GCP-ADP Data Analysis And Presentation Practice Test 4 — 15 Questions

Question 1

In BigQuery, what does the 'Data Preview' tab show?

Accepted Answer

A sample of the table data.. The Data Preview tab in BigQuery shows a sample of the table data. It allows you to quickly inspect the first few rows of a table without running a query, which is useful for understanding the data structure and content.

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The edit history of the table.

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The billing usage for that table.

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The full schema definition.

Question 2

Which service is the primary tool for managing and running Jupyter notebooks in a managed environment within Google Cloud?

Accepted Answer

Vertex AI Workbench.. Vertex AI Workbench is Google Cloud's managed Jupyter notebook environment, offering both user-managed and fully managed notebook instances integrated with Vertex AI, BigQuery, and Dataproc. It is the designated service for running JupyterLab notebooks in a managed, scalable way on GCP.

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Cloud Run.

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Dataflow.

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BigQuery Studio.

Question 3

When reviewing a Looker dashboard, which icon do you click to reload the data?

Accepted Answer

The refresh icon.. The refresh icon in Looker is specifically designed to reload the data displayed on a dashboard or tile. Clicking it triggers a re-execution of the underlying queries, fetching the latest data from the database. This is the standard way to update the dashboard with current information without altering its configuration.

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The edit icon.

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The share icon.

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The filter icon.

Question 4

In Looker, what is the impact of defining a 'primary_key' in a LookML view?

Accepted Answer

It helps Looker avoid fan-outs during joins.. Defining a primary_key in a LookML view tells Looker that the field uniquely identifies each row in that view. During joins, Looker uses this information to determine the join relationship and can avoid fan-out (row duplication) by ensuring that the join is a many-to-one or one-to-one relationship. This prevents incorrect aggregation and improves query performance by allowing Looker to use symmetric aggregates.

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It forces the database to create an index.

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It changes the data type to an integer.

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It restricts users from filtering by that column.

Question 5

You are using Vertex AI Workbench. You want to install a new Python library, `seaborn`, for visualization. What is the correct way to install it in the notebook instance?

Accepted Answer

!pip install seaborn. In a Vertex AI Workbench notebook (JupyterLab-based), the correct way to install a Python package is to run a shell command from a notebook cell using the `!` prefix, so `!pip install seaborn` executes pip in the notebook's kernel environment. This installs the library into the instance's Python environment so it can be imported in subsequent cells.

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Use the Google Cloud Console library menu.

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import seaborn

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apt-get install seaborn

Question 6

You want to share a BigQuery dataset with a partner who uses a different Google Cloud project. What is the most secure way to grant them access?

Accepted Answer

Grant the partner's service account the 'BigQuery Data Viewer' role on the dataset.. Granting the partner's service account the BigQuery Data Viewer role on the dataset is the most secure approach because it follows the principle of least privilege: the partner can read data in that specific dataset only, using their own identity, with no credential sharing. IAM roles can be granted at the dataset level, scoping access precisely and allowing easy revocation.

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Make the dataset public.

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Provide the partner with your project's service account key.

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Export the data to a public Cloud Storage bucket.

Question 7

You need to export query results from BigQuery to a Google Sheet. What is the easiest way to do this?

Accepted Answer

Use the 'Save results' option and select 'Google Sheets'.. The BigQuery web console provides a 'Save results' button directly in the query results pane, offering 'Google Sheets' as a destination. This creates a new Sheet with the query output, making it the simplest, no-code method for exporting results to Sheets.

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Use the BigQuery Data Transfer Service.

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Export to a CSV file and manually upload to Sheets.

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Write a Python script to call the Google Sheets API.

Question 8

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.

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Use a smaller machine type for the project.

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Increase the query timeout limit.

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Switch to BigQuery Omni.

Question 9

A data analyst is working in a Vertex AI Workbench notebook. They want to connect to a private BigQuery dataset. How should they authenticate?

