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NEW QUESTION 35
A data analyst has provided a data engineering team with the following Spark SQL query:
1.SELECT district,
2.avg(sales)
3.FROM store_sales_20220101
4.GROUP BY district;
The data analyst would like the data engineering team to run this query every day. The date at the end of the
table name (20220101) should automatically be replaced with the current date each time the query is run.
Which of the following approaches could be used by the data engineering team to efficiently auto-mate this
process?

• A. They could wrap the query using PySpark and use Python's string variable system to automatically
  update the table name
• B. They could request that the data analyst rewrites the query to be run less frequently
• C. They could replace the string-formatted date in the table with a timestamp-formatted date
• D. They could manually replace the date within the table name with the current day's date
• E. They could pass the table into PySpark and develop a robustly tested module on the existing query

Answer: A

NEW QUESTION 36
A data engineer has three notebooks in an ELT pipeline. The notebooks need to be executed in a specific order
for the pipeline to complete successfully. The data engineer would like to use Delta Live Tables to manage this
process.
Which of the following steps must the data engineer take as part of implementing this pipeline using Delta
Live Tables?

• A. They need to refactor their notebook to use Python and the dlt library
• B. They need to create a Delta Live Tables pipeline from the Jobs page
• C. They need to create a Delta Live tables pipeline from the Compute page
• D. They need to refactor their notebook to use SQL and CREATE LIVE TABLE keyword
• E. They need to create a Delta Live Tables pipeline from the Data page

Answer: B

NEW QUESTION 37
What are the advantages of the Hashing Features?

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

Answer: A,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 38
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)
• B. P(E1+E2)/P(E1)
• C. P(E1)/P(E2)
• D. P(E2)/(P(E1+E2)

Answer: A

NEW QUESTION 39
A data engineer has configured a Structured Streaming job to read from a table, manipulate the data, and then
perform a streaming write into a new table. The code block used by the data engineer is below:
1. (spark.table("sales")
2. .withColumn("avg_price", col("sales") / col("units"))
3. .writeStream
4. .option("checkpointLocation", checkpointPath)
5. .outputMode("complete")
6. ._____
7. .table("new_sales")
8.)
If the data engineer only wants the query to execute a single micro-batch to process all of the available data,
which of the following lines of code should the data engineer use to fill in the blank?

• A. .trigger(once=True)
• B. .trigger(processingTime="once")
• C. .processingTime(1)
• D. .trigger(continuous="once")
• E. .processingTime("once")

Answer: A

NEW QUESTION 40
......