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NEW QUESTION 38
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 creates the smaller models
- C. It requires the lesser memory to store the coefficients for the model
- D. It reduces the non-significant features e.g. punctuations
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
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 39
A junior data engineer needs to create a Spark SQL table my_table for which Spark manages both the data and
the metadata. The metadata and data should also be stored in the Databricks Filesystem (DBFS).
Which of the following commands should a senior data engineer share with the junior data engineer to
complete this task?
- A. 1. CREATE TABLE my_table (id STRING, value STRING);
- B. 1. CREATE TABLE my_table (id STRING, value STRING) USING DBFS;
- C. 1. CREATE MANAGED TABLE my_table (id STRING, value STRING);
- D. 1. CREATE MANAGED TABLE my_table (id STRING, value STRING) USING
2. org.apache.spark.sql.parquet OPTIONS (PATH "storage-path");
- E. 1. CREATE TABLE my_table (id STRING, value STRING) USING
2. org.apache.spark.sql.parquet OPTIONS (PATH "storage-path")
NEW QUESTION 40
A data analyst has noticed that their Databricks SQL queries are running too slowly. They claim that this issue
is affecting all of their sequentially run queries. They ask the data engineering team for help. The data
engineering team notices that each of the queries uses the same SQL endpoint, but the SQL endpoint is not
used by any other user.
Which of the following approaches can the data engineering team use to improve the latency of the data
- A. They can turn on the Serverless feature for the SQL endpoint
- B. They can increase the cluster size of the SQL endpoint
- C. They can turn on the Auto Stop feature for the SQL endpoint
- D. They can turn on the Serverless feature for the SQL endpoint and change the Spot In-stance Policy to
- E. They can increase the maximum bound of the SQL endpoint's scaling range
NEW QUESTION 41