QUIZ GOOGLE - THE BEST PROFESSIONAL-DATA-ENGINEER - GOOGLE CERTIFIED PROFESSIONAL DATA ENGINEER EXAM VCE DUMPS

Quiz Google - The Best Professional-Data-Engineer - Google Certified Professional Data Engineer Exam VCE Dumps

Quiz Google - The Best Professional-Data-Engineer - Google Certified Professional Data Engineer Exam VCE Dumps

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Google Certified Professional Data Engineer Exam Sample Questions (Q174-Q179):

NEW QUESTION # 174
Which of the following are examples of hyperparameters? (Select 2 answers.)

  • A. Number of hidden layers
  • B. Number of nodes in each hidden layer
  • C. Weights
  • D. Biases

Answer: A,B

Explanation:
If model parameters are variables that get adjusted by training with existing data, your hyperparameters are the variables about the training process itself. For example, part of setting up a deep neural network is deciding how many "hidden" layers of nodes to use between the input layer and the output layer, as well as how many nodes each layer should use. These variables are not directly related to the training data at all. They are configuration variables. Another difference is that parameters change during a training job, while the hyperparameters are usually constant during a job.
Weights and biases are variables that get adjusted during the training process, so they are not hyperparameters.
Reference: https://cloud.google.com/ml-engine/docs/hyperparameter-tuning-overview


NEW QUESTION # 175
You designed a database for patient records as a pilot project to cover a few hundred patients in three clinics.
Your design used a single database table to represent all patients and their visits, and you used self-joins to generate reports. The server resource utilization was at 50%. Since then, the scope of the project has expanded.
The database must now store 100 times more patient records. You can no longer run the reports, because they either take too long or they encounter errors with insufficient compute resources. How should you adjust the database design?

  • A. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
  • B. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
  • C. Add capacity (memory and disk space) to the database server by the order of 200.
  • D. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.

Answer: B


NEW QUESTION # 176
How would you query specific partitions in a BigQuery table?

  • A. Use DATE BETWEEN in the WHERE clause
  • B. Use the DAY column in the WHERE clause
  • C. Use the __PARTITIONTIME pseudo-column in the WHERE clause
  • D. Use the EXTRACT(DAY) clause

Answer: C

Explanation:
Partitioned tables include a pseudo column named _PARTITIONTIME that contains a date-based timestamp for data loaded into the table. To limit a query to particular partitions (such as Jan 1st and 2nd of 2017), use a clause similar to this:
WHERE _PARTITIONTIME BETWEEN TIMESTAMP('2017-01-01') AND TIMESTAMP('2017-01-02')


NEW QUESTION # 177
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 A
  • C. Option C
  • D. Option B.

Answer: B


NEW QUESTION # 178
Your company receives both batch- and stream-based event data. You want to process the data using Google Cloud Dataflow over a predictable time period. However, you realize that in some instances data can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is late or out of order?

  • A. Ensure every datasource type (stream or batch) has a timestamp, and use the timestamps to define the logic for lagged data.
  • B. Set a single global window to capture all the data.
  • C. Set sliding windows to capture all the lagged data.
  • D. Use watermarks and timestamps to capture the lagged data.

Answer: C


NEW QUESTION # 179
......

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