PROFESSIONAL-DATA-ENGINEER VALID TEST SYLLABUS - PROFESSIONAL-DATA-ENGINEER CLEAR EXAM

Professional-Data-Engineer Valid Test Syllabus - Professional-Data-Engineer Clear Exam

Professional-Data-Engineer Valid Test Syllabus - Professional-Data-Engineer Clear Exam

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Google Professional-Data-Engineer Clear Exam - New Professional-Data-Engineer Practice Materials

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To prepare for the exam, prospective candidates can enroll in official GCP training courses or leverage various online resources such as practice exams, sample questions, and study materials. It's essential to have practical experience working with Google Cloud technologies to be able to pass the exam successfully. Professional-Data-Engineer Exam's duration is two hours, and the passing score is 70 percent. Once a candidate completes the exam and earns the certification, they gain access to exclusive professional networking opportunities and recognition as an industry expert in data engineering.

Google Certified Professional Data Engineer Exam Sample Questions (Q163-Q168):

NEW QUESTION # 163
You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to BigQuery for analysis. Which job type and transforms should this pipeline use?

  • A. Streaming job, PubSubIO, BigQueryIO, side-outputs
  • B. Streaming job, PubSubIO, BigQueryIO, side-inputs
  • C. Streaming job, PubSubIO, JdbcIO, side-outputs
  • D. Batch job, PubSubIO, side-inputs

Answer: B


NEW QUESTION # 164
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. Instead of feeding in each feature individually, average their values in batches of 3.
  • B. Remove the features that have null values for more than 50% of the training records.
  • C. Eliminate features that are highly correlated to the output labels.
  • D. Combine highly co-dependent features into one representative feature.

Answer: D


NEW QUESTION # 165
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. 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.
  • 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. 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.

Answer: D


NEW QUESTION # 166
You have a requirement to insert minute-resolution data from 50,000 sensors into a BigQuery table. You expect significant growth in data volume and need the data to be available within 1 minute of ingestion for real-time analysis of aggregated trends. What should you do?

  • A. Use bq load to load a batch of sensor data every 60 seconds.
  • B. Use the INSERT statement to insert a batch of data every 60 seconds.
  • C. Use the MERGE statement to apply updates in batch every 60 seconds.
  • D. Use a Cloud Dataflow pipeline to stream data into the BigQuery table.

Answer: D


NEW QUESTION # 167
Each analytics team in your organization is running BigQuery jobs in their own projects. You want to enable each team to monitor slot usage within their projects. What should you do?

  • A. Create a Stackdriver Monitoring dashboard based on the BigQuery metric slots/allocated_for_project
  • B. Create a Stackdriver Monitoring dashboard based on the BigQuery metric query/scanned_bytes
  • C. Create a log export for each project, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric
  • D. Create an aggregated log export at the organization level, capture the BigQuery job execution logs, create a custom metric based on the totalSlotMs, and create a Stackdriver Monitoring dashboard based on the custom metric

Answer: D


NEW QUESTION # 168
......

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