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| Section | Weight | Objectives |
|---|---|---|
| Operationalizing machine learning models | 26% | - ML pipeline integration
|
| Designing data processing systems | 22% | - Batch and streaming data processing design
|
| Building and operationalizing data processing systems | 24% | - Data ingestion and integration
|
| Ensuring solution quality | 28% | - Reliability and performance
|
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NEW QUESTION # 220
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old.
What should you do?
Answer: B
Explanation:
Explanation/Reference: https://support.google.com/datastudio/answer/7020039?hl=en
NEW QUESTION # 221
Which of these rules apply when you add preemptible workers to a Dataproc cluster (select 2 answers)?
Answer: A,C
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
NEW QUESTION # 222
You have several different unstructured data sources, within your on-premises data center as well as in the cloud. The data is in various formats, such as Apache Parquet and CSV. You want to centralize this data in Cloud Storage. You need to set up an object sink for your data that allows you to use your own encryption keys. You want to use a GUI-based solution. What should you do?
Answer: A
Explanation:
To centralize unstructured data from various sources into Cloud Storage using a GUI-based solution while allowing the use of your own encryption keys, Cloud Data Fusion is the most suitable option. Here's why:
* Cloud Data Fusion:
* Cloud Data Fusionis a fully managed, cloud-native data integration service that helps in building and managing ETL pipelines with a visual interface.
* It supports a wide range of data sources and formats, including Apache Parquet and CSV, and provides a user-friendly GUI for pipeline creation and management.
* Custom Encryption Keys:
* Cloud Data Fusion allows the use of customer-managed encryption keys (CMEK) for data encryption, ensuring that your data is securely stored according to your encryption policies.
* Centralizing Data:
* Cloud Data Fusion simplifies the process of moving data from on-premises and cloud sources into Cloud Storage, providing a centralized repository for your unstructured data.
Steps to Implement:
* Set Up Cloud Data Fusion:
* Deploy a Cloud Data Fusion instance and configure it to connect to your various data sources.
* Create ETL Pipelines:
* Use the GUI to create data pipelines that extract data from your sources and load it into Cloud Storage. Configure the pipelines to use your custom encryption keys.
* Run and Monitor Pipelines:
* Execute the pipelines and monitor their performance and data movement through the Cloud Data Fusion dashboard.
Reference Links:
* Cloud Data Fusion Documentation
* Using Customer-Managed Encryption Keys (CMEK)
NEW QUESTION # 223
What are two methods that can be used to denormalize tables in BigQuery?
Answer: D
Explanation:
Explanation
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information.
The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data
NEW QUESTION # 224
You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to BigQuery for analysis. Which job type and transforms should this pipeline use?
Answer: D
NEW QUESTION # 225
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