GCP-ADP Practice Test 2 — 10 Questions

Question 1

A team maintains a Looker dashboard fed by a BigQuery table that receives streaming inserts throughout the day. Users report that totals on the dashboard sometimes disagree with a report generated directly in BigQuery a few minutes later. What is the most likely explanation for the discrepancy?

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

Accepted Answer

Streaming inserts land in BigQuery's streaming buffer and are not immediately available to all query paths, so the dashboard and the direct report read slightly different snapshots.. Streaming ingestion does not write directly into fully queryable managed storage; rows first enter a buffer. Some query paths and export operations cannot see buffered rows, so a dashboard query and a report run moments apart can legitimately observe different row sets. The totals converge once the buffer commits, which is why the disagreement is small and transient rather than persistent.

Answer

BigQuery's on-demand query cache is returning an older result set to the dashboard instead of reading current data.

Answer

The dashboard's Looker model uses a different time zone setting than the BigQuery report, shifting some rows into adjacent days.

Answer

Looker is applying a symmetric aggregate that recalculates totals differently each time the dashboard is loaded.

Question 2

Your team uses Cloud Composer (Airflow) to run a daily ETL DAG. A task occasionally fails due to transient network errors when calling an external API. You want the task to retry automatically up to 3 times with a 5-minute delay between attempts, and you want the DAG to continue running other tasks even if this task ultimately fails. Which combination of Airflow task parameters should you configure?

Accepted Answer

Set retries=3, retry_delay=timedelta(minutes=5), and set the task's downstream tasks to use trigger_rule='all_done'.. To handle transient failures, configure retries and retry_delay on the task. To allow the DAG to continue despite a task failure, set the downstream tasks' trigger_rule to 'all_done', which triggers them regardless of upstream success or failure. This combination ensures retries occur and subsequent tasks are not blocked, meeting both requirements.

Answer

Set retries=3, retry_delay=timedelta(minutes=5), and set the task's trigger_rule='all_failed'.

Answer

Set retries=3, retry_delay=timedelta(minutes=5), and set the task's trigger_rule='all_success'.

Answer

Set retries=3, retry_delay=timedelta(minutes=5), and trigger_rule='all_done' on the task.

Question 3

You are using Cloud Scheduler to trigger a Cloud Workflows execution every hour. You need to ensure that the scheduler authenticates to the Workflows API securely. Which two of the following are valid methods for authenticating Cloud Scheduler to Workflows? (Choose two.)

Accepted Answer

Use an OAuth token generated for a service account that has the Workflows Invoker role.. Cloud Scheduler can authenticate to Workflows using either OIDC or OAuth tokens generated for a service account. The service account must have the Workflows Invoker role. These methods leverage Google Cloud IAM and avoid managing static secrets, making them secure and recommended. API keys and basic auth are not supported for the Workflows API.

Answer

Use an API key associated with the Workflows API and include it in the request header.

Answer

Use an OAuth 2.0 client ID and secret stored in Cloud Scheduler's configuration.

Answer

Use a static username and password stored in Secret Manager and referenced by Cloud Scheduler.

Question 4

Which THREE of the following are valid Airflow connection types in Cloud Composer?

Accepted Answer

HTTP. Option A (HTTP) is correct because Cloud Composer's Airflow connection types include 'HTTP', which stores host, schema, login, password, and port for calling REST endpoints via the HttpHook. Option D (Google Cloud) is correct because 'Google Cloud' is a built-in connection type used to store a GCP service account key or scopes, enabling operators like BigQueryInsertJobOperator and GCS hooks to authenticate. Option E (Postgres) is correct because 'Postgres' is a standard Airflow connection type that stores host, schema, login, password, and port for the PostgresHook and PostgresOperator. Option B (Kubernetes Pod) is not a connection type; KubernetesPodOperator uses a 'Kubernetes' connection (or in-cluster config), not 'Kubernetes Pod'. Option C (Python) is not a connection type; Python callables are executed via the PythonOperator, and no 'Python' connection exists in Airflow's connection type list.

Answer

Kubernetes Pod

Answer

Python

Question 5

You have sensitive data in Cloud Storage that needs to be deleted after 30 days to comply with regulatory requirements. What is the most efficient way to manage this?

