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Google Professional-Data-Engineer Exam is a certification offered by Google to professionals who specialize in data engineering. Professional-Data-Engineer exam is designed to test the candidate's understanding of data processing systems, data modeling, data governance, and data transformation. Google Certified Professional Data Engineer Exam certification aims to validate the candidate's expertise in Google Cloud Platform's data engineering technologies and their ability to design and develop effective data solutions.

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Google Certified Professional Data Engineer Exam Sample Questions (Q227-Q232):

NEW QUESTION # 227
You are developing an application that uses a recommendation engine on Google Cloud. Your solution
should display new videos to customers based on past views. Your solution needs to generate labels for
the entities in videos that the customer has viewed. Your design must be able to provide very fast filtering
suggestions based on data from other customer preferences on several TB of data. What should you do?

A. Build and train a classification model with Spark MLlib to generate labels. Build and train a second
classification model with Spark MLlib to filter results to match customer preferences. Deploy the
models using Cloud Dataproc. Call the models from your application.
B. Build and train a complex classification model with Spark MLlib to generate labels and filter the results.
Deploy the models using Cloud Dataproc. Call the model from your application.
C. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
Bigtable, and filter the predicted labels to match the user's viewing history to generate preferences.
D. Build an application that calls the Cloud Video Intelligence API to generate labels. Store data in Cloud
SQL, and join and filter the predicted labels to match the user's viewing history to generate
preferences.

Answer: C

NEW QUESTION # 228
You are migrating a large number of files from a public HTTPS endpoint to Cloud Storage. The files are protected from unauthorized access using signed URLs. You created a TSV file that contains the list of object URLs and started a transfer job by using Storage Transfer Service. You notice that the job has run for a long time and eventually failed Checking the logs of the transfer job reveals that the job was running fine until one point, and then it failed due to HTTP 403 errors on the remaining files You verified that there were no changes to the source system You need to fix the problem to resume the migration process. What should you do?

A. Set up Cloud Storage FUSE, and mount the Cloud Storage bucket on a Compute Engine Instance Remove the completed files from the TSV file Use a shell script to iterate through the TSV file and download the remaining URLs to the FUSE mount point.
B. Update the file checksums in the TSV file from using MD5 to SHA256. Remove the completed files from the TSV file and rerun the Storage Transfer Service job.
C. Create a new TSV file for the remaining files by generating signed URLs with a longer validity period. Split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel.
D. Renew the TLS certificate of the HTTPS endpoint Remove the completed files from the TSV file and rerun the Storage Transfer Service job.

Answer: C

Explanation:
A signed URL is a URL that provides limited permission and time to access a resource on a web server. It is often used to grant temporary access to protected files without requiring authentication. Storage Transfer Service is a service that allows you to transfer data from external sources, such as HTTPS endpoints, to Cloud Storage buckets. You can use a TSV file to specify the list of URLs to transfer. In this scenario, the most likely cause of the HTTP 403 errors is that the signed URLs have expired before the transfer job could complete. This could happen if the signed URLs have a short validity period or the transfer job takes a long time due to the large number of files or network latency. To fix the problem, you need to create a new TSV file for the remaining files by generating new signed URLs with a longer validity period. This will ensure that the URLs do not expire before the transfer job finishes. You can use the Cloud Storage tools or your own program to generate signed URLs. Additionally, you can split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel. This will speed up the transfer process and reduce the risk of errors. Reference:
Signed URLs | Cloud Storage Documentation
V4 signing process with Cloud Storage tools
V4 signing process with your own program
Using a URL list file
What Is a 403 Forbidden Error (and How Can I Fix It)?

NEW QUESTION # 229
You are building a streaming Dataflow pipeline that ingests noise level data from hundreds of sensors placed near construction sites across a city. The sensors measure noise level every ten seconds, and send that data to the pipeline when levels reach above 70 dBA. You need to detect the average noise level from a sensor when data is received for a duration of more than 30 minutes, but the window ends when no data has been received for 15 minutes What should you do?

A. Use session windows with a 15-minute gap duration.
B. Use session windows with a 30-mmute gap duration.
C. Use hopping windows with a 15-mmute window, and a thirty-minute period.
D. Use tumbling windows with a 15-mmute window and a fifteen-minute. withAllowedLateness operator.

