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If you want to pass a high percentage of the Google Professional-Data-Engineer Exam, you should consider studying for the actual exam. These practice tests are designed to help you prepare for the exam and ensure you know the syllabus content. It will also help you improve your time management skills, as these tests are designed like an actual exam. Moreover, they will help you learn to answer all questions in the time allowed.

The Google Professional-Data-Engineer Exam covers a wide range of topics, including data processing systems design, data modeling, data ingestion, data transformation, data storage, data analysis, and machine learning. You will be tested on your ability to design and implement data processing systems using Google Cloud technologies such as BigQuery, Cloud Dataflow, Cloud Dataproc, and Cloud Pub/Sub. Professional-Data-Engineer exam also covers best practices for data security and compliance, as well as troubleshooting and optimization techniques. Passing Professional-Data-Engineer exam requires a strong understanding of cloud computing principles and a solid grasp of data engineering concepts, making it a challenging but rewarding certification to earn.

Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality

The following will be discussed here:

Ensuring privacy (e.g., Data Loss Prevention API)
Data staging, cataloging, and discovery
Choosing between ACID, idempotent, eventually consistent requirements
Building and running test suites
Pipeline monitoring (e.g., Stackdriver)
Designing for security and compliance
Performing data preparation and quality control (e.g., Cloud Dataprep)
Mapping to current and future business requirements
Designing for data and application portability (e.g., multi-cloud, data residency requirements)
Resizing and autoscaling resources

The benefit of obtaining the Google Professional Data Engineer Exam Certification

A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

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Google Certified Professional Data Engineer Exam Sample Questions (Q327-Q332):

NEW QUESTION # 327
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 wide table in BigQuery, create a column for the sample value at each second, and update the row with the interval for 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 table in BigQuery, and append the new samples for CPU and memory to the table
D. 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

Answer: D

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 # 328
You are administering shared BigQuery datasets that contain views used by multiple teams in your organization. The marketing team is concerned about the variability of their monthly BigQuery analytics spend using the on-demand billing model. You need to help the marketing team establish a consistent BigQuery analytics spend each month. What should you do?

A. Establish a BigQuery quota for the marketing team, and limit the maximum number of bytes scanned each day.
B. Create a BigQuery Standard pay-as-you go reservation with a baseline of 0 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly.
C. Create a BigQuery Enterprise reservation with a baseline of 250 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly.
D. Create a BigQuery reservation with a baseline of 500 slots with no autoscaling for the marketing team, and bill them back accordingly.

Answer: D

Explanation:
To help the marketing team establish a consistent BigQuery analytics spend each month, you can use BigQuery reservations to allocate dedicated slots for their queries. This provides predictable costs by reserving a fixed amount of compute resources.
BigQuery Reservations:
BigQuery Reservations allow you to purchase dedicated query processing capacity in the form of slots.
By reserving slots, you can control costs and ensure that the marketing team has the necessary resources for their queries without unexpected increases in spending.
Baseline Slots:
Setting a baseline of 500 slots without autoscaling ensures a consistent allocation of resources.
This provides a predictable monthly cost, as the marketing team will be billed for the reserved slots regardless of actual usage.
Billing Back:
The marketing team's usage can be billed back based on the fixed reservation cost, ensuring budget predictability.
This approach avoids the variability associated with on-demand billing, where costs can fluctuate based on query volume and complexity.
No Autoscaling:
By not enabling autoscaling, you prevent additional costs from being incurred due to temporary increases in query demand.
This fixed reservation ensures that the marketing team only uses the allocated 500 slots, maintaining a consistent monthly spend.
Google Data Engineer Reference:
BigQuery Reservations Documentation
BigQuery Slot Reservations
Managing BigQuery Costs
Using a fixed reservation of 500 slots provides the marketing team with predictable costs and the necessary resources for their queries without unexpected billing variability.

NEW QUESTION # 329
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. Option D
B. Option C
C. Option A
D. Option B.

Answer: C

NEW QUESTION # 330
You have a data stored in BigQuery. The data in the BigQuery dataset must be highly available. You need to define a storage, backup, and recovery strategy of this data that minimizes cost. How should you configure the BigQuery table?

A. Set the BigQuery dataset to be regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.
B. Set the BigQuery dataset to be regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
C. Set the BigQuery dataset to be multi-regional. In the event of an emergency, use a point-in-time snapshot to recover the data.
D. Set the BigQuery dataset to be multi-regional. Create a scheduled query to make copies of the data to tables suffixed with the time of the backup. In the event of an emergency, use the backup copy of the table.

Answer: C

Explanation:
highly available = multi-regional:
https://cloud.google.com/bigquery/docs/locations
recovery strategy of this data that minimizes cost = point-in-time snapshot:
https://cloud.google.com/solutions/bigquery-data-warehouse#backup-and-recovery

NEW QUESTION # 331
You want to use a BigQuery table as a data sink. In which writing mode(s) can you use BigQuery as a sink?

A. Only batch
B. Both batch and streaming
C. BigQuery cannot be used as a sink
D. Only streaming

Answer: B

Explanation:
When you apply a BigQueryIO.Write transform in batch mode to write to a single table, Dataflow invokes a BigQuery load job. When you apply a BigQueryIO.Write transform in streaming mode or in batch mode using a function to specify the destination table, Dataflow uses BigQuery's streaming inserts Reference: https://cloud.google.com/dataflow/model/bigquery-io

NEW QUESTION # 332
......

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