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To be eligible for the Google Professional-Data-Engineer Exam, candidates must have experience in data engineering, data analytics, and data warehousing. They must also have experience in designing and implementing solutions using Google Cloud Platform's data processing technologies, such as Cloud Dataflow, BigQuery, and Cloud Dataproc. Furthermore, candidates must have excellent knowledge of SQL, Python, and Java programming languages, as well as experience in data modeling and data visualization.

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Google Professional-Data-Engineer exam is intended for professionals who have experience working with data technologies and who are looking to demonstrate their expertise in designing and implementing data solutions on Google Cloud Platform. Professional-Data-Engineer Exam is designed to test a candidate's knowledge of various data engineering tools and technologies, including Google Cloud Storage, Google BigQuery, and Google Cloud Dataflow, among others.

Google Certified Professional Data Engineer Exam Sample Questions (Q394-Q399):

NEW QUESTION # 394
An online brokerage company requires a high volume trade processing architecture. You need to create a secure queuing system that triggers jobs. The jobs will run in Google Cloud and cat thecompany's Python API to execute trades. You need to efficiently implement a solution. What should you do?

A. Write an application that makes a queue in a NoSQL database
B. Write an application hosted on a Compute Engine instance that makes a push subscription to the Pub
/Sub topic
C. Use Cloud Composer to subscribe to a Pub/Sub tope and can the Python API.
D. Use a Pub/Sub push subscription to trigger a Cloud Function to pass the data to tie Python API.

Answer: A

NEW QUESTION # 395
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?

A. Pipeline
B. Transform
C. Sink API
D. PCollection

Answer: B

Explanation:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.

NEW QUESTION # 396
You need to choose a database to store time series CPU and memory usage for millions of computers. You need to store this data in one-second interval samples. Analysts will be performing real-time, ad hoc analytics against the database. You want to avoid being charged for every query executed and ensure that the schema design will allow for future growth of the dataset. Which database and data model should you choose?

A. Create a narrow table in Cloud Bigtable with a row key that combines the Computer Engine computer identifier with the sample time at each second
B. Create a wide table in Cloud Bigtable with a row key that combines the computer identifier with the sample time at each minute, and combine the values for each second as column data.
C. Create a wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for each second
D. Create a table in BigQuery, and append the new samples for CPU and memory to the table

Answer: A

Explanation:
A tall and narrow table has a small number of events per row, which could be just one event, whereas a short and wide table has a large number of events per row. As explained in a moment, tall and narrow tables are best suited for time-series data. For time series, you should generally use tall and narrow tables. This is for two reasons: Storing one event per row makes it easier to run queries against your data. Storing many events per row makes it more likely that the total row size will exceed the recommended maximum (see Rows can be big but are not infinite). https://cloud.google.com/bigtable/docs/schema-design-time-series#patterns_for_row_key_design

NEW QUESTION # 397
You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?

A. Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage
B. Use gsutil cp -Jto compress the content being uploaded to Cloud Storage
C. Use Transfer Appliance to copy the data to Cloud Storage
D. Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/ sec so it does not interfere with the production traffic

Answer: C

Explanation:
Explanation

NEW QUESTION # 398
Your financial services company is moving to cloud technology and wants to store 50 TB of financial time- series data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data. Which product should they use to store the data?

A. Cloud Bigtable
B. Google Cloud Datastore
C. Google Cloud Storage
D. Google BigQuery

Answer: A

Explanation:
https://cloud.google.com/blog/products/databases/getting-started-with-time-series-trend-predictions-using-gcp

NEW QUESTION # 399
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