Databricks-DE-Pro Practice Test 7 — 10 Questions

Question 1

Refer to the exhibit. The engineer wants to replace only one specific partition in the 'orders' table. What is the best method in Databricks?

Accepted Answer

Enable dynamic partition overwrite mode and perform an INSERT OVERWRITE.. Using 'partitionOverwriteMode=dynamic' with a standard INSERT OVERWRITE operation is the correct way to replace a single partition without affecting the entire table. The default behavior is 'static', which overwrites the whole table. By setting the dynamic mode, the system identifies the partitions present in the incoming data and replaces only those, ensuring minimal impact and preventing accidental data loss across the entire dataset during the overwrite process.

Answer

Run a DELETE statement for the partition and then an APPEND.

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Use the MERGE command to overwrite only the records in the specific partition.

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Set 'spark.databricks.delta.retentionDurationCheck.enabled' to false.

Question 2

You are migrating a legacy ETL process to Delta Live Tables (DLT). You have an existing table defined with a complex transformation that involves a custom Python function using a third-party library. How should you structure this in DLT to ensure the function is available and correctly applied?

Accepted Answer

Define the function inside the same notebook but outside the 'dlt.table' function.. DLT pipelines require explicit management of dependencies. Using the 'dlt.table' decorator alongside proper library installation via cluster configuration or pip requirements ensures that Python functions are serialized and distributed to worker nodes. Understanding how DLT handles dependencies and decorators is critical for building reliable, production-grade pipelines that maintain parity with legacy logic while leveraging the automatic orchestration and quality management features inherent in the DLT framework.

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Hardcode the library path using 'sys.path.append'.

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You cannot use custom Python functions in DLT.

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Use the 'spark.udf.register' method globally.

Question 3

Refer to the exhibit. You are using Auto Loader to ingest data with evolving schemas. After running the job for a week, you realize that new columns added to the source JSON are not being captured in the destination table. What must you add to the configuration?

Accepted Answer

Add 'cloudFiles.schemaEvolutionMode': 'addCol'.. By default, Auto Loader schema inference only detects the schema during the initial load. To capture schema evolution, you must explicitly enable 'cloudFiles.schemaEvolutionMode'. Without this parameter, Auto Loader ignores new fields to protect downstream consumers from breaking changes. Enabling 'addCol' allows the schema to expand dynamically, ensuring the target Delta table reflects the structure of the incoming data files as they arrive.

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Add 'cloudFiles.schemaEvolutionMode': 'rescue'.

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Add 'cloudFiles.maxFiles': '1000'.

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Add 'cloudFiles.allowOverwrites': 'true'.

Question 4

When ingesting data using Auto Loader, what is the purpose of the 'cloudFiles.schemaLocation' parameter?

Accepted Answer

It stores metadata about the inferred schema and tracks evolution.. The schema location is vital for Auto Loader because it stores the inferred schema and tracks schema evolution history. By persisting this information in a managed location, Auto Loader can resume ingestion after a job restart without re-inferring the schema from scratch. This ensures consistency and prevents potential ingestion failures caused by schema drift when processing new files in a long-running stream.

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It specifies the target directory where the processed Delta table data is stored.

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It defines the temporary directory used for shuffling large datasets during joins.

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It is used to cache the raw JSON files before they are parsed.

Question 5

You are migrating a legacy CSV-based ETL process to Databricks. The source CSV files contain inconsistent date formats. Which approach provides the most scalable way to handle these inconsistencies during the bronze-to-silver transformation?

Accepted Answer

Use the 'to_timestamp' function with an array of acceptable formats to parse the column.. Using Spark's `to_timestamp` with multiple format strings or a custom UDF is the standard, scalable way to handle format drift. By applying this logic in the Silver layer, you preserve the raw data in the Bronze layer while ensuring the refined data is standardized. This strategy follows the Medallion architecture pattern, allowing for lineage tracking and the ability to reprocess data if requirements change or better parsing logic is developed.

Answer

Convert all columns to string type to avoid parsing errors, and handle formatting in the visualization tool.

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Use the 'from_unixtime' function with a hardcoded string format for all records.

