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Exam Professional-Data-Engineer Experience | Professional-Data-Engineer Exam Blueprint

Exam Professional-Data-Engineer Experience | Professional-Data-Engineer Exam Blueprint

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Google Certified Professional Data Engineer Exam Sample Questions (Q127-Q132):

NEW QUESTION # 127
Which of the following statements about Legacy SQL and Standard SQL is not true?

  • A. Standard SQL is the preferred query language for BigQuery.
  • B. If you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
  • C. You need to set a query language for each dataset and the default is Standard SQL.
  • D. One difference between the two query languages is how you specify fully-qualified table names (i.e. table names that include their associated project name).

Answer: C

Explanation:
You do not set a query language for each dataset. It is set each time you run a query and the default query language is Legacy SQL.
Standard SQL has been the preferred query language since BigQuery 2.0 was released.
In legacy SQL, to query a table with a project-qualified name, you use a colon, :, as a separator. In standard SQL, you use a period, ., instead.
Due to the differences in syntax between the two query languages (such as with project- qualified table names), if you write a query in Legacy SQL, it might generate an error if you try to run it with Standard SQL.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy-sql


NEW QUESTION # 128
You are creating a new pipeline in Google Cloud to stream IoT data from Cloud Pub/Sub through Cloud Dataflow to BigQuery. While previewing the data, you notice that roughly 2% of the data appears to be corrupt. You need to modify the Cloud Dataflow pipeline to filter out this corrupt dat
a. What should you do?

  • A. Add a ParDo transform in Cloud Dataflow to discard corrupt elements.
  • B. Add a SideInput that returns a Boolean if the element is corrupt.
  • C. Add a Partition transform in Cloud Dataflow to separate valid data from corrupt data.
  • D. Add a GroupByKey transform in Cloud Dataflow to group all of the valid data together and discard the rest.

Answer: A


NEW QUESTION # 129
You work for a shipping company that uses handheld scanners to read shipping labels. Your company has strict data privacy standards that require scanners to only transmit recipients' personally identifiable information (PII) to analytics systems, which violates user privacy rules. You want to quickly build a scalable solution using cloud-native managed services to prevent exposure of PII to the analytics systems. What should you do?

  • A. Build a Cloud Function that reads the topics and makes a call to the Cloud Data Loss Prevention API. Use the tagging and confidence levels to either pass or quarantine the data in a bucket for review.
  • B. Install a third-party data validation tool on Compute Engine virtual machines to check the incoming data for sensitive information.
  • C. Use Stackdriver logging to analyze the data passed through the total pipeline to identify transactions that may contain sensitive information.
  • D. Create an authorized view in BigQuery to restrict access to tables with sensitive data.

Answer: A


NEW QUESTION # 130
You've migrated a Hadoop job from an on-prem cluster to dataproc and GCS. Your Spark job is a complicated analytical workload that consists of many shuffing operations and initial data are parquet files (on average 200-400 MB size each). You see some degradation in performance after the migration to Dataproc, so you'd like to optimize for it. You need to keep in mind that your organization is very cost-sensitive, so you'd like to continue using Dataproc on preemptibles (with 2 non-preemptible workers only) for this workload.
What should you do?

  • A. Switch from HDDs to SSDs, copy initial data from GCS to HDFS, run the Spark job and copy results back to GCS.
  • B. Switch from HDDs to SSDs, override the preemptible VMs configuration to increase the boot disk size.
  • C. Increase the size of your parquet files to ensure them to be 1 GB minimum.
  • D. Switch to TFRecords formats (appr. 200MB per file) instead of parquet files.

Answer: C


NEW QUESTION # 131
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, JdbcIO, side-outputs
  • B. Streaming job, PubSubIO, BigQueryIO, side-inputs
  • C. Streaming job, PubSubIO, BigQueryIO, side-outputs
  • D. Batch job, PubSubIO, side-inputs

Answer: B


NEW QUESTION # 132
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