Google Professional-Data-Engineer Exam With Confidence Using Practice Dumps

There are very few occurrences of mutations relative to normal samples.

There are roughly equal occurrences of both normal and mutated samples in the database.

You expect future mutations to have different features from the mutated samples in the database.

You expect future mutations to have similar features to the mutated samples in the database.

You already have labels for which samples are mutated and which are normal in the database.

Use Cloud Dataprep to build and maintain the transformation recipes, and execute them on a scheduled basis

Load each month’s CSV data into BigQuery, and write a SQL query to transform the data to a standard schema. Merge the transformed tables together with a SQL query

Help the analysts write a Cloud Dataflow pipeline in Python to perform the transformation. The Python code should be stored in a revision control system and modified as the incoming data’s schema changes

Use Apache Spark on Cloud Dataproc to infer the schema of the CSV file before creating a Dataframe. Then implement the transformations in Spark SQL before writing the data out to Cloud Storage and loading into BigQuery

Assign global unique identifiers (GUID) to each data entry.

Compute the hash value of each data entry, and compare it with all historical data.

Store each data entry as the primary key in a separate database and apply an index.

Maintain a database table to store the hash value and other metadata for each data entry.