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Free Professional Data Engineer Exam Braindumps (page: 31)

Page 30 of 95
PDF with 375 Questions

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

  1. Using IAM roles, you cannot give a user access to only one table in a project, rather than all tables in a project.
  2. To give a user access to only one table in a project, grant the user the Bigtable Editor role for that table.
  3. You can configure access control only at the project level.
  4. To give a user access to only one table in a project, you must configure access through your application.

Answer(s): B

Explanation:

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



For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?

  1. Have the Compute Engine instance in the furthest zone from the Cloud Bigtable instance.
  2. Have both the Compute Engine instance and the Cloud Bigtable instance to be in different zones.
  3. Have both the Compute Engine instance and the Cloud Bigtable instance to be in the same zone.
  4. Have the Cloud Bigtable instance to be in the same zone as all of the consumers of your data.

Answer(s): C

Explanation:

It is recommended to create your Compute Engine instance in the same zone as your Cloud Bigtable instance for the best possible performance,

If it's not possible to create a instance in the same zone, you should create your instance in another zone within the same region. For example, if your Cloud Bigtable instance is located in us-central1-b, you could create your instance in us-central1-f. This change may result in several milliseconds of additional latency for each Cloud Bigtable request.

It is recommended to avoid creating your Compute Engine instance in a different region from your Cloud Bigtable instance, which can add hundreds of milliseconds of latency to each Cloud Bigtable request.


Reference:

https://cloud.google.com/bigtable/docs/creating-compute-instance



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)?

  1. A sequential numeric ID
  2. A timestamp followed by a stock symbol
  3. A non-sequential numeric ID
  4. A stock symbol followed by a timestamp

Answer(s): A,B

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_hotspotting



When a Cloud Bigtable node fails, ____ is lost.

  1. all data
  2. no data
  3. the last transaction
  4. the time dimension

Answer(s): B

Explanation:

A Cloud Bigtable table is sharded into blocks of contiguous rows, called tablets, to help balance the workload of queries. Tablets are stored on Colossus, Google's file system, in SSTable format. Each tablet is associated with a specific Cloud Bigtable node.

Data is never stored in Cloud Bigtable nodes themselves; each node has pointers to a set of tablets that are stored on Colossus. As a result:

Rebalancing tablets from one node to another is very fast, because the actual data is not copied. Cloud Bigtable simply updates the pointers for each node.

Recovery from the failure of a Cloud Bigtable node is very fast, because only metadata needs to be migrated to the replacement node.

When a Cloud Bigtable node fails, no data is lost


Reference:

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






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