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Google Professional Data Engineer Certified Professional salary

The average salary of a Google Professional Data Engineer Certified Expert in

India - 25,42,327 INR
Europe - 135,347 EURO
United State - 151,247 USD
England - 115,632 POUND

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Google Professional-Data-Engineer Exam is an essential certification for professionals in the data engineering field. It demonstrates the candidate's knowledge and expertise in designing, developing, and implementing data solutions using Google Cloud Platform's technologies. Google Certified Professional Data Engineer Exam certification is recognized globally and is an excellent way to advance your career in data engineering.

Google Certified Professional Data Engineer Exam Sample Questions (Q183-Q188):

NEW QUESTION # 183
You are creating a model to predict housing prices. Due to budget constraints, you must run it on a single
resource-constrained virtual machine. Which learning algorithm should you use?

A. Feedforward neural network
B. Recurrent neural network
C. Logistic classification
D. Linear regression

Answer: D

NEW QUESTION # 184
You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket, which contains a large number of files. You notice that queries against this table are slow. You want to improve the performance of these queries. What should you do?

A. Change the storage class of the Hive partitioned data objects from Coldline to Standard.
B. Migrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.
C. Upgrade the external table to a BigLake table. Enable metadata caching for the table.
D. Create an individual external table for each Hive partition by using a common table name prefix.
Use wildcard table queries to reference the partitioned data.

Answer: C

NEW QUESTION # 185
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?

A. Convert the streaming insert code to batch load for individual messages.
B. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.
C. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
D. Re-write the application to load accumulated data every 2 minutes.

Answer: B

Explanation:
The data is first comes to buffer and then written to Storage. If we are running queries in buffer we will face above mentioned issues. If we wait for the bigquery to write the data to storage then we won't face the issue. So We need to wait till it's written to storage.

NEW QUESTION # 186
Which of these statements about exporting data from BigQuery is false?

A. To export more than 1 GB of data, you need to put a wildcard in the destination filename.
B. The only compression option available is GZIP.
C. The only supported export destination is Google Cloud Storage.
D. Data can only be exported in JSON or Avro format.

Answer: D

Explanation:
Data can be exported in CSV, JSON, or Avro format. If you are exporting nested or repeated data, then CSV format is not supported.
https://cloud.google.com/bigquery/docs/exporting-data

NEW QUESTION # 187
Different teams in your organization store customer and performance data in BigOuery. Each team needs to keep full control of their collected data, be able to query data within their projects, and be able to exchange their data with other teams. You need to implement an organization-wide solution, while minimizing operational tasks and costs. What should you do?

A. Ask each team to create authorized views of their data. Grant the biquery. jobUser role to each team.
B. Create a BigQuery scheduled query to replicate all customer data into team projects.
C. Enable each team to create materialized views of the data they need to access in their projects.
D. Ask each team to publish their data in Analytics Hub. Direct the other teams to subscribe to them.

Answer: D

Explanation:
To enable different teams to manage their own data while allowing data exchange across the organization, using Analytics Hub is the best approach. Here's why option C is the best choice:
* Analytics Hub:
* Analytics Huballows teams to publish their data as data exchanges, making it easy for other teams to discover and subscribe to the data they need.
* This approach maintains each team's control over their data while facilitating easy and secure data sharing across the organization.
* Data Publishing and Subscribing:
* Teams can publish datasets they control, allowing them to manage access and updates independently.
* Other teams can subscribe to these published datasets, ensuring they have access to the latest data without duplicating efforts.
* Minimized Operational Tasks and Costs:
* This method reduces the need for complex replication or data synchronization processes, minimizing operational overhead.
* By centralizing data sharing through Analytics Hub, it also reduces storage costs associated with duplicating large datasets.
Steps to Implement:
* Set Up Analytics Hub:
* Enable Analytics Hub in your Google Cloud project.
* Provide training to teams on how to publish and subscribe to data exchanges.
* Publish Data:
* Each team publishes their datasets in Analytics Hub, configuring access controls and metadata as needed.
* Subscribe to Data:
* Teams that need access to data from other teams can subscribe to the relevant data exchanges, ensuring they always have up-to-date data.
Reference Links:
* Analytics Hub Documentation
* Publishing Data in Analytics Hub
* Subscribing to Data in Analytics Hub

NEW QUESTION # 188
......

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