EMT Practice Test

1. Question Content...

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Question1: A data engineer is overwriting data in a table by deleting the table and recreating the table. Another data
engineer suggests that this is inefficient and the table should simply be overwritten instead.
Which of the following reasons to overwrite the table instead of deleting and recreating the table is incorrect?

Question2: What is the probability that the total of two dice will be greater than 8, given that the first die is a 6?

Question3: A data architect has determined that a table of the following format is necessary:
Which of the following code blocks uses SQL DDL commands to create an empty Delta table in the above
format regardless of whether a table already exists with this name?

Question4: You are using k-means clustering to classify heart patients for a hospital. You have chosen Patient Sex,
Height, Weight, Age and Income as measures and have used 3 clusters. When you create a pair-wise plot of
the clusters, you notice that there is significant overlap between the clusters. What should you do?

Question5: Two junior data engineers are authoring separate parts of a single data pipeline notebook. They are working on
separate Git branches so they can pair program on the same notebook simultaneously. A senior data engineer
experienced in Databricks suggests there is a better alternative for this type of collaboration.
Which of the following supports the senior data engineer's claim?

Question6: 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?

Question7: 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?

Question8: Which of the following benefits does Delta Live Tables provide for ELT pipelines over standard data pipelines
that utilize Spark and Delta Lake on Databricks?

Question9: A data engineer wants to horizontally combine two tables as a part of a query. They want to use a shared
column as a key column, and they only want the query result to contain rows whose value in the key column is
present in both tables.
Which of the following SQL commands can they use to accomplish this task?

Question10: You are asked to create a model to predict the total number of monthly subscribers for a specific magazine.
You are provided with 1 year's worth of subscription and payment data, user demographic data, and 10 years
worth of content of the magazine (articles and pictures). Which algorithm is the most appropriate for building
a predictive model for subscribers?

Question11: Which of the following statements describes Delta Lake?

Question12: Which of the following commands will return records from an existing Delta table my_table where duplicates
have been removed?

Question13: 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?

Question14: A junior data engineer has ingested a JSON file into a table raw_table with the following schema:
1. cart_id STRING,
2. items ARRAY<item_id:STRING>
The junior data engineer would like to unnest the items column in raw_table to result in a new table with the
following schema:
1.cart_id STRING,
2.item_id STRING
Which of the following commands should the junior data engineer run to complete this task?

Question15: A data engineering team is in the process of converting their existing data pipeline to utilize Auto Loader for
incremental processing in the ingestion of JSON files. One data engineer comes across the following code
block in the Auto Loader documentation:
1. (streaming_df = spark.readStream.format("cloudFiles")
2. .option("cloudFiles.format", "json")
3. .option("cloudFiles.schemaLocation", schemaLocation)
4. .load(sourcePath))
Assuming that schemaLocation and sourcePath have been set correctly, which of the following changes does
the data engineer need to make to convert this code block to use Auto Loader to ingest the data?

Question16: Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?

Question17: 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?

Question18: 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?

Question19: Which of the following data workloads will utilize a Bronze table as its source?

Question20: In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?