GCP-ADP Practice Test 17 — 15 Questions

Question 1

Your company stores sensitive customer records in a Cloud Storage bucket. A compliance officer requires that every object in the bucket be automatically classified and that any object containing credit card numbers be flagged for review. You want a managed Google Cloud service that samples and inspects the data without writing custom code. What should you use?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Cloud Data Loss Prevention (DLP) with a discovery scan configured on the Cloud Storage bucket.. Cloud DLP discovery scans are the managed way to sample and inspect data in Cloud Storage and produce findings when infoTypes such as credit card numbers are detected. This provides automatic classification and flagging without custom code. Audit logs, VPC Service Controls, and lifecycle rules operate on metadata, access, or storage class rather than content, so they cannot detect sensitive data inside objects.

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Cloud Audit Logs with a log-based metric that counts objects uploaded to the bucket.

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Cloud Storage Object Lifecycle Management rules that move objects to Coldline storage after 30 days.

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VPC Service Controls with an access policy that restricts the bucket to approved projects.

Question 2

You need to track lineage for a BigQuery table populated by a Dataflow pipeline. How does Dataplex help?

Accepted Answer

Dataplex captures it automatically for supported services. Dataplex automatically captures lineage for supported services, including BigQuery and Dataflow. When a Dataflow pipeline writes to a BigQuery table, Dataplex records the lineage information, showing the source and target. This automatic capture eliminates manual effort.

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Lineage is not supported in Dataplex

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You must use a third-party tool

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You must manually enter the lineage in the Data Catalog UI

Question 3

You want to enforce that all data in a specific Dataplex Zone must be encrypted with a Customer-Managed Encryption Key (CMEK). How do you enforce this?

Accepted Answer

Configure CMEK at the Lake/Zone creation level. In Dataplex, CMEK enforcement is configured at the Lake and Zone creation level. When you create a lake or zone, you can specify a CMEK (a Cloud KMS key) that will be used to encrypt all data assets within that lake or zone. This ensures that any data stored in the zone is encrypted with the customer-managed key, satisfying compliance requirements.

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There is no way to enforce CMEK in Dataplex

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Use a Dataflow job to re-encrypt

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Apply it via an IAM policy

Question 4

A data analyst is using Looker Studio to create a dashboard for a sales team. They want to enable users to interact with the data and explore different dimensions without leaving the dashboard. Which two features should they add to the report? (Choose two.)

Accepted Answer

A date range control that lets users choose a custom time period.. To allow users to interact with the dashboard and explore data, the analyst should add filter controls and date range controls. These are standard Looker Studio components that let viewers adjust the data displayed without editing the report. Filter controls for dimensions like region and date range controls for time periods directly empower users to customize their view, making the dashboard more interactive and useful for the sales team.

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A calculated field that computes profit margin for each sale.

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A scheduled email delivery of the report to the sales team.

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A data source with a parameter that allows switching between different tables.

Question 5

You are designing a Dataflow streaming pipeline that reads from Pub/Sub and writes to BigQuery. The pipeline must handle late-arriving data and you want to emit correct results for each 5-minute window even when events arrive up to 10 minutes late. You also want to minimize cost by not keeping windows open indefinitely. Which combination of Dataflow features should you use?

Accepted Answer

Use fixed windows with a 5-minute size, set an allowed lateness of 10 minutes, and use accumulation mode ACCUMULATING.. Fixed windows of 5 minutes provide the required periodic aggregation. Allowed lateness of 10 minutes allows the pipeline to accept events that arrive after the watermark, and ACCUMULATING mode ensures the re-emitted results include those late events. This combination gives correct per-window results while allowing state to be garbage-collected after the lateness horizon, which balances correctness and cost.

Answer

Use fixed windows with a 5-minute size and set an allowed lateness of 10 minutes with accumulation mode DISCARDING.

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Use session windows with a 5-minute gap duration and set an allowed lateness of 10 minutes with accumulation mode ACCUMULATING.

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Use sliding windows with a 5-minute size and a 1-minute period, and set an allowed lateness of 0 minutes.

Question 6

What is the primary function of a Looker Explore?

Accepted Answer

To allow users to explore and visualize data.. A Looker Explore is the primary interface where users drag and drop dimensions and measures to query data, apply filters, and visualize results. It provides a governed, self-service environment built on LookML models, allowing business users to explore data without writing SQL. The Explore is not a database backup, email client, or data deletion tool.

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To automatically delete old data.

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To act as a database backup.

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To act as an email client.

Question 7

What is the primary benefit of using partitioned tables in BigQuery?

Accepted Answer

Performance and cost reduction. Partitioned tables in BigQuery split data into segments based on a column (e.g., date, timestamp, integer range), so queries with filters on the partition column scan only relevant partitions. This dramatically reduces bytes processed, improving query performance and lowering on-demand costs, which are billed per byte scanned.

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Data security

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Increased storage capacity

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Data integrity

Question 8

You maintain a Cloud Composer 2 DAG that processes files as they land in Cloud Storage. Instead of polling, you want the DAG to start processing within seconds of a new object being created, and you want the trigger to be reliable even if the Composer environment is briefly restarting. Which approach should you use?

