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Databricks Certified Professional Data Engineer Exam Sample Questions (Q77-Q82):

NEW QUESTION # 77
The downstream consumers of a Delta Lake table have been complaining about data quality issues impacting performance in their applications. Specifically, they have complained that invalidlatitudeandlongitudevalues in theactivity_detailstable have been breaking their ability to use other geolocation processes.
A junior engineer has written the following code to addCHECKconstraints to the Delta Lake table:

A senior engineer has confirmed the above logic is correct and the valid ranges for latitude and longitude are provided, but the code fails when executed.
Which statement explains the cause of this failure?

A. The activity details table already exists; CHECK constraints can only be added during initial table creation.
B. The activity details table already contains records; CHECK constraints can only be added prior to inserting values into a table.
C. Because another team uses this table to support a frequently running application, two-phase locking is preventing the operation from committing.
D. The current table schema does not contain the field valid coordinates; schema evolution will need to be enabled before altering the table to add a constraint.
E. The activity details table already contains records that violate the constraints; all existing data must pass CHECK constraints in order to add them to an existing table.

Answer: E

Explanation:
The failure is that the code to add CHECK constraints to the Delta Lake table fails when executed. The code uses ALTER TABLE ADD CONSTRAINT commands to add two CHECK constraints to a table named activity_details. The first constraint checks if the latitude value is between -90 and 90, and the second constraint checks if the longitude value is between -180 and 180. The cause of this failure is that the activity_details table already contains records that violate these constraints, meaning that they have invalid latitude or longitude values outside of these ranges. When adding CHECK constraints to an existing table, Delta Lake verifies that all existing data satisfies the constraints before adding them to the table. If any record violates the constraints, Delta Lake throws an exception and aborts the operation. Verified References:
[Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Add a CHECK constraint to an existing table" section.
https://docs.databricks.com/en/sql/language-manual/sql-ref-syntax-ddl-alter-table.html#add-constraint

NEW QUESTION # 78
An external object storage container has been mounted to the location/mnt/finance_eda_bucket.
The following logic was executed to create a database for the finance team:

After the database was successfully created and permissions configured, a member of the finance team runs the following code:

If all users on the finance team are members of thefinancegroup, which statement describes how thetx_sales table will be created?

A. A logical table will persist the query plan to the Hive Metastore in the Databricks control plane.
B. A managed table will be created in the DBFS root storage container.
C. An managed table will be created in the storage container mounted to /mnt/finance eda bucket.
D. A logical table will persist the physical plan to the Hive Metastore in the Databricks control plane.
E. An external table will be created in the storage container mounted to /mnt/finance eda bucket.

Answer: E

Explanation:
Explanation
The code uses the CREATE TABLE USING DELTA command to create a Delta Lake table from an existing Parquet file stored in an external object storage container mounted to /mnt/finance_eda_bucket. The code 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 code 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. Verified References: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Create an external table" section.

NEW QUESTION # 79
A junior data engineer is working to implement logic for a Lakehouse table named silver_device_recordings. The source data contains 100 unique fields in a highly nested JSON structure.
The silver_device_recordings table will be used downstream for highly selective joins on a number of fields, and will also be leveraged by the machine learning team to filter on a handful of relevant fields, in total, 15 fields have been identified that will often be used for filter and join logic.
The data engineer is trying to determine the best approach for dealing with these nested fields before declaring the table schema.
Which of the following accurately presents information about Delta Lake and Databricks that may Impact their decision-making process?

A. Because Delta Lake uses Parquet for data storage, Dremel encoding information for nesting can be directly referenced by the Delta transaction log.
B. Tungsten encoding used by Databricks is optimized for storing string data: newly-added native support for querying JSON strings means that string types are always most efficient.
C. By default Delta Lake collects statistics on the first 32 columns in a table; these statistics are leveraged for data skipping when executing selective queries.
D. Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

Answer: C

Explanation:
Delta Lake, built on top of Parquet, enhances query performance through data skipping, which is based on the statistics collected for each file in a table. For tables with a large number of columns, Delta Lake by default collects and stores statistics only for the first 32 columns. These statistics include min/max values and null counts, which are used to optimize query execution by skipping irrelevant data files. When dealing with highly nested JSON structures, understanding this behavior is crucial for schema design, especially when determining which fields should be flattened or prioritized in the table structure to leverage data skipping efficiently for performance optimization.
Reference: Databricks documentation on Delta Lake optimization techniques, including data skipping and statistics collection (https://docs.databricks.com/delta/optimizations/index.html).

NEW QUESTION # 80
Which of the following developer operations in the CI/CD can only be implemented through a GIT provider when using Databricks Repos.

A. Pull request and review process
B. Create and edit code
C. Create a new branch
D. Trigger Databricks Repos pull API to update the latest version
E. Commit and push code

Answer: A

Explanation:
Explanation
The answer is Pull request and review process, please note: the question is asking for steps that are being implemented in GIT provider not Databricks Repos.
See below diagram to understand the role of Databricks Repos and Git provider plays when building a CI/CD workdlow.
All the steps highlighted in yellow can be done Databricks Repo, all the steps highlighted in Gray are done in a git provider like Github or Azure Devops.
Diagram Description automatically generated

Bottom of Form
Top of Form

NEW QUESTION # 81
Which of the following is true of Delta Lake and the Lakehouse?

A. Because Parquet compresses data row by row. strings will only be compressed when a character is repeated multiple times.
B. Views in the Lakehouse maintain a valid cache of the most recent versions of source tables at all times.
C. Delta Lake automatically collects statistics on the first 32 columns of each table which are leveraged in data skipping based on query filters.
D. Primary and foreign key constraints can be leveraged to ensure duplicate values are never entered into a dimension table.
E. Z-order can only be applied to numeric values stored in Delta Lake tables

Answer: C

Explanation:
Explanation
https://docs.delta.io/2.0.0/table-properties.html
Delta Lake automatically collects statistics on the first 32 columns of each table, which are leveraged in data skipping based on query filters1. Data skipping is a performance optimization technique that aims to avoid reading irrelevant data from the storage layer1. By collecting statistics such as min/max values, null counts, and bloom filters, Delta Lake can efficiently prune unnecessary files or partitions from the query plan1. This can significantly improve the query performance and reduce the I/O cost.
The other options are false because:
Parquet compresses data column by column, not row by row2. This allows for better compression ratios, especially for repeated or similar values within a column2.
Views in the Lakehouse do not maintain a valid cache of the most recent versions of source tables at all times3. Views are logical constructs that are defined by a SQL query on one or more base tables3. Views are not materialized by default, which means they do not store any data, but only the query definition3. Therefore, views always reflect the latest state of the source tables when queried3.
However, views can be cached manually using the CACHE TABLE or CREATE TABLE AS SELECT commands.
Primary and foreign key constraints can not be leveraged to ensure duplicate values are never entered into a dimension table. Delta Lake does not support enforcing primary and foreign key constraints on tables. Constraints are logical rules that define the integrity and validity of the data in a table. Delta Lake relies on the application logic or the user to ensure the data quality and consistency.
Z-order can be applied to any values stored in Delta Lake tables, not only numeric values. Z-order is a technique to optimize the layout of the data files by sorting them on one or more columns. Z-order can improve the query performance by clustering related values together and enabling more efficient data skipping. Z-order can be applied to any column that has a defined ordering, such as numeric, string, date, or boolean values.
References: Data Skipping, Parquet Format, Views, [Caching], [Constraints], [Z-Ordering]

NEW QUESTION # 82
......

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