Answer: A

Explanation:
Session windows are dynamic windows that group elements based on the periods of activity. They are useful for streaming data that is irregularly distributed with respect to time. In this case, the noise level data from the sensors is only sent when it exceeds a certain threshold, and the duration of the noise events may vary.
Therefore, session windows can capture the average noise level for each sensor during the periods of high noise, and end the window when there is no data for a specified gap duration. The gap duration should be 15 minutes, as the requirement is to end the window when no data has been received for 15 minutes. A 30-minute gap duration would be too long and may miss some noise events that are shorter than 30 minutes. Tumbling windows and hopping windows are fixed windows that group elements based on a fixed time interval. They are not suitable for this use case, as they may split or overlap the noise events from the sensors, and do not account for the periods of inactivity. References:
* Windowing concepts
* Session windows
* Windowing in Dataflow

NEW QUESTION # 230
You are a BigQuery admin supporting a team of data consumers who run ad hoc queries and downstream reporting in tools such as Looker. All data and users are combined under a single organizational project. You recently noticed some slowness in query results and want to troubleshoot where the slowdowns are occurring.
You think that there might be some job queuing or slot contention occurring as users run jobs, which slows down access to results. You need to investigate the query job information and determine where performance is being affected. What should you do?

A. Use available administrative resource charts to determine how slots are being used and how jobs are performing over time. Run a query on the INFORMATION_SCHEMA to review query performance.
B. Use Cloud Monitoring to view BigQuery metrics and set up alerts that let you know when a certain percentage of slots were used.
C. Use slot reservations for your project to ensure that you have enough query processing capacity and are able to allocate available slots to the slower queries.
D. Use Cloud Logging to determine if any users or downstream consumers are changing or deleting access grants on tagged resources.

Answer: A

Explanation:
To troubleshoot query performance issues related to job queuing or slot contention in BigQuery, using administrative resource charts along with querying the INFORMATION_SCHEMA is the best approach.
Here's why option D is the best choice:
Administrative Resource Charts:
BigQuery provides detailed resource charts that show slot usage and job performance over time. These charts help identify patterns of slot contention and peak usage times.
INFORMATION_SCHEMA Queries:
The INFORMATION_SCHEMA tables in BigQuery provide detailed metadata about query jobs, including execution times, slots consumed, and other performance metrics.
Running queries on INFORMATION_SCHEMA allows you to pinpoint specific jobs causing contention and analyze their performance characteristics.
Comprehensive Analysis:
Combining administrative resource charts with detailed queries on INFORMATION_SCHEMA provides a holistic view of the system's performance.
This approach enables you to identify and address the root causes of performance issues, whether they are due to slot contention, inefficient queries, or other factors.
Steps to Implement:
Access Administrative Resource Charts:
Use the Google Cloud Console to view BigQuery's administrative resource charts. These charts provide insights into slot utilization and job performance metrics over time.
Run INFORMATION_SCHEMA Queries:
Execute queries on BigQuery's INFORMATION_SCHEMA to gather detailed information about job performance. For example:
SELECT
creation_time,
job_id,
user_email,
query,
total_slot_ms / 1000 AS slot_seconds,
total_bytes_processed / (1024 * 1024 * 1024) AS processed_gb,
total_bytes_billed / (1024 * 1024 * 1024) AS billed_gb
FROM
`region-us`.INFORMATION_SCHEMA.JOBS_BY_PROJECT
WHERE
creation_time > TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 1 DAY)
AND state = 'DONE'
ORDER BY
slot_seconds DESC
LIMIT 100;
Analyze and Optimize:
Use the information gathered to identify bottlenecks, optimize queries, and adjust resource allocations as needed to improve performance.
Reference Links:
Monitoring BigQuery Slots
BigQuery INFORMATION_SCHEMA
BigQuery Performance Best Practices

NEW QUESTION # 231
When you design a Google Cloud Bigtable schema it is recommended that you _________.

A. Avoid schema designs that are based on NoSQL concepts
B. Create schema designs that require atomicity across rows
C. Create schema designs that are based on a relational database design
D. Avoid schema designs that require atomicity across rows

Answer: D

Explanation:
Explanation
All operations are atomic at the row level. For example, if you update two rows in a table, it's possible that one row will be updated successfully and the other update will fail. Avoid schema designs that require atomicity across rows.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

NEW QUESTION # 232
......

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Pass Guaranteed Quiz Latest Professional-Data-Engineer - Google Certified Professional Data Engineer Exam Exam Torrent

Professional-Data-Engineer Valid Exam Review - Valid Professional-Data-Engineer Test Dumps

Google Certified Professional Data Engineer Exam Sample Questions (Q227-Q232):