Accepted Answer

Create a Cloud Storage notification that publishes object events to a Pub/Sub topic, and run a sensor or triggerer-based component that starts the DAG when a message arrives.. Event-driven processing on Cloud Storage uses Pub/Sub notifications. Cloud Storage publishes object events to a topic, and a consumer in Cloud Composer starts the DAG when messages arrive. Pub/Sub retains messages during brief Composer restarts, so no events are lost. Polling schedules, Cloud Scheduler intervals, and lifecycle rules do not provide low-latency, durable, per-object triggering.

Answer

Schedule the DAG to run every minute with a short interval and use a GCSObjectExistenceSensor to detect new files.

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Configure an Object Lifecycle Management rule on the bucket that transitions new objects to Nearline storage, which signals the DAG to run.

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Use a Cloud Scheduler job that calls the Airflow REST API to trigger the DAG every five minutes.

Question 9

You need to provide temporary access to a GCS file for a third party. What should you use?

Accepted Answer

Signed URL. A signed URL provides temporary, time-limited access to a specific Cloud Storage object, granting the bearer permission to perform a specific action (e.g., GET) for a limited duration. This is ideal for sharing a file with a third party without granting them a Google account or IAM role. Signed URLs are generated using a service account key or by a user with sufficient permissions, and they include authentication information in the query string.

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Public access

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Bucket Policy

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IAM Role

Question 10

You are building a Workflows definition that calls a Cloud Run service and then passes the returned JSON payload to a Cloud Function. The Cloud Run response body is stored in a variable named result. Which syntax correctly passes that value as an argument in the subsequent call step?

Accepted Answer

args:
  body: ${result}. Workflows uses ${...} to embed variable and expression values into YAML fields. When a previous step's response is assigned to a variable such as result, referencing ${result} substitutes that parsed JSON object directly into the next step's arguments. Omitting the braces, adding quotes, or inventing a body subfield all prevent the actual payload from being passed correctly.

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args:
  body: ${"result"}

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args:
  body: $result

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args:
  body: ${result.body}

Question 11

Which of the following describes the purpose of 'BigQuery ML'?

Accepted Answer

To run machine learning models using SQL.. BigQuery ML enables users to create and execute machine learning models directly in BigQuery using SQL queries. It allows data analysts to build models without needing to export data or use separate ML frameworks. This makes it accessible for SQL users to perform predictive analytics.

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To create dashboard charts.

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To compress table data.

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To manage IAM permissions.

Question 12

You are analyzing a BigQuery table of e-commerce orders and want to identify the top 5 product categories by total revenue for each month in 2024. You need to produce a result set with columns `order_month`, `category`, and `total_revenue`. Which SQL technique should you use?

Accepted Answer

Use a GROUP BY on order_month and category, then a window function RANK() OVER (PARTITION BY order_month ORDER BY SUM(revenue) DESC) to filter top 5.. To get top N per group, you need to aggregate first, then rank within each group. A GROUP BY on month and category computes total revenue, and a window function like RANK() partitioned by month orders categories by revenue within each month. Filtering on the rank gives the top 5 per month. This is the correct and efficient pattern in BigQuery.

Answer

Use a CROSS JOIN with a calendar table and then filter with a WHERE clause on revenue.

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Use a GROUP BY on order_month and category, then a LIMIT 5 clause at the end of the query.

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Use the APPROX_TOP_COUNT function on category partitioned by month.

Question 13

You notice your Looker dashboard is showing duplicate data. What is the most likely cause?

Accepted Answer

Fan-out due to a join without correctly defined primary keys.. Duplicate data in a Looker dashboard is most commonly caused by fan-out, which occurs when a join is performed without correctly defined primary keys, leading to multiple rows for each original row. This results in inflated counts and duplicate records in the visualization. Ensuring proper primary key definitions in LookML prevents this.

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The data is cached in the browser.

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The user is logged in twice.

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The BigQuery table is corrupted.

Question 14

You need to load a large CSV file from Cloud Storage into BigQuery. The file has a header row and uses comma delimiters, but the data contains commas within quoted fields. Which BigQuery load option should you use to correctly parse the file?

Accepted Answer

Use the default CSV load with the appropriate schema and let BigQuery handle quoted fields. BigQuery's CSV parser natively supports quoted fields, so commas inside double quotes are treated as part of the field value. Providing a schema or using autodetect ensures correct column mapping. No special options are needed for this common CSV scenario.

Answer

Set the field delimiter to a pipe character (|)

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Set the Quote character to a single quote (')

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Enable the AllowQuotedNewlines option

Question 15

A user needs to see the distribution of values in a column in BigQuery. Which visualization approach is most effective for this?

Accepted Answer

Histogram.. A histogram is the most effective visualization for showing the distribution of values in a column because it groups continuous data into bins and displays frequency counts, revealing the shape, central tendency, and spread of the data. Line charts are for trends over time, pie charts for parts of a whole, and scatter plots for relationships between two variables. Thus, histogram is the correct choice for distribution analysis.

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Line chart.

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Pie chart.

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Scatter plot.