Free Datadog Fundamentals Exam Braindumps
Datadog Fundamentals Questions 33–40 (page: 6)

Updated On: 8-Jul-2026

For which of the following technologies is a Datadog Agent-based integration available?

  1. Google Cloud
  2. NGINX
  3. Slack

Answer(s): B

Explanation:

Datadog provides an Agent-based integration for NGINX that runs on the host or container, allowing the Agent to collect metrics and performance data directly from the NGINX service.



A user wants to fetch all of their existing dashboard list definitions programmatically.
What authorization scope should be assigned to the endpoint “https://api.datadoghq.com/api/v1/dashboard/lists/manual”?

  1. dashboards_read
  2. user_access_read
  3. dashboards_public_share

Answer(s): A

Explanation:

Fetching existing dashboard list definitions requires read access to dashboards, which is provided by the dashboards_read authorization scope.



Which reserved tags are used as part of unified service tagging?

  1. host, source, env
  2. service, env, version
  3. source, version, device

Answer(s): B

Explanation:

Unified service tagging relies on the reserved tags service, env, and version to consistently identify and correlate telemetry data across metrics, traces, and logs for the same service.



Which of the following tag definitions is valid?

  1. namespace:kube-system-*
  2. 2_python_version:2.7.11
  3. image_name:nginx/ingress

Answer(s): C

Explanation:

Datadog tags use a key:value format where the key must start with a letter and can include underscores, and the value can include forward slashes, making this tag definition valid.



Which of the following strategies does Datadog recommend for tagging metrics from services running on multiple Datadog-supported cloud platforms?

  1. Create a small number of normalized tags applicable to all of the metrics.
  2. Install the proper cloud integration and allow it to tag metrics automatically
  3. Tag the metrics with source: cloud to enable aggregation

Answer(s): A

Explanation:

Datadog recommends using a small, normalized set of consistent tags so metrics from services running across different cloud platforms can be easily aggregated, filtered, and compared.



Authentication or “crawler” integrations differ from Datadog Agent integrations in which of the following ways?

  1. Unlike crawlers, the timing granularity of Agent integrations cannot be controlled: Agent checks run on pre-
    defined timing cycles only.
  2. Crawlers are configured directly in the Datadog application, and their execution and reporting cycles are not tied to an individual Agent.
    -C. Agent integrations are used only to monitor system and OS-level resources, and cannot access cloud resources.
    -

Answer(s): B

Explanation:

Crawler integrations are configured and executed directly within the Datadog platform, operate independently of any specific Agent, and collect data by authenticating to external services on a scheduled basis.



Which of the following is true about crawler-based integrations?

  1. They leverage Agents to synchronize metrics with Datadog.
  2. They are used to pull metrics into Datadog.
  3. They are used to push metrics to Datadog.

Answer(s): B

Explanation:

Crawler-based integrations authenticate to external services and periodically pull metrics from those services into Datadog without requiring a locally running Agent.



Which of the following steps is required to enable a Datadog crawler-based integration to authenticate with a cloud provider?

  1. Create a service account, identity, or role in the cloud provider.
  2. Install the Datadog Agent on each cloud instance.
  3. Configure the crawler to use the cloud provider’s SDK.

Answer(s): A

Explanation:

Crawler-based integrations require credentials or identities, such as a service account or role, to authenticate securely with the cloud provider and access the necessary APIs.



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