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2022 Professional-Data-Engineer證照資訊 & Professional-Data-Engineer認證資料 – Google Certified Professional Data Engineer Exam考試資料

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NEW QUESTION 23
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. Normalize the master patient-record table into the patient table and the visits table, and create other necessary tables to avoid self-join.
  • B. Shard the tables into smaller ones based on date ranges, and only generate reports with prespecified date ranges.
  • C. Partition the table into smaller tables, with one for each clinic. Run queries against the smaller table pairs, and use unions for consolidated reports.
  • D. Add capacity (memory and disk space) to the database server by the order of 200.

Answer: A

Explanation:
It provides the least amount of inconvenience over using pre-specified date ranges or one table per clinic while also increasing performance due to avoiding self-joins.

 

NEW QUESTION 24
You have spent a few days loading data from comma-separated values (CSV) files into the Google BigQuery table CLICK_STREAM. The column DTstores the epoch time of click events. For convenience, you chose a simple schema where every field is treated as the STRINGtype. Now, you want to compute web session durations of users who visit your site, and you want to change its data type to the TIMESTAMP. You want to minimize the migration effort without making future queries computationally expensive. What should you do?

  • A. Delete the table CLICK_STREAM, and then re-create it such that the column DTis of the TIMESTAMP type. Reload the data.
  • B. Add a column TSof the TIMESTAMPtype to the table CLICK_STREAM, and populate the numeric values from the column TSfor each row. Reference the column TSinstead of the column DTfrom now on.
  • C. Create a view CLICK_STREAM_V, where strings from the column DTare cast into TIMESTAMPvalues.
    Reference the view CLICK_STREAM_Vinstead of the table CLICK_STREAMfrom now on.
  • D. Add two columns to the table CLICK STREAM: TSof the TIMESTAMPtype and IS_NEWof the BOOLEANtype. Reload all data in append mode. For each appended row, set the value of IS_NEWto true. For future queries, reference the column TSinstead of the column DT, with the WHEREclause ensuring that the value of IS_NEWmust be true.
  • E. Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DTinto TIMESTAMPvalues. Run the query into a destination table NEW_CLICK_STREAM, in which the column TSis the TIMESTAMPtype. Reference the table NEW_CLICK_STREAMinstead of the table CLICK_STREAMfrom now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.

Answer: D

 

NEW QUESTION 25
You are building a model to predict whether or not it will rain on a given day. You have thousands of input features and want to see if you can improve training speed by removing some features while having a minimum effect on model accuracy. What can you do?

  • A. Combine highly co-dependent features into one representative feature.
  • B. Eliminate features that are highly correlated to the output labels.
  • C. Remove the features that have null values for more than 50% of the training records.
  • D. Instead of feeding in each feature individually, average their values in batches of 3.

Answer: A

 

NEW QUESTION 26
Which TensorFlow function can you use to configure a categorical column if you don’t know all of the possible values for that column?

  • A. categorical_column_with_unknown_values
  • B. categorical_column_with_vocabulary_list
  • C. sparse_column_with_keys
  • D. categorical_column_with_hash_bucket

Answer: D

Explanation:
If you know the set of all possible feature values of a column and there are only a few of them, you can use categorical_column_with_vocabulary_list. Each key in the list will get assigned an auto-incremental ID starting from 0.
What if we don’t know the set of possible values in advance? Not a problem. We can use categorical_column_with_hash_bucket instead. What will happen is that each possible value in the feature column occupation will be hashed to an integer ID as we encounter them in training.
Reference: https://www.tensorflow.org/tutorials/wide

 

NEW QUESTION 27
……

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