Free Professional Data Engineer Exam Braindumps (page: 13)

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You are deploying a new storage system for your mobile application, which is a media
streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity `Movie' the property `actors' and the property `tags' have multiple values but the property `date released' does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

  1. Option A
  2. Option
  3. Option C
  4. Option D

Answer(s): A

You are choosing a NoSQL database to handle telemetry data submitted from millions of Internet-of-Things (IoT) devices. The volume of data is growing at 100 TB per year, and each data entry has about 100 attributes. The data processing pipeline does not require atomicity, consistency, isolation, and durability (ACID). However, high availability and low latency are required.

You need to analyze the data by querying against individual fields.
Which three databases meet your requirements? (Choose three.)

  1. Redis
  2. HBase
  3. MySQL
  4. MongoDB
  5. Cassandra
  6. HDFS with Hive

Answer(s): B,D,F

You work for an economic consulting firm that helps companies identify economic trends as they happen. As part of your analysis, you use Google BigQuery to correlate customer data with the average prices of the 100 most common goods sold, including bread, gasoline, milk, and others. The average prices of these goods are updated every 30 minutes. You want to make sure this data stays up to date so you can combine it with other data in BigQuery as cheaply as possible.
What should you do?

  1. Load the data every 30 minutes into a new partitioned table in BigQuery.
  2. Store and update the data in a regional Google Cloud Storage bucket and create a federated data source in BigQuery
  3. Store the data in Google Cloud Datastore. Use Google Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Cloud Datastore
  4. Store the data in a file in a regional Google Cloud Storage bucket. Use Cloud Dataflow to query BigQuery and combine the data programmatically with the data stored in Google Cloud Storage.

Answer(s): A

Your company has recently grown rapidly and now ingesting data at a significantly higher rate than it was previously. You manage the daily batch MapReduce analytics jobs in Apache Hadoop. However, the recent increase in data has meant the batch jobs are falling behind. You were asked to recommend ways the development team could increase the responsiveness of the analytics without increasing costs.
What should you recommend they do?

  1. Rewrite the job in Pig.
  2. Rewrite the job in Apache Spark.
  3. Increase the size of the Hadoop cluster.
  4. Decrease the size of the Hadoop cluster but also rewrite the job in Hive.

Answer(s): A

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madhan 6/16/2023 6:22:08 AM
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