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The GAQM Databricks-Certified-Data-Engineer-Associate exam consists of 60 multiple-choice questions that cover a range of topics related to data engineering with Databricks. Databricks-Certified-Data-Engineer-Associate exam is designed to test the candidate's knowledge and skills in areas such as data ingestion, data transformation, data modeling, data integration, and data processing. Candidates who pass the exam will receive a certificate that validates their expertise in data engineering with Databricks and demonstrates their ability to work effectively with the platform. Databricks Certified Data Engineer Associate Exam certification is valuable for professionals working in data engineering roles, as it helps them to showcase their skills and expertise to potential employers and clients.

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Databricks Certified Data Engineer Associate Exam Sample Questions (Q17-Q22):

NEW QUESTION # 17
Which of the following Git operations must be performed outside of Databricks Repos?

A. Clone
B. Push
C. Pull
D. Merge
E. Commit

Answer: D

Explanation:
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as commit, pull, push, branch management, and visual comparison of diffs when committing1. However, merge is not supported in the Git dialog2. You need to use the Repos UI or your Git provider to merge branches3. Merge is a way to combine the commit history from one branch into another branch1. During a merge, a merge conflict is encountered when Git cannot automatically combine code from one branch into another. Merge conflicts require manual resolution before a merge can be completed1. Reference: 4: Run Git operations on Databricks Repos4, 1: CI/CD techniques with Git and Databricks Repos1, 3: Collaborate in Repos3, 2: Databricks Repos - What it is and how we can use it2.
Databricks Repos is a visual Git client and API in Databricks that supports common Git operations such as commit, pull, push, merge, and branch management. However, to clone a remote Git repository to a Databricks repo, you must use the Databricks UI or API. You cannot clone a Git repo using the CLI through a cluster's web terminal, as the files won't display in the Databricks UI1. Reference: 1: Run Git operations on Databricks Repos | Databricks on AWS2

NEW QUESTION # 18
A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?

A. Clone
B. Merge
C. Pull
D. Push
E. Commit

Answer: C

Explanation:
To sync a Databricks Repo with the changes from a central Git repository, the data engineer needs to run the Git pull operation. This operation fetches the latest updates from the remote repository and merges them with the local repository. The data engineer can use the Pull button in the Databricks Repos UI, or use the git pull command in a terminal session. The other options are not relevant for this task, as they either push changes to the remote repository (Push), combine two branches (Merge), save changes to the local repository (Commit), or create a new local repository from a remote one (Clone). Reference:
Run Git operations on Databricks Repos
Git pull

NEW QUESTION # 19
A data engineer is running code in a Databricks Repo that is cloned from a central Git repository. A colleague of the data engineer informs them that changes have been made and synced to the central Git repository. The data engineer now needs to sync their Databricks Repo to get the changes from the central Git repository.
Which of the following Git operations does the data engineer need to run to accomplish this task?

A. Clone
B. Merge
C. Pull
D. Push
E. Commit

Answer: C

NEW QUESTION # 20
Which method should a Data Engineer apply to ensure Workflows are being triggered on schedule?

A. Scheduled Workflows can reduce resource consumption and expense since the cluster runs only long enough to execute the pipeline.
B. Scheduled Workflows process data as it arrives at configured sources.
C. Scheduled Workflows run continuously until manually stopped.
D. Scheduled Workflows require an always-running cluster, which is more expensive but reduces processing latency.

Answer: A

NEW QUESTION # 21
A data engineer has developed a data pipeline to ingest data from a JSON source using Auto Loader, but the engineer has not provided any type inference or schema hints in their pipeline. Upon reviewing the data, the data engineer has noticed that all of the columns in the target table are of the string type despite some of the fields only including float or boolean values.
Which of the following describes why Auto Loader inferred all of the columns to be of the string type?

A. All of the fields had at least one null value
B. There was a type mismatch between the specific schema and the inferred schema
C. Auto Loader cannot infer the schema of ingested data
D. Auto Loader only works with string data
E. JSON data is a text-based format

Answer: E

Explanation:
JSON data is a text-based format that represents data as a collection of name-value pairs. By default, when Auto Loader infers the schema of JSON data, it treats all columns as strings. This is because JSON data can have varying data types for the same column across different files or records, and Auto Loader does not attempt to reconcile these differences. For example, a column named "age" may have integer values in some files, but string values in others. To avoid data loss or errors, Auto Loader infers the column as a string type.
However, Auto Loader also provides an option to infer more precise column types based on the sample data.
This option is called cloudFiles.inferColumnTypes and it can be set to true or false. When set to true, Auto Loader tries to infer the exact data types of the columns, such as integers, floats, booleans, or nested structures.
When set to false, Auto Loader infers all columns as strings. The default value of this option is false. References: Configure schema inference and evolution in Auto Loader, Schema inference with auto loader (non-DLT and DLT), Using and Abusing Auto Loader's Inferred Schema, Explicit path to data or a defined schema required for Auto loader.

NEW QUESTION # 22
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