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Google Google Cloud Data Engineer Professional Exam Questions
Google Cloud Data Engineer Professional (Page 4 )

Updated On: 16-Mar-2026
Page 4 of 78
PDF with 384 Questions

You are designing a basket abandonment system for an ecommerce company. The system will send a message to a user based on these rules:

No interaction by the user on the site for 1 hour

Has added more than $30 worth of products to the basket

Has not completed a transaction

You use Google Cloud Dataflow to process the data and decide if a message should be sent. How should you design the pipeline?

  1. Use a fixed-time window with a duration of 60 minutes.
  2. Use a sliding time window with a duration of 60 minutes.
  3. Use a session window with a gap time duration of 60 minutes.
  4. Use a global window with a time based trigger with a delay of 60 minutes.

Answer(s): C



Your company handles data processing for a number of different clients. Each client prefers to use their own suite of analytics tools, with some allowing direct query access via Google BigQuery. You need to secure the data so that clients cannot see each other's dat

  1. You want to ensure appropriate access to the data.
    Which three steps should you take? (Choose three.)
  2. Load data into different partitions.
  3. Load data into a different dataset for each client.
  4. Put each client's BigQuery dataset into a different table.
  5. Restrict a client's dataset to approved users.
  6. Only allow a service account to access the datasets.
  7. Use the appropriate identity and access management (IAM) roles for each client's users.

Answer(s): B,D,F



You want to process payment transactions in a point-of-sale application that will run on Google Cloud Platform. Your user base could grow exponentially, but you do not want to manage infrastructure scaling.

Which Google database service should you use?

  1. Cloud SQL
  2. BigQuery
  3. Cloud Bigtable
  4. Cloud Datastore

Answer(s): A



You want to use a database of information about tissue samples to classify future tissue samples as either normal or mutated. You are evaluating an unsupervised anomaly detection method for classifying the tissue samples.
Which two characteristic support this method? (Choose two.)

  1. There are very few occurrences of mutations relative to normal samples.
  2. There are roughly equal occurrences of both normal and mutated samples in the database.
  3. You expect future mutations to have different features from the mutated samples in the database.
  4. You expect future mutations to have similar features to the mutated samples in the database.
  5. You already have labels for which samples are mutated and which are normal in the database.

Answer(s): A,D

Explanation:

Unsupervised anomaly detection techniques detect anomalies in an unlabeled test data set under the assumption that the majority of the instances in the data set are normal by looking for instances that seem to fit least to the remainder of the data set.
https://en.wikipedia.org/wiki/Anomaly_detection



You need to store and analyze social media postings in Google BigQuery at a rate of 10,000 messages per minute in near real-time. Initially, design the application to use streaming inserts for individual postings. Your application also performs data aggregations right after the streaming inserts. You discover that the queries after streaming inserts do not exhibit strong consistency, and reports from the queries might miss in-flight dat

  1. How can you adjust your application design?
  2. Re-write the application to load accumulated data every 2 minutes.
  3. Convert the streaming insert code to batch load for individual messages.
  4. Load the original message to Google Cloud SQL, and export the table every hour to BigQuery via streaming inserts.
  5. Estimate the average latency for data availability after streaming inserts, and always run queries after waiting twice as long.

Answer(s): D

Explanation:

The data is first comes to buffer and then written to Storage. If we are running queries in buffer we will face above mentioned issues. If we wait for the bigquery to write the data to storage then we won't face the issue. So We need to wait till it's written tio storage



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