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| Section | Weight | Objectives |
|---|---|---|
| Managing and optimizing solutions | 20%-25% | - Reliability and scalability
|
| Ensuring solution quality | 20%-25% | - Data quality management
|
| Designing data processing systems | 22%-27% | - Designing for data ingestion
|
| Building and operationalizing data processing systems | 28%-33% | - Operationalizing systems
|
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NEW QUESTION # 59
You are developing a software application using Google's Dataflow SDK, and want to use conditional, for loops and other complex programming structures to create a branching pipeline. Which component will be used for the data processing operation?
Answer: C
Explanation:
In Google Cloud, the Dataflow SDK provides a transform component. It is responsible for the data processing operation. You can use conditional, for loops, and other complex programming structure to create a branching pipeline.
Reference: https://cloud.google.com/dataflow/model/programming-model
NEW QUESTION # 60
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)?
Answer: B,D
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 # 61
Which of the following is NOT true about Dataflow pipelines?
Answer: C
Explanation:
Dataflow pipelines can also run on alternate runtimes like Spark and Flink, as they are built using the Apache Beam SDKs Reference: https://cloud.google.com/dataflow/
NEW QUESTION # 62
You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?
Answer: B
NEW QUESTION # 63
When a Cloud Bigtable node fails, is lost.
Answer: C
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
NEW QUESTION # 64
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