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Databricks Certified Professional Data Engineer Exam Sample Questions (Q56-Q61):

NEW QUESTION # 56
Which of the below SQL commands creates a session scoped temporary view?

A. 1.CREATE OR REPLACE LOCAL VIEW view_name
2.AS SELECT * FROM table_name
B. 1.CREATE OR REPLACE LOCAL TEMPORARY VIEW view_name
2.AS SELECT * FROM table_name
C. 1.CREATE OR REPLACE TEMPORARY VIEW view_name
2.AS SELECT * FROM table_name
(Correct)
D. 1.CREATE OR REPLACE VIEW view_name
2.AS SELECT * FROM table_name
E. 1.CREATE OR REPLACE GLOBAL TEMPORARY VIEW view_name
2.AS SELECT * FROM table_name

Answer: C

Explanation:
Explanation
The answer is
1.CREATE OR REPLACE TEMPORARY VIEW view_name
2.AS SELECT * FROM table_name
The default temporary view is session scoped, as soon as the session ends or if a notebook is de-tached session scoped temporary view is dropped.

NEW QUESTION # 57
The data governance team has instituted a requirement that all tables containing Personal Identifiable Information (PH) must be clearly annotated. This includes adding column comments, table comments, and setting the custom table property "contains_pii" = true.
The following SQL DDL statement is executed to create a new table:
Which command allows manual confirmation that these three requirements have been met?

A. DESCRIBE HISTORY dev.pii test
B. SHOW TBLPROPERTIES dev.pii test
C. DESCRIBE DETAIL dev.pii test
D. SHOW TABLES dev
E. DESCRIBE EXTENDED dev.pii test

Answer: E

Explanation:
This is the correct answer because it allows manual confirmation that these three requirements have been met.
The requirements are that all tables containing Personal Identifiable Information (PII) must be clearly annotated, which includes adding column comments, table comments, and setting the custom table property
"contains_pii" = true. The DESCRIBE EXTENDED command is used to display detailed information about a table, such as its schema, location, properties, and comments. By using this command on the dev.pii_test table, one can verify that the table has been created with the correct column comments, table comment, and custom table property as specified in the SQL DDL statement. Verified References: [Databricks Certified Data Engineer Professional], under "Lakehouse" section; Databricks Documentation, under "DESCRIBE EXTENDED" section.

NEW QUESTION # 58
Question-3: In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel
trick), is a fast and space-efficient way of vectorizing features (such as the words in a language), i.e., turning
arbitrary features into indices in a vector or matrix. It works by applying a hash function to the features and
using their hash values modulo the number of features as indices directly, rather than looking the indices up in
an associative array. So what is the primary reason of the hashing trick for building classifiers?

A. Noisy features are removed
B. It requires the lesser memory to store the coefficients for the model
C. It reduces the non-significant features e.g. punctuations
D. It creates the smaller models

Answer: B

Explanation:
Explanation
This hashed feature approach has the distinct advantage of requiring less memory and one less pass through
the training data, but it can make it much harder to reverse engineer vectors to determine which original
feature mapped to a vector location. This is because multiple features may hash to the same location. With
large vectors or with multiple locations per feature, this isn't a problem for accuracy but it can make it hard to
understand what a classifier is doing.
Models always have a coefficient per feature, which are stored in memory during model building. The hashing
trick collapses a high number of features to a small number which reduces the number of coefficients and thus
memory requirements. Noisy features are not removed; they are combined with other features and so still have
an impact.
The validity of this approach depends a lot on the nature of the features and problem domain; knowledge of
the domain is important to understand whether it is applicable or will likely produce poor results. While
hashing features may produce a smaller model, it will be one built from odd combinations of real-world
features, and so will be harder to interpret.
An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like
variables aren't a problem.

NEW QUESTION # 59
Assuming that the Databricks CLI has been installed and configured correctly, which Databricks CLI command can be used to upload a custom Python Wheel to object storage mounted with the DBFS for use with a production job?

A. configure
B. fs
C. jobs
D. workspace
E. libraries

Answer: E

Explanation:
The libraries command group allows you to install, uninstall, and list libraries on Databricks clusters. You can use the libraries install command to install a custom Python Wheel on a cluster by specifying the --whl option and the path to the wheel file. For example, you can use the following command to install a custom Python Wheel named mylib-0.1-py3-none-any.whl on a cluster with the id 1234-567890-abcde123:
databricks libraries install --cluster-id 1234-567890-abcde123 --whl dbfs:/mnt/mylib/mylib-0.1-py3-none-any.whl This will upload the custom Python Wheel to the cluster and make it available for use with a production job. You can also use the libraries uninstall command to uninstall a library from a cluster, and the libraries list command to list the libraries installed on a cluster.
Reference:
Libraries CLI (legacy): https://docs.databricks.com/en/archive/dev-tools/cli/libraries-cli.html Library operations: https://docs.databricks.com/en/dev-tools/cli/commands.html#library-operations Install or update the Databricks CLI: https://docs.databricks.com/en/dev-tools/cli/install.html

NEW QUESTION # 60
Which of the following statements describes Delta Lake?

A. Delta Lake is an open format storage layer that processes data
B. Delta Lake is an open source platform to help manage the complete machine learning lifecycle
C. Delta Lake is an open format storage layer that delivers reliability, security, and per-formance
D. Delta Lake is an open source data storage format for distributed data
E. Delta Lake is an open source analytics engine used for big data workloads

Answer: C

Explanation:
Explanation
Delta Lake

NEW QUESTION # 61
......

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