Free Access Google.Professional-Data-Engineer.v2025-09-04.q126 Practice Test (Page 7)

Question 26

Your company built a TensorFlow neutral-network model with a large number of neurons and layers. The model fits well for the training data. However, when tested against new data, it performs poorly. What method can you employ to address this?

A.Threading

B.Serialization

C.Dropout Methods

D.Dimensionality Reduction

Question 27

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?

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

B.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.

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

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

Question 28

Your company uses a proprietary system to send inventory data every 6 hours to a data ingestion service in the cloud. Transmitted data includes a payload of several fields and the timestamp of the transmission. If there are any concerns about a transmission, the system re-transmits the data. How should you deduplicate the data most efficiency?

A.Assign global unique identifiers (GUID) to each data entry.

B.Compute the hash value of each data entry, and compare it with all historical data.

C.Store each data entry as the primary key in a separate database and apply an index.

D.Maintain a database table to store the hash value and other metadata for each data entry.

Question 29

Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of data. They want to improve this performance while minimizing cost. What should they do?

A.Redefine the schema by evenly distributing reads and writes across the row space of the table.

B.The performance issue should be resolved over time as the site of the BigDate cluster is increased.

C.Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.

D.Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.

Question 30

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 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.

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 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.

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.

Correct Answer: D

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

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