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To be eligible for the exam, candidates should have a minimum of three years of experience in data engineering, as well as a thorough understanding of the Google Cloud Platform. They should also have hands-on experience in designing and implementing data processing systems using various Google Cloud tools and services, such as BigQuery, Cloud Dataflow, Cloud Storage, and Cloud Pub/Sub.
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This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:
NEW QUESTION # 20
You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity `Movie' the property `actors' and the property `tags' have multiple values but the property `date released' does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?
Answer: B
NEW QUESTION # 21
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 # 22
Suppose you have a table that includes a nested column called "city" inside a column called "person", but when you try to submit the following query in BigQuery, it gives you an error. SELECT person FROM
`project1.example.table1` WHERE city = "London" How would you correct the error?
Answer: B
Explanation:
To access the person.city column, you need to "UNNEST(person)" and JOIN it to table1 using a comma.
Reference:
https://cloud.google.com/bigquery/docs/reference/standard-sql/migrating-from-legacy- sql#nested_repeated_results
NEW QUESTION # 23
What are two methods that can be used to denormalize tables in BigQuery?
Answer: A
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.
NEW QUESTION # 24
Your team is working on a binary classification problem. You have trained a support vector machine (SVM) classifier with default parameters, and received an area under the Curve (AUC) of 0.87 on the validation set.
You want to increase the AUC of the model. What should you do?
Answer: D
NEW QUESTION # 25
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