Google Google Certifications PROFESSIONAL-DATA-ENGINEER Questions & Answers

• Question 11:

  You have a job that you want to cancel. It is a streaming pipeline, and you want to ensure that any data that is in-flight is processed and written to the output. Which of the following commands can you use on the Dataflow monitoring console to stop the pipeline job?

  A. Cancel

  B. Drain

  C. Stop

  D. Finish

  Correct Answer: B

  Using the Drain option to stop your job tells the Dataflow service to finish your job in its current state. Your job will immediately stop ingesting new data from input sources, but the Dataflow service will preserve any existing resources (such as worker instances) to finish processing and writing any buffered data in your pipeline. Reference: https://cloud.google.com/dataflow/pipelines/stopping-a-pipeline

• Question 12:

  Which of these statements about BigQuery caching is true?

  A. By default, a query's results are not cached.

  B. BigQuery caches query results for 48 hours.

  C. Query results are cached even if you specify a destination table.

  D. There is no charge for a query that retrieves its results from cache.

  Correct Answer: D

  When query results are retrieved from a cached results table, you are not charged for the query.

  BigQuery caches query results for 24 hours, not 48 hours. Query results are not cached if you specify a destination table. A query's results are always cached except under certain conditions, such as if you specify a destination table.

  Reference: https://cloud.google.com/bigquery/querying-data#query-caching

• Question 13:

  Which of the following is not true about Dataflow pipelines?

  A. Pipelines are a set of operations

  B. Pipelines represent a data processing job

  C. Pipelines represent a directed graph of steps

  D. Pipelines can share data between instances

  Correct Answer: D

  The data and transforms in a pipeline are unique to, and owned by, that pipeline. While your program can create multiple pipelines, pipelines cannot share data or transforms Reference: https://cloud.google.com/dataflow/model/pipelines

• Question 14:

  Which Java SDK class can you use to run your Dataflow programs locally?

  A. LocalRunner

  B. DirectPipelineRunner

  C. MachineRunner

  D. LocalPipelineRunner

  Correct Answer: B

  DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization. Useful for small local execution and tests Reference: https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRunner

• Question 15:

  Which Cloud Dataflow / Beam feature should you use to aggregate data in an unbounded data source every hour based on the time when the data entered the pipeline?

  A. An hourly watermark

  B. An event time trigger

  C. The with Allowed Lateness method

  D. A processing time trigger

  Correct Answer: D

  When collecting and grouping data into windows, Beam uses triggers to determine when to emit the aggregated results of each window. Processing time triggers. These triggers operate on the processing time ?the time when the data element is processed at any given stage in the pipeline. Event time triggers. These triggers operate on the event time, as indicated by the timestamp on each data element. Beam's default trigger is event time-based. Reference: https://beam.apache.org/documentation/programming-guide/#triggers

• Question 16:

  Which is the preferred method to use to avoid hotspotting in time series data in Bigtable?

  A. Field promotion

  B. Randomization

  C. Salting

  D. Hashing

  Correct Answer: A

  By default, prefer field promotion. Field promotion avoids hotspotting in almost all cases, and it tends to make it easier to design a row key that facilitates queries. Reference: https://cloud.google.com/bigtable/docs/schema-design-time- series#ensure_that_your_row_key_avoids_hotspotting

• Question 17:

  Dataproc clusters contain many configuration files. To update these files, you will need to use the --properties option. The format for the option is: file_prefix:property=_____.

  A. details

  B. value

  C. null

  D. id

  Correct Answer: B

  To make updating files and properties easy, the --properties command uses a special format to specify the configuration file and the property and value within the file that should be updated. The formatting is as follows:

  file_prefix:property=value.

  Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-properties#formatting

• Question 18:

  You are planning to use Google's Dataflow SDK to analyze customer data such as displayed below. Your project requirement is to extract only the customer name from the data source and then write to an output PCollection.

  Tom,555 X street

  Tim,553 Y street

  Sam, 111 Z street

  Which operation is best suited for the above data processing requirement?

  A. ParDo

  B. Sink API

  C. Source API

  D. Data extraction

  Correct Answer: A

  In Google Cloud dataflow SDK, you can use the ParDo to extract only a customer name of each element in your PCollection. Reference: https://cloud.google.com/dataflow/model/par-do

• Question 19:

  What is the recommended action to do in order to switch between SSD and HDD storage for your Google Cloud Bigtable instance?

  A. create a third instance and sync the data from the two storage types via batch jobs

  B. export the data from the existing instance and import the data into a new instance

  C. run parallel instances where one is HDD and the other is SDD

  D. the selection is final and you must resume using the same storage type

  Correct Answer: B

  When you create a Cloud Bigtable instance and cluster, your choice of SSD or HDD storage for the cluster is permanent. You cannot use the Google Cloud Platform Console to change the type of storage that is used for the cluster. If you need to convert an existing HDD cluster to SSD, or vice-versa, you can export the data from the existing instance and import the data into a new instance. Alternatively, you can write a Cloud Dataflow or Hadoop MapReduce job that copies

  the data from one instance to another.

  Reference: https://cloud.google.com/bigtable/docs/choosing-ssd-hdd?

• Question 20:

  Which of the following statements is NOT true regarding Bigtable access roles?

  A. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.

  B. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.

  C. You can configure access control only at the project level.

  D. To give a user access to only one table in a project, you must configure access through your application.

  Correct Answer: B

  For Cloud Bigtable, you can configure access control at the project level. For example, you can grant the ability to:

  Read from, but not write to, any table within the project. Read from and write to any table within the project, but not manage instances. Read from and write to any table within the project, and manage instances. Reference: https://

  cloud.google.com/bigtable/docs/access-control