SIMULATED PROFESSIONAL-DATA-ENGINEER TEST - DUMP PROFESSIONAL-DATA-ENGINEER FILE

Simulated Professional-Data-Engineer Test - Dump Professional-Data-Engineer File

Simulated Professional-Data-Engineer Test - Dump Professional-Data-Engineer File

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Google Certified Professional Data Engineer Exam Sample Questions (Q376-Q381):

NEW QUESTION # 376
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?

  • A. Pig
  • B. YARN
  • C. Spark
  • D. Hive

Answer: A,C,D

Explanation:
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.


NEW QUESTION # 377
You decided to use Cloud Datastore to ingest vehicle telemetry data in real time. You want to build a storage system that will account for the long-term data growth, while keeping the costs low. You also want to create snapshots of the data periodically, so that you can make a point-in-time (PIT) recovery, or clone a copy of the data for Cloud Datastore in a different environment. You want to archive these snapshots for a long time.
Which two methods can accomplish this? Choose 2 answers.

  • A. Use managed export, and then import the data into a BigQuery table created just for that export, and delete temporary export files.
  • B. Write an application that uses Cloud Datastore client libraries to read all the entities. Format the exported data into a JSON file. Apply compression before storing the data in Cloud Source Repositories.
  • C. Use managed exportm, and then import to Cloud Datastore in a separate project under a unique namespace reserved for that export.
  • D. Use managed export, and store the data in a Cloud Storage bucket using Nearline or Coldline class.
  • E. Write an application that uses Cloud Datastore client libraries to read all the entities. Treat each entity as a BigQuery table row via BigQuery streaming insert. Assign an export timestamp for each export, and attach it as an extra column for each row. Make sure that the BigQuery table is partitioned using the export timestamp column.

Answer: A,B


NEW QUESTION # 378
Which row keys are likely to cause a disproportionate number of reads and/or writes on a particular node in a Bigtable cluster (select 2 answers)?

  • A. A sequential numeric ID
  • B. A stock symbol followed by a timestamp
  • C. A timestamp followed by a stock symbol
  • D. A non-sequential numeric ID

Answer: A,C

Explanation:
Explanation
using a timestamp as the first element of a row key can cause a variety of problems.
In brief, when a row key for a time series includes a timestamp, all of your writes will target a single node; fill that node; and then move onto the next node in the cluster, resulting in hotspotting.
Suppose your system assigns a numeric ID to each of your application's users. You might be tempted to use the user's numeric ID as the row key for your table. However, since new users are more likely to be active users, this approach is likely to push most of your traffic to a small number of nodes.
[https://cloud.google.com/bigtable/docs/schema-design]
Reference:
https://cloud.google.com/bigtable/docs/schema-design-time-series#ensure_that_your_row_key_avoids_hotspotti


NEW QUESTION # 379
If a dataset contains rows with individual people and columns for year of birth, country, and income, how many of the columns are continuous and how many are categorical?

  • A. 1 continuous and 2 categorical
  • B. 3 continuous
  • C. 2 continuous and 1 categorical
  • D. 3 categorical

Answer: C

Explanation:
The columns can be grouped into two types-categorical and continuous columns:
A column is called categorical if its value can only be one of the categories in a finite set. For example, the native country of a person (U.S., India, Japan, etc.) or the education level (high school, college, etc.) are categorical columns.
A column is called continuous if its value can be any numerical value in a continuous range. For example, the capital gain of a person (e.g. $14,084) is a continuous column.
Year of birth and income are continuous columns. Country is a categorical column.
You could use bucketization to turn year of birth and/or income into categorical features, but the raw columns are continuous.
Reference: https://www.tensorflow.org/tutorials/wide#reading_the_census_data


NEW QUESTION # 380
When using Cloud Dataproc clusters, you can access the YARN web interface by configuring a browser to connect through a ____ proxy.

  • A. SOCKS
  • B. HTTPS
  • C. VPN
  • D. HTTP

Answer: A

Explanation:
Explanation
When using Cloud Dataproc clusters, configure your browser to use the SOCKS proxy. The SOCKS proxy routes data intended for the Cloud Dataproc cluster through an SSH tunnel.
Reference: https://cloud.google.com/dataproc/docs/concepts/cluster-web-interfaces#interfaces


NEW QUESTION # 381
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