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Google Professional-Data-Engineer (Google Certified Professional Data Engineer) Exam is a certification program that validates the skills of a data engineer in designing, building, and managing data processing systems. It is designed to assess the proficiency of a data engineer in using Google Cloud technologies to develop and deploy scalable, reliable, and efficient data pipelines. Professional-Data-Engineer Exam covers various topics, including data processing architectures, data modeling, data analysis, and machine learning.

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Google Certified Professional Data Engineer Exam Sample Questions (Q44-Q49):

NEW QUESTION # 44
You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?

A. Use bq loadto load a batch of sensor data every 60 seconds.
B. Use a Cloud Dataflow pipeline to stream data into the BigQuery table.
C. Use the INSERT statement to insert a batch of data every 60 seconds.
D. Use the MERGE statement to apply updates in batch every 60 seconds.

Answer: C

NEW QUESTION # 45
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 an Authorized View with the provided query. Share the dataset that contains the view with the application service account.
C. Create a Cloud Dataflow pipeline using BigQueryIOto read results from the query. Grant the Dataflow Worker role to the application service account.
D. Create a Cloud Dataflow pipeline using BigQueryIOto 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.

Answer: D

NEW QUESTION # 46
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?

A. 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.
B. Have each application server write the bid events to Google Cloud Pub/Sub as they occur. Use a pull subscription to pull the bid events using Google Cloud Dataflow. Give the bid for each item to the user in the bid event that is processed first.
C. 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.
D. 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.

Answer: A

NEW QUESTION # 47
Your business users need a way to clean and prepare data before using the data for analysis. Your business users are less technically savvy and prefer to work with graphical user interfaces to define their transformations. After the data has been transformed, the business users want to perform their analysis directly in a spreadsheet. You need to recommend a solution that they can use. What should you do?

A. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Looker Studio.
B. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Looker Studio.
C. Use Dataflow to clean the data, and write the results to BigQuery. Analyze the data by using Connected Sheets.
D. Use Dataprep to clean the data, and write the results to BigQuery Analyze the data by using Connected Sheets.

Answer: D

Explanation:
For business users who are less technically savvy and prefer graphical user interfaces, Dataprep is an ideal tool for cleaning and preparing data, as it offers a user-friendly interface for defining data transformations without the need for coding. Once the data is cleaned and prepared, writing the results to BigQuery allows for the storage and management of large datasets. Analyzing the data using Connected Sheets enables business users to work within the familiar environment of a spreadsheet, leveraging the power of BigQuery directly within Google Sheets. This solution aligns with the needs of the users and follows Google's recommended practices for data cleaning, preparation, and analysis.
References:
Connected Sheets | Google Sheets | Google for Developers
Professional Data Engineer Certification Exam Guide | Learn - Google Cloud Engineer Data in Google Cloud | Google Cloud Skills Boost - Qwiklabs

NEW QUESTION # 48
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by
10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (Choose two.)

A. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premises data to the designated storage bucket.
B. Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
C. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
D. Introduce data compression for each file to increase the rate file of file transfer.
E. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.

Answer: A,C

Explanation:
Explanation/Reference:

NEW QUESTION # 49
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Pass Guaranteed 2026 Google Professional-Data-Engineer Pass-Sure Instant Download

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Test Professional-Data-Engineer Engine Version - Professional-Data-Engineer Valid Exam Syllabus

Google Certified Professional Data Engineer Exam Sample Questions (Q44-Q49):

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