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NEW QUESTION 39
A data engineering team has created a series of tables using Parquet data stored in an external sys-tem. The
team is noticing that after appending new rows to the data in the external system, their queries within
Databricks are not returning the new rows. They identify the caching of the previous data as the cause of this
issue.
Which of the following approaches will ensure that the data returned by queries is always up-to-date?

• A. The tables should be altered to include metadata to not cache
• B. The tables should be stored in a cloud-based external system
• C. The tables should be converted to the Delta format
• D. The tables should be refreshed in the writing cluster before the next query is run
• E. The tables should be updated before the next query is run

Answer: C

NEW QUESTION 40
A data architect is designing a data model that works for both video-based machine learning work-loads and
highly audited batch ETL/ELT workloads.
Which of the following describes how using a data lakehouse can help the data architect meet the needs of
both workloads?

• A. A data lakehouse stores unstructured data and is ACID-compliant
• B. A data lakehouse fully exists in the cloud
• C. A data lakehouse combines compute and storage for simple governance
• D. A data lakehouse provides autoscaling for compute clusters
• E. A data lakehouse requires very little data modeling

Answer: A

NEW QUESTION 41
What are the advantages of the Hashing Features?

• A. Easily reverse engineer vectors to determine which original feature mapped to a vector location
• B. Less pass through the training data
• C. Requires the less memory

Answer: B,C

Explanation:
Explanation
SGD-based classifiers avoid the need to predetermine vector size by simply picking a reasonable size and
shoehorning the training data into vectors of that size. This approach is known as feature hashing. The
shoehorning is done by picking one or more locations by using a hash of the name of the variable for
continuous variables or a hash of the variable name and the category name or word for categorical, text*like, or
word-like data.
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.
An additional benefit of feature hashing is that the unknown and unbounded vocabularies typical of word-like
variables aren't a problem.

NEW QUESTION 42
If E1 and E2 are two events, how do you represent the conditional probability given that E2 occurs given that
E1 has occurred?

• A. P(E2)/(P(E1+E2)
• B. P(E1+E2)/P(E1)
• C. P(E2)/P(E1)
• D. P(E1)/P(E2)

Answer: C

NEW QUESTION 43
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