GCP-ADP Practice Test 1 — 10 Questions

Question 1

You have a query that processes 500GB of data. You want to reduce the cost of this query. What is the most effective approach?

Accepted Answer

Add a WHERE clause to filter by the partitioned column.. BigQuery charges based on the amount of data processed by a query. Adding a WHERE clause that filters on a partitioned column allows BigQuery to prune partitions and scan only the relevant data, drastically reducing the bytes processed and thus the cost. This is the most effective and direct cost-reduction technique.

Answer

Use a smaller machine type for the project.

Answer

Increase the query timeout limit.

Answer

Switch to BigQuery Omni.

Question 2

Which THREE of the following are valid GCS storage classes?

Accepted Answer

Nearline. Option B (Nearline) is a valid GCS storage class designed for data accessed less than once per month, offering lower storage cost with a 30-day minimum storage duration. Option D (Coldline) is also valid, intended for data accessed less than once per quarter, with a 90-day minimum storage duration and retrieval costs. Option E (Standard) is the default GCS storage class for frequently accessed ("hot") data with no minimum storage duration and the highest availability. Options A (Warmline) and C (Hotline) are not real GCS storage classes; they are fabricated names that do not correspond to any Google Cloud Storage offering.

Answer

Warmline

Answer

Hotline

Question 3

A user is running a query on a BigQuery table with nested data (JSON). How do you access a field named 'city' inside a record field named 'address'?

Accepted Answer

address.city. BigQuery uses Standard SQL, which accesses nested RECORD fields using dot notation. If 'address' is a RECORD (STRUCT) column containing a 'city' field, you reference it as address.city. This is the canonical syntax for traversing STRUCT types in BigQuery.

Answer

address->city

Answer

address[city]

Answer

JSON_EXTRACT(address, '$.city')

Question 4

You are designing a data lake in Cloud Storage and need to ensure that analysts can query the data using BigQuery without moving or copying the data. You also need to enforce fine-grained access control at the column level for sensitive fields. Which two actions should you take? (Choose two.)

Accepted Answer

Create an external table in BigQuery that points to the Cloud Storage data.. To query Cloud Storage data in BigQuery without moving it, you create an external table. To enforce column-level access control, you apply policy tags to the columns of that external table. Policy tags are the native BigQuery feature for column-level security and work with external tables, meeting both requirements.

Answer

Create a BigQuery dataset and use authorized views to restrict access to specific columns.

Answer

Enable uniform bucket-level access on the Cloud Storage bucket.

Answer

Set up a Dataplex lake and zone, and register the Cloud Storage bucket as an asset.

Question 5

Which service should you use to ingest streaming data from mobile devices?

Accepted Answer

Pub/Sub. Pub/Sub is designed for real-time messaging and ingestion of streaming data from various sources, including mobile devices. It decouples producers from consumers, allowing mobile apps to publish events to topics that downstream services can subscribe to. This makes it ideal for high-throughput, low-latency streaming ingestion. Other options are storage or analytics services, not ingestion services.

Answer

Cloud Storage

Answer

BigQuery

Answer

Cloud SQL

Question 6

Which service allows you to view the schema and metadata of a BigQuery table that has been registered in Dataplex?

Accepted Answer

Data Catalog. Data Catalog is the metadata management service in Google Cloud that indexes and exposes schema, column names, descriptions, tags, and other metadata for BigQuery tables (and other assets) registered in Dataplex. Once a BigQuery table is registered as a Dataplex asset, its technical metadata is surfaced through Data Catalog, which is the intended UI/API for schema and metadata discovery.

Answer

Cloud Storage Console

Answer

Cloud Build

Answer

Cloud IAM console

Question 7

You need to analyze a petabyte-scale dataset in BigQuery. To optimize cost, you must ensure that users only query relevant partitions. How should you enforce this?

Accepted Answer

Enable the 'Require partition filter' option in the table settings.. BigQuery's 'Require partition filter' table option forces every query against the table to include a WHERE clause that filters on the partitioning column, preventing full-table scans and the associated on-demand costs. This is the native, enforced mechanism for ensuring users only query relevant partitions. It applies to partitioned tables and rejects queries that lack a partition predicate.

Answer

Use a materialized view for all queries.

Answer

Apply a data masking policy to the partitioning column.

Answer

Set a project-level query quota.

Question 8

How do you restrict access to a specific GCS bucket to only members of a specific team?

Accepted Answer

Apply an IAM policy to the bucket. Bucket-level IAM policies are the native GCS mechanism for granting access to specific principals. By binding roles such as roles/storage.objectViewer to a Google Group representing the team, only members of that group inherit access to the bucket. This enforces least privilege at the resource boundary without affecting other buckets in the project.

Answer

Use file-level permissions

Answer

Use project-level roles only

Answer

Use public access

Question 9

You manage a BigQuery dataset that contains a table with millions of rows. Analysts frequently query only the most recent 30 days of data, but full-table scans are expensive. You need to reduce query costs and improve performance for these time-based queries with minimal changes to the table structure. What should you do?

Accepted Answer

Partition the table by a DATE or TIMESTAMP column using daily partitioning.. Partitioning a BigQuery table by a date or timestamp column enables partition pruning, so queries that filter on that column scan only the relevant partitions. This directly reduces the bytes processed and improves performance for time-based queries. Other options either do not reduce scanned data for arbitrary filters or add unnecessary complexity without addressing the root cause.

Answer

Create a separate table for each month and use a UNION ALL view to combine them.

Answer

Create a materialized view that selects the last 30 days of data and instruct analysts to query the view.

Answer

Enable the BigQuery BI Engine reservation on the dataset.

Question 10

You have a Looker Studio report connected to a BigQuery table that contains millions of rows. The report takes a long time to load and often times out. You need to improve performance without changing the underlying data. What should you do?

Accepted Answer

Enable BigQuery BI Engine and set up a reservation for the dataset used by the report.. BI Engine provides an in-memory cache that accelerates queries from Looker Studio, reducing latency and preventing timeouts. It integrates seamlessly with BigQuery and requires no data duplication. The other options either do not improve performance or introduce impractical workarounds that fail with large datasets.

Answer

Set the Looker Studio report to use 'Extract data' with a refresh schedule of every 15 minutes.

Answer

Increase the number of Looker Studio data sources by adding duplicate connections to the same table.

Answer

Schedule a daily extract of the BigQuery table into a Google Sheet and connect Looker Studio to the Sheet.