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Google Certified Professional Data Engineer Exam Sample Questions (Q145-Q150):

NEW QUESTION # 145
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?

Answer: B


NEW QUESTION # 146
You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data. Which query type should you use?

Answer: B

Explanation:
Explanation
https://cloud.google.com/bigquery/docs/reference/standard-sql/analytic-function-concepts


NEW QUESTION # 147
Which of the following is not possible using primitive roles?

Answer: A

Explanation:
Primitive roles can be used to give owner, editor, or viewer access to a user or group, but they can't be used to separate data access permissions from job-running permissions.
Reference: https://cloud.google.com/bigquery/docs/access-control#primitive_iam_roles


NEW QUESTION # 148
Your company is in a highly regulated industry. One of your requirements is to ensure individual users have access only to the minimum amount of information required to do their jobs. You want to enforce this requirement with Google BigQuery.
Which three approaches can you take? (Choose three.)

Answer: A,C,E

Explanation:
bigquery.tables.create Create new tables.
bigquery.tables.delete Delete tables.
bigquery.tables.export Export table data out of BigQuery.
bigquery.tables.get Get table metadata.
To get table data, you need bigquery.tables.getData.
bigquery.tables.getData Get table data. This permission is required for querying table data.
To get table metadata, you need bigquery.tables.get.
bigquery.tables.list List tables and metadata on tables.
bigquery.tables.setCategory Set policy tags in table schema.
bigquery.tables.update
Update table metadata.
To update table data, you need bigquery.tables.updateData.
bigquery.tables.updateData
Update table data.
To update table metadata, you need bigquery.tables.update.


NEW QUESTION # 149
You are running a streaming pipeline with Dataflow and are using hopping windows to group the data as the data arrives. You noticed that some data is arriving late but is not being marked as late data, which is resulting in inaccurate aggregations downstream. You need to find a solution that allows you to capture the late data in the appropriate window. What should you do?

Answer: B

Explanation:
Watermarks are a way of tracking the progress of time in a streaming pipeline. They are used to determine when a window can be closed and the results emitted. Watermarks can be either event-time based or processing-time based. Event-time watermarks track the progress of time based on the timestamps of the data elements, while processing-time watermarks track the progress of time based on the system clock. Event-time watermarks are more accurate, but they require the data source to provide reliable timestamps. Processing-time watermarks are simpler, but they can be affected by system delays or backlogs.
By using watermarks, you can define the expected data arrival window for each windowing function. You can also specify how to handle late data, which is data that arrives after the watermark has passed. You can either discard late data, or allow late data and update the results as new data arrives. Allowing late data requires you to use triggers to control when the results are emitted.
In this case, using watermarks and allowing late data is the best solution to capture the late data in the appropriate window. Changing the windowing function to session windows or tumbling windows will not solve the problem of late data, as they still rely on watermarks to determine when to close the windows. Expanding the hopping window might reduce the amount of late data, but it will also change the semantics of the windowing function and the results.
Reference:
Streaming pipelines | Cloud Dataflow | Google Cloud
Windowing | Apache Beam


NEW QUESTION # 150
......

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