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Google Professional Data Engineer exam dumps: questions 101 to 200 of 278

Page 2 of the free Google Professional Data Engineer question bank for the Professional Data Engineer exam. Questions 101 to 200 are listed below, the first 5 in full with answers and explanations. Back to page 1 for the exam overview and FAQ.

Question bank last updated December 2024

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Free Google Professional Data Engineer practice questions

Questions 101 to 105 of 278

Pick an answer before you open the explanation. Each question also has its own page with a permalink.

Google Professional Data Engineer Question 101

Select 3Google Cloud Platform

You are building a multilingual chatbot on Google Cloud that needs to handle language translation, entity recognition, and sentiment analysis. Given the requirements, which Google Cloud services would you use to implement this solution efficiently?

A
Cloud Translation API
B
Cloud Natural Language API
C
Vertex AI
D
Dialogflow CX
E
Cloud Speech-to-Text

Show answer and explanation

Correct answers: A, B, D

Explanation

To implement a multilingual chatbot with capabilities for language translation, entity recognition, and sentiment analysis, you would use Cloud Translation API for translation, Cloud Natural Language API for entity recognition and sentiment analysis, and Dialogflow CX for building the conversational interface. While other services like Vertex AI and Cloud Speech-to-Text are powerful, they are not directly relevant to the tasks described in this scenario.

A. Correct.
Cloud Translation API is used to translate text between different languages, which is essential for handling multilingual inputs.
B. Correct.
Cloud Natural Language API is used to perform tasks like entity recognition and sentiment analysis, which are key requirements for the chatbot.
C. Incorrect.
Vertex AI is used for building and deploying custom machine learning models, but it is not directly needed for the specified tasks in this scenario.
D. Correct.
Dialogflow CX is specifically designed for building conversational interfaces like chatbots, making it a suitable choice for this use case.
E. Incorrect.
Cloud Speech-to-Text is used for converting spoken language into text, but the scenario does not mention voice input as a requirement.

Google Professional Data Engineer Question 102

Single answerGoogle Cloud Platform

You are tasked with building a data ingestion pipeline in Google Cloud for a one-time batch process. The data is stored in CSV files on a remote FTP server, and you need to transfer the files to BigQuery for analysis. The solution should ensure minimal setup and leverage Google Cloud services effectively. Which approach should you choose?

A
Use Cloud Storage Transfer Service to directly transfer the data from the FTP server to BigQuery.
B
Download the files locally, upload them to Cloud Storage, and then use BigQuery's import feature to load the data.
C
Write a custom Python script using the BigQuery API to fetch data from the FTP server and load it into BigQuery.
D
Use a Cloud Function triggered by a Pub/Sub message to retrieve the data from the FTP server, save it to Cloud Storage, and then load it into BigQuery.

Show answer and explanation

Correct answer: B

Explanation

For a one-time batch ingestion process, manually downloading the data, uploading it to Cloud Storage, and using BigQuery's import feature is the simplest and most efficient approach. It avoids unnecessary complexity and leverages BigQuery's built-in capabilities for data ingestion.

A. Incorrect.
Cloud Storage Transfer Service does not support direct transfers from FTP servers. It is designed for transferring data from supported cloud providers or HTTP/HTTPS endpoints to Cloud Storage.
B. Correct.
This is a valid approach for a one-time batch process. Manually downloading the files locally, uploading them to Cloud Storage, and using BigQuery's import feature is straightforward and requires minimal setup for a one-time task.
C. Incorrect.
While it is possible to write a custom Python script using the BigQuery API, this approach requires significant effort and is not recommended for a one-time batch process unless absolutely necessary.
D. Incorrect.
Using a Cloud Function and Pub/Sub is better suited for automated or recurring ingestion pipelines. It introduces unnecessary complexity for a one-time batch process.

Google Professional Data Engineer Question 103

Single answerGoogle Cloud Platform

You are a data engineer working for a retail company. The marketing team has requested a one-time ingestion of a large CSV file containing customer feedback data into BigQuery for analysis. The file is currently stored in a Cloud Storage bucket. What is the most efficient and cost-effective method to handle this ad hoc ingestion?

A
Use the BigQuery Data Transfer Service to schedule a transfer from Cloud Storage.
B
Manually upload the CSV file into BigQuery using the BigQuery Console.
C
Write a Dataflow pipeline to read the file from Cloud Storage and write it to BigQuery.
D
Use the bq command-line tool to load the file directly from Cloud Storage into BigQuery.

