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NEW QUESTION 42
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 pass the table into PySpark and develop a robustly tested module on the existing query
• C. They could manually replace the date within the table name with the current day's date
• D. They could request that the data analyst rewrites the query to be run less frequently
• E. They could replace the string-formatted date in the table with a timestamp-formatted date

Answer: A

NEW QUESTION 43
Question-26. There are 5000 different color balls, out of which 1200 are pink color. What is the maximum
likelihood estimate for the proportion of "pink" items in the test set of color balls?

• A. 24 0
• B. 4.8
• C. .24
• D. .48
• E. 2.4

Answer: C

Explanation:
Explanation
Given no additional information, the MLE for the probability of an item in the test set is exactly its frequency
in the training set. The method of maximum likelihood corresponds to many well-known estimation methods
in statistics. For example, one may be interested in the heights of adult female penguins, but be unable to
measure the height of every single penguin in a population due to cost or time constraints. Assuming that the
heights are normally (Gaussian) distributed with some unknown mean and variance, the mean and variance
can be estimated with MLE while only knowing the heights of some sample of the overall population. MLE
would accomplish this by taking the mean and variance as parameters and finding particular parametric values
that make the observed results the most probable (given the model).
In general, for a fixed set of data and underlying statistical model the method of maximum likelihood selects
the set of values of the model parameters that maximizes the likelihood function. Intuitively, this maximizes
the "agreement" of the selected model with the observed data, and for discrete random variables it indeed
maximizes the probability of the observed data under the resulting distribution. Maximum-likelihood
estimation gives a unified approach to estimation, which is well-defined in the case of the normal distribution
and many other problems. However in some complicated problems, difficulties do occur: in such problems,
maximum-likelihood estimators are unsuitable or do not exist.

NEW QUESTION 44
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 MANAGED TABLE my_table (id STRING, value STRING) USING
  2. org.apache.spark.sql.parquet OPTIONS (PATH "storage-path");
• B. 1. CREATE MANAGED TABLE my_table (id STRING, value STRING);
• C. 1. CREATE TABLE my_table (id STRING, value STRING) USING DBFS;
• D. 1. CREATE TABLE my_table (id STRING, value STRING);
• E. 1. CREATE TABLE my_table (id STRING, value STRING) USING
  2. org.apache.spark.sql.parquet OPTIONS (PATH "storage-path")

Answer: D

NEW QUESTION 45
A data engineer needs to dynamically create a table name string using three Python varia-bles: region, store,
and year. An example of a table name is below when region = "nyc", store = "100", and year = "2021":
nyc100_sales_2021
Which of the following commands should the data engineer use to construct the table name in Py-thon?

• A. "{region}+{store}+_sales_+{year}"
• B. f"{region}+{store}+_sales_+{year}"
• C. "{region}{store}_sales_{year}"
• D. f"{region}{store}_sales_{year}"
• E. "{region}+{store}+"_sales_"+{year}"

Answer: D

NEW QUESTION 46
A dataset has been defined using Delta Live Tables and includes an expectations clause:
1. CONSTRAINT valid_timestamp EXPECT (timestamp > '2020-01-01')
What is the expected behaviour when a batch of data containing data that violates these constraints is
processed?

• A. Records that violate the expectation cause the job to fail
• B. Records that violate the expectation are dropped from the target dataset and loaded into a quarantine table
• C. Records that violate the expectation are dropped from the target dataset and recorded as invalid in the event log
• D. Records that violate the expectation are added to the target dataset and flagged as in-valid in a field added to the target dataset
• E. Records that violate the expectation are added to the target dataset and recorded as invalid in the event log

Answer: E

NEW QUESTION 47
......