Accepted Answer

Use Cloud Storage lifecycle management policies. Cloud Storage Object Lifecycle Management lets you define rules directly on a bucket (e.g., Delete objects 30 days after creation) that GCS enforces automatically at no additional cost. This is the native, server-side, most efficient mechanism for time-based deletion and requires no code, scheduler, or external service.

Answer

Run a manual BigQuery script

Answer

Write a Cloud Function to check dates and delete files

Answer

Create a Dataplex lifecycle task

Question 6

In Dataflow, what is the impact of using a high number of workers for a small dataset?

Accepted Answer

Increased cost and overhead. Dataflow provisions workers as a fixed pool sized by the number of workers you specify, and each worker incurs per-hour Compute Engine charges plus associated overhead (startup, shuffle, network). For a small dataset, the work is finished quickly regardless of parallelism, so extra workers add no throughput benefit while multiplying cost and adding coordination overhead. The correct answer is therefore increased cost and overhead, not faster processing.

Answer

Faster processing

Answer

Decreased latency

Answer

Data corruption

Question 7

Your team is ingesting streaming clickstream events from Pub/Sub into BigQuery. You need to ensure that the pipeline can handle duplicate message deliveries without creating duplicate rows in the destination table. What should you do?

Accepted Answer

Enable exactly-once delivery on the Pub/Sub subscription and use a Dataflow pipeline with the BigQuery Storage Write API configured for exactly-once semantics.. End-to-end duplicate elimination for Pub/Sub to BigQuery requires exactly-once delivery on the subscription and a sink that supports exactly-once writes. The BigQuery Storage Write API with exactly-once semantics, combined with Pub/Sub exactly-once delivery, ensures each message is processed and written once. Other options either provide best-effort deduplication or do not prevent duplicates.

Answer

Use the BigQuery streaming insert API with insertId populated from a unique event identifier, and rely on BigQuery to deduplicate rows.

Answer

Use Cloud Data Fusion to read from Pub/Sub and write to BigQuery with the deduplicate plugin enabled.

Answer

Configure the Pub/Sub subscription to use a dead-letter topic and set the maximum delivery attempts to 1.

Question 8

When creating a Looker visualization, which chart type is best for showing the relationship between two numerical variables?

Accepted Answer

Scatter plot.. A scatter plot is specifically designed to display the relationship between two numerical variables by plotting each data point as a dot on a two-dimensional Cartesian plane, with one variable on the x-axis and the other on the y-axis. This allows you to visually assess correlation, clustering, outliers, and patterns such as linear or non-linear relationships. In Looker, the scatter plot chart type automatically maps two measures to the axes, making it the ideal choice for bivariate numerical analysis.

Answer

Donut chart.

Answer

Table chart.

Answer

Area chart.

Question 9

Which service should you use to search for and discover datasets across your entire Google Cloud organization?

Accepted Answer

Data Catalog. Data Catalog is Google Cloud's fully managed, scalable metadata management service that enables search and discovery of datasets across the organization. It provides a unified inventory of data assets with tagging and search capabilities, making it the correct choice for discovering datasets organization-wide.

Answer

BigQuery Explorer

Answer

Cloud Storage Browser

Answer

Cloud Logging

Question 10

A data engineer needs to create a visualization in Looker Studio that shows the relationship between advertising spend and revenue for each marketing campaign. They want to quickly identify which campaigns have high spend but low revenue. Which chart type is most appropriate?

Accepted Answer

Scatter chart with advertising spend on the X-axis and revenue on the Y-axis, with each point representing a campaign.. To identify campaigns with high spend but low revenue, the analyst needs to see the correlation between the two metrics across campaigns. A scatter chart is designed for this purpose, plotting each campaign as a point based on its spend and revenue values. This visual format makes it easy to spot outliers that fall outside the expected pattern, such as those with high spend and low revenue.

Answer

Time series chart with both advertising spend and revenue as metrics over time.

Answer

Geo chart with campaign locations and revenue as the metric.

Answer

Bar chart with campaigns on the X-axis and revenue on the Y-axis, sorted by revenue descending.