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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Building and operationalizing data processing systems | 24% | - Data processing and transformation
|
| Topic 2: Ensuring solution quality | 28% | - Security and governance
|
| Topic 3: Operationalizing machine learning models | 26% | - ML pipeline integration
|
| Topic 4: Designing data processing systems | 22% | - Data architecture and storage design
|
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NEW QUESTION # 345
You are building a new application that you need to collect data from in a scalable way. Data arrives continuously from the application throughout the day, and you expect to generate approximately 150 GB of JSON data per day by the end of the year. Your requirements are:
* Decoupling producer from consumer
* Space and cost-efficient storage of the raw ingested data, which is to be stored indefinitely
* Near real-time SQL query
* Maintain at least 2 years of historical data, which will be queried with SQ Which pipeline should you use to meet these requirements?
Answer: B
NEW QUESTION # 346
Your team is building a data lake platform on Google Cloud. As a part of the data foundation design, you are planning to store all the raw data in Cloud Storage You are expecting to ingest approximately 25 GB of data a day and your billing department is worried about the increasing cost of storing old data. The current business requirements are:
* The old data can be deleted anytime
* You plan to use the visualization layer for current and historical reporting
* The old data should be available instantly when accessed
* There should not be any charges for data retrieval.
What should you do to optimize for cost?
Answer: C
Explanation:
- Autoclass automatically moves objects between storage classes without impacting performance or availability, nor incurring retrieval costs. - It continuously optimizes storage costs based on access patterns without the need to set specific lifecycle management policies.
NEW QUESTION # 347
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?
Answer: D
Explanation:
Explanation/Reference:
NEW QUESTION # 348
Your company uses Looker Studio connected to BigQuery for reporting. Users are experiencing slow dashboard load times due to complex queries on a large table. The queries involve aggregations and filtering on several columns. You need to optimize query performance to decrease the dashboard load times. What should you do?
Answer: D
Explanation:
The scenario describes slow performance caused by complex queries with aggregations and filtering on a large table. The best way to optimize this type of workload in BigQuery for dashboarding is to pre-compute the needed data.
* Materialized Views (MVs) are pre-computed views that cache the results of a query, including aggregations and filters. When a dashboard's query matches the MV's query (or a part of it), BigQuery can use the cached results, which is much faster than running the original complex query against the large raw table, directly improving dashboard load times. They are designed to improve performance and reduce costs for repeating, complex queries.
* Correcting other options:
* A (Shorter Refresh Interval): This would make the problem worse by triggering the slow, complex queries more frequently.
* C (Row-Level Security): This is a security measure, not primarily a performance optimization.
While it might slightly reduce the data scanned per user if the table is partitioned on the access column, it doesn't fundamentally speed up the complex aggregation and filtering logic which is the core problem.
* D (BigQuery BI Engine): BI Engine is an in-memory analysis service for BigQuery that accelerates many SQL queries, and it is a good general option for BI. However, creating a Materialized View specifically pre-calculates the exact aggregations and filters needed for the slow dashboard, which provides a more targeted and often more dramatic performance improvement for known, complex, and recurring queries than a general-purpose caching service.
The combination of MVs and BI Engine is a best practice, but the MV is the most targeted fix for pre-calculating the complex aggregations.
Reference: Google Cloud Documentation on Materialized Views:
"In BigQuery, materialized views are pre-computed views that cache a query's results, enhancing performance and efficiency... They periodically refresh to capture changes from the underlying base tables, allowing BigQuery to read only the updated data. Materialized views improve query performance by storing precomputed results, which reduces the need to process raw data repeatedly. This caching mechanism speeds up retrieval times, especially for complex queries." (3Source: Optimizing Query Performance with BigQuery Materialized Views)
"Smart tuning: BigQuery automatically rewrites queries to use materialized views whenever possible.
Automatic rewriting improves query performance and reduces costs without changing query results." (Source:
Use materialized views)
NEW QUESTION # 349
If a dataset contains rows with individual people and columns for year of birth, country, and income, how many of the columns are continuous and how many are categorical?
Answer: A
Explanation:
The columns can be grouped into two types-categorical and continuous columns:
A column is called categorical if its value can only be one of the categories in a finite set.
For example, the native country of a person (U.S., India, Japan, etc.) or the education level (high school, college, etc.) are categorical columns.
A column is called continuous if its value can be any numerical value in a continuous range. For example, the capital gain of a person (e.g. $14,084) is a continuous column.
Year of birth and income are continuous columns. Country is a categorical column.
You could use bucketization to turn year of birth and/or income into categorical features, but the raw columns are continuous.
Reference: https://www.tensorflow.org/tutorials/wide#reading_the_census_data
NEW QUESTION # 350
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