Accurate Databricks-Certified-Professional-Data-Engineer Study Material - Trusted Databricks-Certified-Professional-Data-Engineer Exam Resource

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q54-Q59):

NEW QUESTION # 54
A Delta Lake table was created with the below query:

Realizing that the original query had a typographical error, the below code was executed:
ALTER TABLE prod.sales_by_stor RENAME TO prod.sales_by_store
Which result will occur after running the second command?

A. A new Delta transaction log Is created for the renamed table.
B. The table name change is recorded in the Delta transaction log.
C. All related files and metadata are dropped and recreated in a single ACID transaction.
D. The table reference in the metastore is updated and no data is changed.
E. The table reference in the metastore is updated and all data files are moved.

Answer: D

Explanation:
The query uses the CREATE TABLE USING DELTA syntax to create a Delta Lake table from an existing Parquet file stored in DBFS. The query also uses the LOCATION keyword to specify the path to the Parquet file as /mnt/finance_eda_bucket/tx_sales.parquet. By using the LOCATION keyword, the query creates an external table, which is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created from an existing directory in a cloud storage system, such as DBFS or S3, that contains data files in a supported format, such as Parquet or CSV.
The result that will occur after running the second command is that the table reference in the metastore is updated and no data is changed. The metastore is a service that stores metadata about tables, such as their schema, location, properties, and partitions. The metastore allows users to access tables using SQL commands or Spark APIs without knowing their physical location or format. When renaming an external table using the ALTER TABLE RENAME TO command, only the table reference in the metastore is updated with the new name; no data files or directories are moved or changed in the storage system. The table will still point to the same location and use the same format as before. However, if renaming a managed table, which is a table whose metadata and data are both managed by Databricks, both the table reference in the metastore and the data files in the default warehouse directory are moved and renamed accordingly. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "ALTER TABLE RENAME TO" section; Databricks Documentation, under "Metastore" section; Databricks Documentation, under "Managed and external tables" section.

NEW QUESTION # 55
A new user who currently does not have access to the catalog or schema is requesting access to the customer table in sales schema, but the customer table contains sensitive information, so you have decided to create view on the table excluding columns that are sensitive and granted access to the view GRANT SELECT ON view_name to [email protected] but when the user tries to query the view, gets the error view does not exist. What is the issue preventing user to access the view and how to fix it?

A. User requires SELECT on the underlying table
B. User needs ADMIN privilege on the view
C. User requires to be put in a special group that has access to PII data
D. User requires USAGE privilege on Sales schema
E. User has to be the owner of the view

Answer: D

Explanation:
Explanation
The answer is User requires USAGE privilege on Sales schema,
Data object privileges - Azure Databricks | Microsoft Docs
GRANT USAGE ON SCHEMA sales TO [email protected];
*USAGE: does not give any abilities, but is an additional requirement to perform any action on a schema object.

NEW QUESTION # 56
A data engineer has written the following query:
1. SELECT *
2. FROM json.`/path/to/json/file.json`;
The data engineer asks a colleague for help to convert this query for use in a Delta Live Tables (DLT)
pipeline. The query should create the first table in the DLT pipeline.
Which of the following describes the change the colleague needs to make to the query?

A. They need to add a CREATE LIVE TABLE table_name AS line at the beginning of the query
B. They need to add a live. prefix prior to json. in the FROM line
C. They need to add the cloud_files(...) wrapper to the JSON file path
D. They need to add a COMMENT line at the beginning of the query
E. They need to add a CREATE DELTA LIVE TABLE table_name AS line at the beginning of the query

Answer: A

NEW QUESTION # 57
Why does AUTO LOADER require schema location?

A. Schema location is used to store schema inferred by AUTO LOADER
B. Schema location is used to store user provided schema
C. Schema location is used to identify the schema of target table
D. AUTO LOADER does not require schema location, because its supports Schema evolution
E. Schema location is used to identify the schema of target table and source table

Answer: A

Explanation:
Explanation
The answer is, Schema location is used to store schema inferred by AUTO LOADER, so the next time AUTO LOADER runs faster as does not need to infer the schema every single time by trying to use the last known schema.
Auto Loader samples the first 50 GB or 1000 files that it discovers, whichever limit is crossed first. To avoid incurring this inference cost at every stream start up, and to be able to provide a stable schema across stream restarts, you must set the option cloudFiles.schemaLocation. Auto Loader creates a hidden directory _schemas at this location to track schema changes to the input data over time.
The below link contains detailed documentation on different options
Auto Loader options | Databricks on AWS

NEW QUESTION # 58
A table in the Lakehouse namedcustomer_churn_paramsis 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?

A. Calculate the difference between the previous model predictions and the current customer_churn_params on a key identifying unique customers before making new predictions; only make predictions on those customers not in the previous predictions.
B. Apply the churn model to all rows in the customer_churn_params table, but implement logic to perform an upsert into the predictions table that ignores rows where predictions have not changed.
C. Convert the batch job to a Structured Streaming job using the complete output mode; configure a Structured Streaming job to read from the customer_churn_params table and incrementally predict against the churn model.
D. Replace the current overwrite logic with a merge statement to modify only those records that have changed; write logic to make predictions on the changed records identified by the change data feed.
E. Modify the overwrite logic to include a field populated by calling
spark.sql.functions.current_timestamp() as data are being written; use this field to identify records written on a particular date.

Answer: C

Explanation:
Explanation
This is the correct answer because the JSON posted to the Databricks REST API endpoint 2.0/jobs/create defines a new job with an existing cluster id and a notebook task, but also specifies a new cluster spec with some configurations. According to the documentation, if both an existing cluster id and a new cluster spec are provided, then a new cluster will be created for each run of the job with those configurations, and then terminated after completion. Therefore, the logic defined in the referenced notebook will be executed three times on new clusters with those configurations. Verified References: [Databricks Certified Data Engineer Professional], under "Monitoring & Logging" section; Databricks Documentation, under
"JobsClusterSpecNewCluster" section.

NEW QUESTION # 59
......

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q54-Q59):

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