Show answer and explanation

Correct answer: D

Explanation

For one-time ingestion of a file from Cloud Storage into BigQuery, the bq command-line tool provides a straightforward, efficient, and cost-effective solution. It is specifically designed for tasks like loading data directly into BigQuery without the need for complex pipelines or recurring schedules. The other options either involve unnecessary complexity, are not suitable for ad hoc ingestion, or are less efficient for large files.

A. Incorrect.
The BigQuery Data Transfer Service is intended for automated, recurring data transfers and does not support ad hoc ingestion of files from Cloud Storage.
B. Incorrect.
Manually uploading the file via the BigQuery Console is possible, but it is less efficient and not suitable for large files compared to using the command-line tool.
C. Incorrect.
While Dataflow can handle complex data processing and ingestion, it is overkill for a simple one-time ingestion and incurs additional cost and complexity.
D. Correct.
The bq command-line tool is the most efficient and cost-effective method for ad hoc ingestion as it supports loading files directly from Cloud Storage into BigQuery with minimal setup.

Google Professional Data Engineer Question 104

Single answerGoogle Cloud Platform

You are designing a data pipeline to ingest a one-time batch of data from an on-premises MySQL database into Google BigQuery for analysis. The data volume is around 500 GB, and you want to minimize manual intervention while ensuring reliability. Which solution should you choose?

A
Use the BigQuery Data Transfer Service to directly connect to the MySQL database and import the data.
B
Export the MySQL data to CSV files, upload them to a Cloud Storage bucket, and then load them into BigQuery using the BigQuery Data Transfer Service.
C
Export the MySQL data to CSV files, upload them to a Cloud Storage bucket, and use the BigQuery Data Loading feature to import the data.
D
Set up a Dataflow pipeline to stream the data from MySQL into BigQuery in real-time.

Show answer and explanation

Correct answer: C

Explanation

The most suitable solution for a one-time batch ingestion of 500 GB of data from MySQL to BigQuery involves exporting the data to CSV files, uploading them to a Cloud Storage bucket, and then using the BigQuery Data Loading feature. This approach ensures reliability, minimizes manual intervention, and leverages scalable tools. Other options either involve unsupported tools (e.g., BigQuery Data Transfer Service) or add unnecessary complexity (e.g., real-time Dataflow pipeline).

A. Incorrect.
The BigQuery Data Transfer Service is not suitable for ingesting data directly from MySQL databases. It is designed for pre-built connectors like Google Ads or Google Analytics.
B. Incorrect.
The BigQuery Data Transfer Service does not support importing data from CSV files. This option is not feasible for this scenario.
C. Correct.
This is the correct approach. Exporting the data to CSV files, uploading them to a Cloud Storage bucket, and then using the BigQuery Data Loading feature ensures reliability and scalability for a one-time batch ingestion.
D. Incorrect.
Setting up a Dataflow pipeline for real-time streaming is not necessary for one-time batch data ingestion. It would also add unnecessary complexity and cost.

Google Professional Data Engineer Question 105

Select 2Google Cloud Platform

You are working for a retail company that needs to ingest and process large volumes of sales data from multiple on-premises databases into Google Cloud for real-time analytics. Which of the following approaches would be most appropriate to achieve this in a scalable and efficient manner?

A
Use Cloud Storage as a staging area and transfer data to BigQuery using scheduled batch jobs.
B
Set up a Pub/Sub topic to stream data from the on-premises databases directly into BigQuery.
C
Leverage Dataflow to implement a streaming pipeline that ingests data from on-premises databases and writes it to BigQuery.
D
Manually export the data from on-premises databases, upload it to Cloud Storage, and then import it into BigQuery using the web UI.
E
Deploy Transfer Appliance to physically ship data to Google Cloud and import the data into BigQuery.

Show answer and explanation

Correct answers: B, C

Explanation

For real-time analytics, a combination of Google Cloud services like Pub/Sub and Dataflow is ideal. Pub/Sub allows real-time ingestion of data from on-premises databases, while Dataflow can process and transform the streaming data before loading it into BigQuery. These services ensure scalability and low latency, unlike batch processing or manual methods which are not suitable for real-time needs.

A. Incorrect.
Using Cloud Storage as a staging area and scheduling batch jobs is suitable for batch processing but does not fulfill the real-time analytics requirement.
B. Correct.
Using a Pub/Sub topic for streaming data directly into BigQuery is a scalable and efficient solution for real-time data ingestion.
C. Correct.
Dataflow is ideal for building streaming pipelines and allows you to process and transform data in real-time before loading it into BigQuery.
D. Incorrect.
Manually exporting, uploading, and importing data is not scalable or efficient for real-time analytics.
E. Incorrect.
Transfer Appliance is designed for bulk data transfers and is not suitable for real-time ingestion or streaming scenarios.

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Google Professional Data Engineer practice questions 101 to 200 of 278

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