Free Professional Data Engineer Exam Braindumps (page: 17)

Page 17 of 68

Cloud Bigtable is a recommended option for storing very large amounts of ____________________________?

  1. multi-keyed data with very high latency
  2. multi-keyed data with very low latency
  3. single-keyed data with very low latency
  4. single-keyed data with very high latency

Answer(s): C

Explanation:

Cloud Bigtable is a sparsely populated table that can scale to billions of rows and thousands of columns, allowing you to store terabytes or even petabytes of data. A single value in each row is indexed; this value is known as the row key. Cloud Bigtable is ideal for storing very large amounts of single-keyed data with very low latency. It supports high read and write throughput at low latency, and it is an ideal data source for MapReduce operations.


Reference:

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



By default, which of the following windowing behavior does Dataflow apply to unbounded data sets?

  1. Windows at every 100 MB of data
  2. Single, Global Window
  3. Windows at every 1 minute
  4. Windows at every 10 minutes

Answer(s): B

Explanation:

Dataflow's default windowing behavior is to assign all elements of a PCollection to a single, global window, even for unbounded PCollections


Reference:

https://cloud.google.com/dataflow/model/pcollection



Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?

  1. Preemptible workers cannot use persistent disk.
  2. Preemptible workers cannot store data.
  3. If a preemptible worker is reclaimed, then a replacement worker must be added manually.
  4. A Dataproc cluster cannot have only preemptible workers.

Answer(s): B,D

Explanation:

The following rules will apply when you use preemptible workers with a Cloud Dataproc cluster:
Processing only--Since preemptibles can be reclaimed at any time, preemptible workers do not store data. Preemptibles added to a Cloud Dataproc cluster only function as processing nodes.
No preemptible-only clusters--To ensure clusters do not lose all workers, Cloud Dataproc
cannot create preemptible-only clusters.
Persistent disk size--As a default, all preemptible workers are created with the smaller of 100GB or the primary worker boot disk size. This disk space is used for local caching of data and is not available through HDFS.
The managed group automatically re-adds workers lost due to reclamation as capacity permits.


Reference:

https://cloud.google.com/dataproc/docs/concepts/preemptible-vms



All Google Cloud Bigtable client requests go through a front-end server ______ they are sent to a Cloud Bigtable node.

  1. before
  2. after
  3. only if
  4. once

Answer(s): A

Explanation:

In a Cloud Bigtable architecture all client requests go through a front-end server before they are sent to a Cloud Bigtable node.
The nodes are organized into a Cloud Bigtable cluster, which belongs to a Cloud Bigtable instance, which is a container for the cluster. Each node in the cluster handles a subset of the requests to the cluster.
When additional nodes are added to a cluster, you can increase the number of simultaneous requests that the cluster can handle, as well as the maximum throughput for the entire cluster.


Reference:

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



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