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NEW QUESTION 43
Which of the following data workloads will utilize a Silver table as its source?
- A. A job that queries aggregated data that already feeds into a dashboard
- B. A job that ingests raw data from a streaming source into the Lakehouse
- C. A job that enriches data by parsing its timestamps into a human-readable format
- D. A job that aggregates cleaned data to create standard summary statistics
- E. A job that cleans data by removing malformatted records
Answer: D
NEW QUESTION 44
An engineering manager uses a Databricks SQL query to monitor their team's progress on fixes related to
customer-reported bugs. The manager checks the results of the query every day, but they are manually
rerunning the query each day and waiting for the results.
Which of the following approaches can the manager use to ensure the results of the query are up-dated each
day?
- A. They can schedule the query to run every 1 day from the Jobs UI
- B. They can schedule the query to refresh every 1 day from the query's page in Databricks SQL
- C. They can schedule the query to run every 12 hours from the Jobs UI
- D. They can schedule the query to refresh every 12 hours from the SQL endpoint's page in Databricks SQL
- E. They can schedule the query to refresh every 1 day from the SQL endpoint's page in Databricks SQL
Answer: B
NEW QUESTION 45
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. It creates the smaller models
- B. Noisy features are removed
- C. It reduces the non-significant features e.g. punctuations
- D. It requires the lesser memory to store the coefficients for the model
Answer: D
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 46
A data engineering team has been using a Databricks SQL query to monitor the performance of an ELT job.
The ELT job is triggered by a specific number of input records being ready to process. The Databricks SQL
query returns the number of minutes since the job's most recent runtime.
Which of the following approaches can enable the data engineering team to be notified if the ELT job has not
been run in an hour?
- A. They can set up an Alert for the query to notify when the ELT job fails
- B. They can set up an Alert for the accompanying dashboard to notify them if the returned value is greater
than 60 - C. This type of alerting is not possible in Databricks
- D. They can set up an Alert for the query to notify them if the returned value is greater than 60
- E. They can set up an Alert for the accompanying dashboard to notify when it has not re-freshed in 60
minutes
Answer: D
NEW QUESTION 47
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