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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 Certified Professional Data Engineer Exam Sample Questions (Q351-Q356):

NEW QUESTION # 351
MJTelco is building a custom interface to share dat
a. They have these requirements:
They need to do aggregations over their petabyte-scale datasets.
They need to scan specific time range rows with a very fast response time (milliseconds).
Which combination of Google Cloud Platform products should you recommend?

A. BigQuery and Cloud Bigtable
B. Cloud Bigtable and Cloud SQL
C. Cloud Datastore and Cloud Bigtable
D. BigQuery and Cloud Storage

Answer: A

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NEW QUESTION # 352
You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible. What should you do?

A. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
B. Load the data every 30 minutes into a new partitioned table in BigQuery.
C. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
D. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Answer: A

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NEW QUESTION # 353
You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity 'Movie' the property 'actors' and the property 'tags' have multiple values but the property 'date released' does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

A. Manually configure the index in your index config as follows:
B. Set the following in your entity options: exclude_from_indexes = 'date_published'
C. Manually configure the index in your index config as follows:
D. Set the following in your entity options: exclude_from_indexes = 'actors, tags'

Answer: C

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NEW QUESTION # 354
What is the general recommendation when designing your row keys for a Cloud Bigtable schema?

A. Keep your row key as long as the field permits
B. Include multiple time series values within the row key
C. Keep the row keep as an 8 bit integer
D. Keep your row key reasonably short

Answer: D

Explanation:
Explanation
A general guide is to, keep your row keys reasonably short. Long row keys take up additional memory and storage and increase the time it takes to get responses from the Cloud Bigtable server.
Reference: https://cloud.google.com/bigtable/docs/schema-design#row-keys

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NEW QUESTION # 355
Which of these statements about exporting data from BigQuery is false?

A. The only supported export destination is Google Cloud Storage.
B. The only compression option available is GZIP.
C. To export more than 1 GB of data, you need to put a wildcard in the destination filename.
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

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NEW QUESTION # 356
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