Certified Data Engineer Professional Exam

Exam Certified Data Engineer Professional All QuestionsBrowse all questions from this exam

Question 31

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 to power several production monitoring dashboards and a production model. At present, 45 of the 100 fields are being used in at least one of these applications.

The data engineer is trying to determine the best approach for dealing with schema declaration given the highly-nested structure of the data and the numerous fields.

Which of the following accurately presents information about Delta Lake and Databricks that may impact their decision-making process?

The 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.

Because Delta Lake uses Parquet for data storage, data types can be easily evolved by just modifying file footer information in place.

Human labor in writing code is the largest cost associated with data engineering workloads; as such, automating table declaration logic should be a priority in all migration workloads.

Because Databricks will infer schema using types that allow all observed data to be processed, setting types manually provides greater assurance of data quality enforcement.

Schema inference and evolution on Databricks ensure that inferred types will always accurately match the data types used by downstream systems.

Correct Answer: D

Setting types manually provides greater assurance of data quality enforcement. Relying on inferred schema can be risky due to the possibility of encountering unexpected data types, which may result in errors or inefficient processing. Specifying the schema manually ensures that the data is accurately interpreted and conforms to expected standards, which is crucial for maintaining data integrity in production environments.

Discussion

RafaelCFCOption: D

A is wrong, because Tungsten is a project around improving Spark's efficiency on memory and CPU usage; B is wrong because Parquet does not support file editing, it only supports overwrite and create operations by itself; C is wrong because completely automating schema declaration for tables will incur in reduced previsibility for data types and data quality; E is false because unlucky sampling can yield bad inferences by Spark;

sturcuOption: D

correct

hammer_1234_hOption: D

D is correct. we can use `schema hint` to enforce the schema information that we know and expect on an inferred schema.

hal2401me

from my exam today, both C & D are no longer available, so they can't be correct. E & A are available. E states "always accurate" so I hesitate to choose it. There is a new option stating like "delta lake indexes first 32column in delta log for Z order and optimization"(not sure I remember exactly, it looks statementfully correct). and I chosed this "new" option. Because, this should impact the schema decision by putting high-usage field in the first 32 columns.

guillesdOption: D

Only answer that makes sense

AziLaOption: D

Correct Ans is D