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Design Data Processing Systems
NEW QUESTION # 137
You receive data files in CSV format monthly from a third party. You need to cleanse this data, but every third month the schema of the files changes. Your requirements for implementing these transformations include:
Executing the transformations on a schedule
Enabling non-developer analysts to modify transformations
Providing a graphical tool for designing transformations
What should you do?
Answer: C
Explanation:
you can use dataprep for continuously changing target schema
In general, a target consists of the set of information required to define the expected data in a dataset. Often referred to as a "schema," this target schema information can include:
Names of columns
Order of columns
Column data types
Data type format
Example rows of data
A dataset associated with a target is expected to conform to the requirements of the schema. Where there are differences between target schema and dataset schema, a validation indicator (or schema tag) is displayed.
https://cloud.google.com/dataprep/docs/html/Overview-of-RapidTarget_136155049
NEW QUESTION # 138
You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this data. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? (Choose two.)
Answer: D,E
NEW QUESTION # 139
You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket, which contains a large number of files. You notice that queries against this table are slow. You want to improve the performance of these queries What should you do?
Answer: B
Explanation:
BigLake is a Google Cloud service that allows you to query structured data in external data stores such as Cloud Storage, Amazon S3, and Azure Blob Storage with access delegation and governance. BigLake tables extend the capabilities of BigQuery to data lakes and enable a flexible, open lakehouse architecture. By upgrading an external table to a BigLake table, you can improve the performance of your queries by leveraging the BigQuery storage API, which supports data format conversion, predicate pushdown, column projection, and metadata caching. Metadata caching reduces the number of requests to the external data store and speeds up query execution. To upgrade an external table to a BigLake table, you can use the ALTER TABLE statement with the SET OPTIONS clause and specify the enable_metadata_caching option as true. For example:
SQL
ALTER TABLE hive_partitioned_data
SET OPTIONS (
enable_metadata_caching=true
);
AI-generated code. Review and use carefully. More info on FAQ.
Reference:
Introduction to BigLake tables
Upgrade an external table to BigLake
BigQuery storage API
NEW QUESTION # 140
You are using BigQuery with a regional dataset that includes a table with the daily sales volumes. This table is updated multiple times per day. You need to protect your sales table in case of regional failures with a recovery point objective (RPO) of less than 24 hours, while keeping costs to a minimum. What should you do?
Answer: C
Explanation:
To apply complex business logic on a JSON response using Python's standard library within a Workflow, invoking a Cloud Function is the most efficient and straightforward approach. Here's why option A is the best choice:
Cloud Functions:
Cloud Functions provide a lightweight, serverless execution environment for running code in response to events. They support Python and can easily integrate with Workflows.
This approach ensures simplicity and speed of execution, as Cloud Functions can be invoked directly from a Workflow and handle the complex logic required.
Flexibility and Simplicity:
Using Cloud Functions allows you to leverage Python's extensive standard library and ecosystem, making it easier to implement and maintain the complex business logic.
Cloud Functions abstract the underlying infrastructure, allowing you to focus on the application logic without worrying about server management.
Performance:
Cloud Functions are optimized for fast execution and can handle the processing of the JSON response efficiently.
They are designed to scale automatically based on demand, ensuring that your workflow remains performant.
Steps to Implement:
Write the Cloud Function:
Develop a Cloud Function in Python that processes the JSON response and applies the necessary business logic.
Deploy the function to Google Cloud.
Invoke Cloud Function from Workflow:
Modify your Workflow to call the Cloud Function using an HTTP request or Google Cloud Function connector.
steps:
- callCloudFunction:
call: http.post
args:
url: https://REGION-PROJECT_ID.cloudfunctions.net/FUNCTION_NAME
body:
key: value
Process Results:
Handle the response from the Cloud Function and proceed with the next steps in the Workflow, such as loading data into BigQuery.
Reference:
Google Cloud Functions Documentation
Using Workflows with Cloud Functions
Workflows Standard Library
NEW QUESTION # 141
Which Java SDK class can you use to run your Dataflow programs locally?
Answer: C
Explanation:
DirectPipelineRunner allows you to execute operations in the pipeline directly, without any optimization.
Useful for small local execution and tests
Reference:
https://cloud.google.com/dataflow/java-sdk/JavaDoc/com/google/cloud/dataflow/sdk/runners/DirectPipelineRun
NEW QUESTION # 142
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