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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Designing data processing systems | 20% | - Data pipeline architecture design
|
| Topic 2: Building and operationalizing data processing systems | 30% | - Data ingestion and transformation
|
| Topic 3: Maintaining and optimizing data and ML solutions | 20% | - Machine learning integration
|
| Topic 4: Operationalizing data and ML pipelines | 30% | - Monitoring and troubleshooting
|
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NEW QUESTION # 29
When a Cloud Bigtable node fails, is lost.
Answer: A
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 # 30
Which of these is not a supported method of putting data into a partitioned table?
Answer: B
Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name. Reference: https://cloud.google.com/bigquery/docs/partitioned-tables
NEW QUESTION # 31
For the best possible performance, what is the recommended zone for your Compute Engine instance and Cloud Bigtable instance?
Answer: B
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
NEW QUESTION # 32
You need to choose a database for a new project that has the following requirements:
Fully managed
Able to automatically scale up
Transactionally consistent
Able to scale up to 6 TB
Able to be queried using SQL Which database do you choose?
Answer: A
NEW QUESTION # 33
Cloud Dataproc is a managed Apache Hadoop and Apache service.
Answer: D
Explanation:
Cloud Dataproc is a managed Apache Spark and Apache Hadoop service that lets you use open source data tools for batch processing, querying, streaming, and machine learning.
Reference: https://cloud.google.com/dataproc/docs/
NEW QUESTION # 34
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