Free Professional Data Engineer Exam Braindumps (page: 32)

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Which is not a valid reason for poor Cloud Bigtable performance?

  1. The workload isn't appropriate for Cloud Bigtable.
  2. The table's schema is not designed correctly.
  3. The Cloud Bigtable cluster has too many nodes.
  4. There are issues with the network connection.

Answer(s): C

Explanation:

The Cloud Bigtable cluster doesn't have enough nodes. If your Cloud Bigtable cluster is overloaded,

adding more nodes can improve performance. Use the monitoring tools to check whether the cluster is overloaded.


Reference:

https://cloud.google.com/bigtable/docs/performance



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

  1. Field promotion
  2. Randomization
  3. Salting
  4. Hashing

Answer(s): A

Explanation:

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



When you design a Google Cloud Bigtable schema it is recommended that you _________.

  1. Avoid schema designs that are based on NoSQL concepts
  2. Create schema designs that are based on a relational database design
  3. Avoid schema designs that require atomicity across rows
  4. Create schema designs that require atomicity across rows

Answer(s): C

Explanation:

All operations are atomic at the row level. For example, if you update two rows in a table, it's possible that one row will be updated successfully and the other update will fail. Avoid schema designs that require atomicity across rows.


Reference:

https://cloud.google.com/bigtable/docs/schema-design#row-keys



Which of the following is NOT a valid use case to select HDD (hard disk drives) as the storage for Google Cloud Bigtable?

  1. You expect to store at least 10 TB of data.
  2. You will mostly run batch workloads with scans and writes, rather than frequently executing random reads of a small number of rows.
  3. You need to integrate with Google BigQuery.
  4. You will not use the data to back a user-facing or latency-sensitive application.

Answer(s): C

Explanation:

For example, if you plan to store extensive historical data for a large number of remote-sensing devices and then use the data to generate daily reports, the cost savings for HDD storage may justify the performance tradeoff. On the other hand, if you plan to use the data to display a real-time dashboard, it probably would not make sense to use HDD storage--reads would be much more frequent in this case, and reads are much slower with HDD storage.


Reference:

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






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