Accepted Answer

The instance's attached service account will handle authentication automatically.. Vertex AI Workbench instances are associated with a service account that provides credentials to Google Cloud APIs. When the notebook calls BigQuery, the client library uses the instance's attached service account automatically via the metadata server, so no manual credentials are needed. This is the secure and recommended authentication method.

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Enter your username and password in every query.

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Hardcode the service account key in the notebook.

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Use the 'gcloud auth login' command in every cell.

Question 10

You are creating a Looker report with a 'measure' that calculates the total revenue. You notice it is summing correctly but the format is wrong. Where do you change the format?

Accepted Answer

In the LookML view file using 'value_format_name'.. In Looker, a measure's display format is defined in the LookML view file using the `value_format_name` parameter (or `value_format` for custom formats). This parameter controls how the numeric value is rendered in the UI without altering the underlying calculation. Since the measure is already summing correctly, only the presentation layer needs adjustment, which is exactly what `value_format_name` does.

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In the dashboard filter settings.

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By editing the underlying SQL.

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In the 'User' settings.

Question 11

Which BigQuery feature allows you to monitor query usage and identify expensive queries?

Accepted Answer

INFORMATION_SCHEMA.JOBS. INFORMATION_SCHEMA.JOBS is a system view in BigQuery that provides real-time and historical metadata about all jobs (queries, loads, extracts, copies) run in a project, including details like total bytes processed, slot milliseconds, user, and query text. This makes it the ideal tool to monitor query usage and pinpoint expensive queries by analyzing metrics such as total_bytes_billed or total_slot_ms. Unlike other options, it offers granular, query-level cost and performance data directly within BigQuery.

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Billing Reports.

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Cloud Logging.

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BigQuery Monitoring Dashboard.

Question 12

Which TWO of the following are valid ways to visualize BigQuery data within the Google Cloud ecosystem?

Accepted Answer

Looker Studio. Looker Studio (option C) is a valid visualization tool because it natively connects to BigQuery as a data source, letting you build interactive dashboards and reports directly from BigQuery tables or custom SQL queries. Looker (option E) is also correct since it is Google Cloud's enterprise BI platform that uses LookML models to query BigQuery and render dashboards, charts, and embedded analytics. The other options are not visualization tools: Cloud Storage (A) is an object storage service, Pub/Sub (B) is an asynchronous messaging service, and Bigtable (D) is a NoSQL wide-column database, none of which provide data visualization capabilities.

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Cloud Storage

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Pub/Sub

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Bigtable

Question 13

You are building a Looker model and want to create a measure that calculates the count of distinct customers. Which type of measure should you use?

Accepted Answer

type: count_distinct. Looker's measure type 'count_distinct' computes the number of unique values in a given dimension or column, which is exactly what is needed to count distinct customers. It is the standard LookML measure type for distinct counts.

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type: count

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type: average_distinct

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type: sum_distinct

Question 14

You are querying a dataset in BigQuery that uses partitioning. Your query doesn't use the partitioning column. What is the consequence?

Accepted Answer

The query will perform a full table scan.. When a query filters or joins on a non-partitioning column, BigQuery cannot prune partitions and must scan every partition in the table, resulting in a full table scan. This increases bytes processed, cost, and query latency, but the query still returns correct results.

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BigQuery will automatically optimize the scan.

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The query will fail immediately.

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The query results will be inaccurate.

Question 15

Which TWO actions can help reduce the time it takes to build a Jupyter notebook in Vertex AI Workbench?

Accepted Answer

Scaling up the machine type for the instance. Option A is correct because scaling up the machine type for the Vertex AI Workbench instance allocates more vCPUs and RAM, which directly speeds up compute-intensive notebook build and execution tasks such as model training or data processing. Option D is correct because using custom container images with pre-installed packages eliminates the time spent installing dependencies at notebook startup, reducing overall build time. Option B is incorrect because the project name has no effect on notebook build performance. Option C is incorrect because disabling the persistent disk would remove storage needed for the notebook environment and data, not improve build speed. Option E is incorrect because a single-core machine provides less compute capacity and would slow down, not reduce, build time.

Answer

Change the project name

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Disable the persistent disk

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Using a single-core machine