Pass the Google Cloud Certified Professional-Data-Engineer Questions and answers with ValidTests

Do not use a production instance.

Run your test for at least 10 minutes.

Before you test, run a heavy pre-test for several minutes.

Use at least 300 GB of data.

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Keep the schema the same, maintain the different tables for the book and each of the attributes, and query as you are doing today

Create a table that is wide and includes a column for each attribute, including the author's first name, last name, date of birth, etc

Create a table that includes information about the books and authors, but nest the author fields inside the author column

Keep the schema the same, create a view that joins all of the tables, and always query the view

Delete the table CLICK_STREAM, and then re-create it such that the column DT is of the TIMESTAMP type. Reload the data.

Add a column TS of the TIMESTAMP type to the table CLICK_STREAM, and populate the numeric values from the column TS for each row. Reference the column TS instead of the column DT from now on.

Create a view CLICK_STREAM_V, where strings from the column DT are cast into TIMESTAMP values. Reference the view CLICK_STREAM_V instead of the table CLICK_STREAM from now on.

Add two columns to the table CLICK STREAM: TS of the TIMESTAMP type and IS_NEW of the BOOLEAN type. Reload all data in append mode. For each appended row, set the value of IS_NEW to true. For future queries, reference the column TS instead of the column DT, with the WHERE clause ensuring that the value of IS_NEW must be true.

Construct a query to return every row of the table CLICK_STREAM, while using the built-in function to cast strings from the column DT into TIMESTAMP values. Run the query into a destination table NEW_CLICK_STREAM, in which the column TS is the TIMESTAMP type. Reference the table NEW_CLICK_STREAM instead of the table CLICK_STREAM from now on. In the future, new data is loaded into the table NEW_CLICK_STREAM.

Load data into different partitions.

Load data into a different dataset for each client.

Put each client’s BigQuery dataset into a different table.

Restrict a client’s dataset to approved users.

Only allow a service account to access the datasets.

Use the appropriate identity and access management (IAM) roles for each client’s users.

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.

Eliminate features that are highly correlated to the output labels.

Combine highly co-dependent features into one representative feature.

Instead of feeding in each feature individually, average their values in batches of 3.

Remove the features that have null values for more than 50% of the training records.

Make a call to the Stackdriver API to list all logs, and apply an advanced filter.

In the Stackdriver logging admin interface, and enable a log sink export to BigQuery.

In the Stackdriver logging admin interface, enable a log sink export to Google Cloud Pub/Sub, and subscribe to the topic from your monitoring tool.

Using the Stackdriver API, create a project sink with advanced log filter to export to Pub/Sub, and subscribe to the topic from your monitoring tool.

Create a deep inspection job on each table in your dataset with Cloud Data Loss Prevention and create an inspection template that includes the STREET_ADDRESS infoType.

Create a de-identification job in Cloud Data Loss Prevention and use the masking transformation.

Write a SQL query in BigQuery by using REGEXP_CONTAINS on all tables in your dataset to find rows where the word "street" appears.

Create a discovery scan configuration on your organization with Cloud Data Loss Prevention and create an inspection template that

includes the STREET_ADDRESS infoType.

Remove Cloud Storage IAM permissions to the External Team on the acme-raw-data project

Create Virtual Private Cloud (VPC) firewall rules on the acme-raw-data protect that deny all Ingress traffic from the External Team CIDR range

Create a VPC Service Controls perimeter containing both protects and BigQuery as a restricted API Add the External Team users to the perimeter s Access Level

Create a VPC Service Controls perimeter containing both protects and Cloud Storage as a restricted API. Add the Development Team users to the perimeter's Access Level