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Delete all rows with invalid date formats using a 'drop' expectation in a DLT pipeline.

Question 6

A data engineer needs to restrict access to personally identifiable information (PII) columns in a Unity Catalog table for a group of analysts. Which Unity Catalog feature should be used to enforce this policy while ensuring data remains queryable?

Accepted Answer

Define a column mask using SQL functions to redact data for the analyst group.. Dynamic views or Row-level security (RLS) and Column-level security (CLS) are essential for compliance. By utilizing SQL functions like current_user() within a defined mask, you ensure that PII is obscured based on user identity. This approach centralizes security within the data platform, preventing the need to duplicate datasets for different access levels, which significantly reduces the administrative burden and minimizes the risk of unauthorized data exposure in production environments.

Answer

Apply a static mask using a custom UDF during data ingestion.

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Grant the analyst group SELECT access on the table and instruct them to cast PII columns to null.

Answer

Create separate physical tables for analysts containing only non-sensitive columns.

Question 7

A Data Engineer needs to verify that the column 'user_id' is unique in a critical Gold table. What is the most efficient, non-blocking way to perform this check in a production environment?

Accepted Answer

Define a primary key constraint in the Delta table definition.. Using a Delta constraint or a DLT expectation is the most efficient way to enforce uniqueness at the storage layer. Unlike a full table scan query, which is reactive and performance-intensive, native constraints are checked during the commit process, providing proactive quality assurance. This ensures that the table never enters an invalid state, which is vital for the integrity of downstream machine learning models and reporting applications.

Answer

Run a daily query: SELECT COUNT(user_id) - COUNT(DISTINCT user_id).

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Use a custom UDF to check for duplicates inside a Spark streaming loop.

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Perform a left-anti join with the previous day's data every time the pipeline runs.

Question 8

A Data Engineer is building a Delta Live Tables (DLT) pipeline to ingest raw JSON data. They need to ensure that records missing the required 'user_id' field are dropped while simultaneously capturing these discarded records in a separate table for auditing purposes. Which approach achieves this in DLT?

Accepted Answer

Define two separate tables in the pipeline, one filtering for valid records and one for invalid records using the NOT condition.. To handle data quality in DLT, the 'expect_violation_or_drop' constraint is not a standard clause. Instead, the 'expect_or_drop' constraint removes invalid records, but does not preserve them. The correct architectural pattern involves using a separate pipeline or query that filters for the inverse condition (where 'user_id' is null) and writes those records to an 'expect_all_fail' target table, maintaining strict lineage and auditing for schema non-compliance.

Answer

Apply the 'expect_or_drop' constraint and use a trigger to log dropped records.

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Use the 'expect_or_fail' constraint to halt the pipeline and flag the error.

Answer

Configure a DLT pipeline to use 'expect_all_drop' to automatically split the data stream.

Question 9

A Data Engineer needs to monitor the health of a Delta Live Tables (DLT) pipeline. Which metric should they monitor to track the number of data quality violations over time?

Accepted Answer

expectations_violated_records. The 'expectations_violated_records' metric is specifically designed to track data quality issues in DLT pipelines. By integrating these metrics with Databricks SQL alerts or external tools like Grafana, engineers can gain visibility into data lineage and quality degradation. Monitoring these metrics is essential for maintaining pipeline reliability and ensuring that downstream data consumers receive accurate and validated data, preventing the propagation of corrupted data throughout the analytical ecosystem.

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pipeline_latency_seconds

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cluster_cpu_utilization

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task_retry_count

Question 10

A data engineer is setting up a new Unity Catalog metastore. What is the primary purpose of the 'Metastore Admin' role?

Accepted Answer

To define the root storage location and manage top-level catalogs.. The Metastore Admin is the highest-privilege role in Unity Catalog, responsible for the initial configuration and top-level governance settings. This role is essential for establishing the security foundation of the platform, including catalog creation and cross-workspace access control. Proper management of this role is critical to prevent privilege escalation and ensure that only authorized personnel can define the organization's overall data governance strategy.

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To create and manage compute clusters for all users.

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To monitor and respond to daily system health alerts.

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To approve all requests for new user sign-ups.