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| Section | Weight | Objectives |
|---|---|---|
| Data Warehouse and Lakehouse Architecture | 15-20% | - Lakehouse architecture principles
|
| Data Ingestion | 15-20% | - Batch ingestion methods
|
| Data Processing with Spark | 25-30% | - Python and SQL for data engineering
|
| Pipeline Development and Orchestration | 10-15% | - Databricks workflows
|
| Delta Lake | 20-25% | - Delta Lake fundamentals
|
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NEW QUESTION # 100
A data engineer is tasked with ensuring that a Delta table in Databricks continuously retains deleted files for
15 days (instead of the default 7 days), in order to permanently comply with the organization's data retention policy.
Which code snippet correctly sets this retention period for deleted files?
Answer: D
Explanation:
In Delta Lake , the property delta.deletedFileRetentionDuration controls how long deleted data files are retained before being permanently removed during a VACUUM operation.
* By default, this retention duration is set to 7 days .
* To comply with stricter retention requirements, organizations can explicitly update the table property using an ALTER TABLE statement.
Option A uses the correct SQL command:
ALTER TABLE my_table SET TBLPROPERTIES ( ' delta.deletedFileRetentionDuration ' = ' interval 15 days ' ) This updates the Delta table metadata so that all future operations respect the 15-day retention policy for deleted files.
Why not the others?
* B : This code incorrectly tries to set the property via the DeltaTable API. Delta's Python API does not expose direct attributes like deletedFileRetentionDuration; instead, properties must be set through ALTER TABLE or DataFrameWriter options.
* C : VACUUM ... RETAIN specifies a one-time file cleanup action (e.g., retaining 15 hours of history), not a persistent retention policy. It cannot be used to set a continuous retention duration .
* D : Setting spark.conf applies a session-level configuration and does not permanently update the table' s retention metadata. Once the session ends, this configuration is lost.
Therefore, Option A is the correct and documented approach for persistently enforcing a 15-day deleted file retention period in Delta Lake.
NEW QUESTION # 101
A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?
Answer: B
Explanation:
In Databricks, catalogs, schemas (or databases), and tables are managed through the Unity Catalog or Hive Metastore, depending on the environment. Permissions and ownership within these structures are governed by access control lists (ACLs).
* Catalog and Schema Ownership: When a platform engineer creates a catalog (such as catalog_A) and schema (such as schema_A), they automatically become the owner of those entities. This ownership gives them control over granting permissions for those entities (i.e., granting the USE CATALOG and USE SCHEMA privileges to others). However, ownership of the catalog or schema does not automatically extend to ownership or permission of individual tables within that schema.
* Table Permissions: For tables within a schema, the permission model is more granular. The table creator (i.e., whoever creates the table) is automatically assigned as the owner of that table. In this case, the platform engineer owns the schema but does not automatically inherit permissions to any table created within the schema unless explicitly granted by the table's owner or unless they grant permissions to themselves.
* Why the Engineer Lacks Access: The platform engineer notices that they do not have access to the underlying tables in schema_A despite being the owner of the schema. This occurs because the schema's ownership does not cascade to the tables. The engineer must either:
* Grant permissions to themselves for the tables in schema_A, or
* Be granted permissions by whoever created the tables within the schema.
* Resolution: As the owner of the schema, the platform engineer can easily grant themselves the required permissions (such as SELECT, INSERT, etc.) for the tables in the schema. This explains why the owner of a schema may not automatically have access to the tables and must take explicit steps to acquire those permissions.
References
* Databricks Unity Catalog Documentation: Manage Permissions
* [Databricks Permissions and Ownership (https://docs.databricks.com/security/access-control/workspace-acl.html#permissions
NEW QUESTION # 102
Which of the following techniques structured streaming uses to create an end-to-end fault toler-ance?
Answer: B
Explanation:
Explanation
The answer is Checkpointing and idempotent sinks
How does structured streaming achieves end to end fault tolerance:
*First, Structured Streaming uses checkpointing and write-ahead logs to record the offset range of data being processed during each trigger interval.
*Next, the streaming sinks are designed to be _idempotent_-that is, multiple writes of the same data (as identified by the offset) do not result in duplicates being written to the sink.
Taken together, replayable data sources and idempotent sinks allow Structured Streaming to en-sure end-to-end, exactly-once semantics under any failure condition.
NEW QUESTION # 103
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by thedatevariable:
Assume that the fieldscustomer_idandorder_idserve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?
Answer: B
Explanation:
Explanation
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation usingmergeInto. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.
NEW QUESTION # 104
A Delta Lake table with Change Data Feed (CDF) enabled in the Lakehouse named customer_churn_params is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources. The churn prediction model used by the ML team is fairly stable in production. The team is only interested in making predictions on records that have changed in the past 24 hours. Which approach would simplify the identification of these changed records?
Answer: A
Explanation:
Comprehensive and Detailed Explanation From Exact Extract:
* Exact extract: "Change data feed (CDF) provides row-level change information for Delta tables."
* Exact extract: "Use table_changes to query the set of rows that were inserted, updated, or deleted between two versions (or timestamps)." References: Delta Lake Change Data Feed; Delta Lake MERGE INTO.
NEW QUESTION # 105
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