloud
* 5: Zones | Dataplex | Google Cloud
* [6]: Managing a Data Mesh with Dataplex - ROI Training
NEW QUESTION # 99
What Dataflow concept determines when a Window's contents should be output based on certain criteria being met?
- A. Sessions
- B. Triggers
- C. Windows
- D. OutputCriteria
Answer: B
Explanation:
Triggers control when the elements for a specific key and window are output. As elements arrive, they are put into one or more windows by a Window transform and its associated WindowFn, and then passed to the associated Trigger to determine if the Windows contents should be output.
NEW QUESTION # 100
You're training a model to predict housing prices based on an available dataset with real estate properties.
Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?
- A. Provide latitude and longitude as input vectors to your neural net.
- B. Create a feature cross of latitude and longitude, bucketize at the minute level and use L1 regularization during optimization.
- C. Create a numeric column from a feature cross of latitude and longitude.
- D. Create a feature cross of latitude and longitude, bucketize it at the minute level and use L2 regularization during optimization.
Answer: B
Explanation:
Use L1 regularization when you need to assign greater importance to more influential features. It shrinks less important feature to 0.
L2 regularization performs better when all input features influence the output & all with the weights are of equal size.
NEW QUESTION # 101
A data scientist has created a BigQuery ML model and asks you to create an ML pipeline to serve predictions. You have a REST API application with the requirement to serve predictions for an individual user ID with latency under 100 milliseconds. You use the following query to generate predictions: SELECT predicted_label, user_id FROM ML.PREDICT (MODEL 'dataset.model', table user_features). How should you create the ML pipeline?
- A. Add a WHERE clause to the query, and grant the BigQuery Data Viewer role to the application service account.
- B. Create a Cloud Dataflow pipeline using BigQueryIO to read results from the query. Grant the Dataflow Worker role to the application service account.
- C. Create a Cloud Dataflow pipeline using BigQueryIO to read predictions for all users from the query. Write the results to Cloud Bigtable using BigtableIO. Grant the Bigtable Reader role to the application service account so that the application can read predictions for individual users from Cloud Bigtable.
- D. Create an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
Answer: C
NEW QUESTION # 102
You are a head of BI at a large enterprise company with multiple business units that each have different priorities and budgets. You use on-demand pricing for BigQuery with a quota of 2K concurrent on-demand slots per project. Users at your organization sometimes don't get slots to execute their query and you need to correct this. You'd like to avoid introducing new projects to your account.
What should you do?
- A. Convert your batch BQ queries into interactive BQ queries.
- B. Create an additional project to overcome the 2K on-demand per-project quota.
- C. Switch to flat-rate pricing and establish a hierarchical priority model for your projects.
- D. Increase the amount of concurrent slots per project at the Quotas page at the Cloud Console.
Answer: C
NEW QUESTION # 103
You're training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.
What should you do?
- A. Provide latitude and longtitude as input vectors to your neural net.
- B. Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.
- C. Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.
- D. Create a numeric column from a feature cross of latitude and longtitude.
Answer: D
Explanation:
Feature Crosses:
Feature crosses combine multiple features into a single feature that captures the interaction between them. For location data, a feature cross of latitude and longitude can capture spatial dependencies that affect housing prices.
This approach allows the neural network to learn complex patterns related to geographic location more effectively than using raw latitude and longitude values.
Numerical Representation:
Converting the feature cross into a numeric column simplifies the input for the neural network and can improve the model's ability to learn from the data.
This method ensures that the model can leverage the combined information from both latitude and longitude in a meaningful way.
Model Training:
Using a numeric column for the feature cross helps in regularizing the model and prevents overfitting, which is crucial for achieving good generalization on unseen data.
Reference:
To engineer a feature that incorporates the physical dependency of location on housing prices for a neural network, creating a numeric column from a feature cross of latitude and longitude is the most effective approach. Here's why option B is the best choice:
NEW QUESTION # 104
What are all of the BigQuery operations that Google charges for?
- A. Queries and streaming inserts
- B. Storage, queries, and streaming inserts
- C. Storage, queries, and exporting data
- D. Storage, queries, and loading data from a file
Answer: B
Explanation:
Explanation
Google charges for storage, queries, and streaming inserts. Loading data from a file and exporting data are free operations.
Reference: https://cloud.google.com/bigquery/pricing
NEW QUESTION # 105
Government regulations in the banking industry mandate the protection of client's personally identifiable information (PII). Your company requires PII to be access controlled encrypted and compliant with major data protection standards In addition to using Cloud Data Loss Prevention (Cloud DIP) you want to follow Google-recommended practices and use service accounts to control access to PII. What should you do?
- A. Assign the required identity and Access Management (IAM) roles to every employee, and create a single service account to access protect resources
- B. Use Cloud Storage to comply with major data protection standards. Use multiple service accounts attached to IAM groups to grant the appropriate access to each group
- C. Use Cloud Storage to comply with major data protection standards. Use one service account shared by all users
- D. Use one service account to access a Cloud SQL database and use separate service accounts for each human user
Answer: B
NEW QUESTION # 106
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