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質問 11
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 refactor their notebook to use SQL and CREATE LIVE TABLE keyword
- D. They need to create a Delta Live Tables pipeline from the Data page
- E. They need to create a Delta Live tables pipeline from the Compute page
正解: B
質問 12
A data engineer has ingested data from an external source into a PySpark DataFrame raw_df. They need to
briefly make this data available in SQL for a data analyst to perform a quality assurance check on the data.
Which of the following commands should the data engineer run to make this data available in SQL for only
the remainder of the Spark session?
- A. raw_df.createTable("raw_df")
- B. raw_df.createOrReplaceTempView("raw_df")
- C. raw_df.write.save("raw_df")
- D. There is no way to share data between PySpark and SQL
- E. raw_df.saveAsTable("raw_df")
正解: B
質問 13
Which of the following describes how Databricks Repos can help facilitate CI/CD workflows on the
Databricks Lakehouse Platform?
- A. Databricks Repos can commit or push code changes to trigger a CI/CD process
- B. Databricks Repos can store the single-source-of-truth Git repository
- C. Databricks Repos can be used to design, develop, and trigger Git automation pipelines
- D. Databricks Repos can facilitate the pull request, review, and approval process before merging branches
- E. Databricks Repos can merge changes from a secondary Git branch into a main Git branch
正解: A
質問 14
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. 2.4
- B. 24 0
- C. .48
- D. 4.8
- E. .24
正解: E
解説:
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.
質問 15
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?
- A. Overwriting a table results in a clean table history for logging and audit purposes
- B. Overwriting a table is an atomic operation and will not leave the table in an unfinished state
- C. Overwriting a table maintains the old version of the table for Time Travel
- D. Overwriting a table allows for concurrent queries to be completed while in progress
- E. Overwriting a table is efficient because no files need to be deleted
正解: A
質問 16
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?
- A. Decrease the number of clusters
- B. Increase the number of clusters
- C. Remove one of the measures
- D. Identify additional measures to add to the analysis
正解: A
質問 17
Which of the following Structured Streaming queries is performing a hop from a Bronze table to a Silver
table?
- A. 1. (spark.table("sales")
2. .agg(sum("sales"),
3. sum("units"))
4. .writeStream
5. .option("checkpointLocation", checkpointPath)
6. .outputMode("complete")
7. .table("aggregatedSales")
8. ) - B. 1. (spark.table("sales")
2. .withColumn("avgPrice", col("sales") / col("units"))
3. .writeStream
4. .option("checkpointLocation", checkpointPath)
5. .outputMode("append")
6. .table("cleanedSales")
7.) - C. 1. (spark.readStream.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales")
6. ) - D. 1. (spark.read.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales")
6. ) - E. 1. (spark.table("sales")
2. .groupBy("store")
3. .agg(sum("sales"))
4. .writeStream
5. .option("checkpointLocation", checkpointPath)
6. .outputMode("complete")
7. .table("aggregatedSales")
8.)
正解: B
質問 18
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
analyst's queries?
- A. They can turn on the Auto Stop feature for the SQL endpoint
- B. They can increase the maximum bound of the SQL endpoint's scaling range
- C. They can turn on the Serverless feature for the SQL endpoint and change the Spot In-stance Policy to
"Reliability Optimized" - D. They can turn on the Serverless feature for the SQL endpoint
- E. They can increase the cluster size of the SQL endpoint
正解: E
質問 19
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?
- A. A data lakehouse provides autoscaling for compute clusters
- B. A data lakehouse combines compute and storage for simple governance
- C. A data lakehouse requires very little data modeling
- D. A data lakehouse fully exists in the cloud
- E. A data lakehouse stores unstructured data and is ACID-compliant
正解: E
質問 20
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?
- A. The ability to declare and maintain data table dependencies
- B. The ability to automatically scale compute resources
- C. The ability to perform batch and streaming queries
- D. The ability to access previous versions of data tables
- E. The ability to write pipelines in Python and/or SQL
正解: A
質問 21
A data engineer has a Job with multiple tasks that runs nightly. One of the tasks unexpectedly fails during 10
percent of the runs.
Which of the following actions can the data engineer perform to ensure the Job completes each night while
minimizing compute costs?
- A. They can institute a retry policy for the task that periodically fails
- B. They can set up the Job to run multiple times ensuring that at least one will complete
- C. They can utilize a Jobs cluster for each of the tasks in the Job
- D. They can observe the task as it runs to try and determine why it is failing
- E. They can institute a retry policy for the entire Job
正解: A
質問 22
What are the advantages of the Hashing Features?
- A. Easily reverse engineer vectors to determine which original feature mapped to a vector location
- B. Requires the less memory
- C. Less pass through the training data
正解: B,C
解説:
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.
質問 23
A new data engineer has started at a company. The data engineer has recently been added to the company's
Databricks workspace as [email protected]. The data engineer needs to be able to query the table
sales in the database retail. The new data engineer already has been granted USAGE on the database retail.
Which of the following commands can be used to grant the appropriate permissions to the new data engineer?
- A. GRANT USAGE ON TABLE [email protected] TO sales;
- B. GRANT USAGE ON TABLE sales TO [email protected];
- C. GRANT SELECT ON TABLE sales TO [email protected];
- D. GRANT CREATE ON TABLE sales TO [email protected];
- E. GRANT SELECT ON TABLE [email protected] TO sales;
正解: C
質問 24
A data engineer has set up two Jobs that each run nightly. The first Job starts at 12:00 AM, and it usually
completes in about 20 minutes. The second Job depends on the first Job, and it starts at 12:30 AM. Sometimes,
the second Job fails when the first Job does not complete by 12:30 AM.
Which of the following approaches can the data engineer use to avoid this problem?
- A. They can set up a retry policy on the first Job to help it run more quickly
- B. They can set up the data to stream from the first Job to the second Job
- C. They can utilize multiple tasks in a single job with a linear dependency
- D. They can use cluster pools to help the Jobs run more efficiently
- E. They can limit the size of the output in the second Job so that it will not fail as easily
正解: C
質問 25
Which of the following describes a benefit of a data lakehouse that is unavailable in a traditional data
warehouse?
- A. A data lakehouse utilizes proprietary storage formats for data
- B. A data lakehouse captures snapshots of data for version control purposes
- C. A data lakehouse provides a relational system of data management
- D. A data lakehouse enables both batch and streaming analytics
- E. A data lakehouse couples storage and compute for complete control
正解: D
質問 26
Which of the following commands will return records from an existing Delta table my_table where duplicates
have been removed?
- A. 1. SELECT *
2. FROM my_table
3. WHERE duplicate = False; - B. 1. MERGE INTO my_table a
2. USING new_records b; - C. 1. SELECT DISTINCT *
2. FROM my_table; - D. 1. DROP DUPLICATES
2. FROM my_table; - E. 1. MERGE INTO my_table a
2. USING new_records b ON a.id = b.id
3. WHEN NOT MATCHED
4. THEN INSERT *;
正解: C
質問 27
A data engineering manager has noticed that each of the queries in a Databricks SQL dashboard takes a few
minutes to update when they manually click the "Refresh" button. They are curious why this might be
occurring, so a team member provides a variety of reasons on why the delay might be occurring.
Which of the following reasons fails to explain why the dashboard might be taking a few minutes to update?
- A. The queries attached to the dashboard might take a few minutes to run under normal circumstances
- B. The queries attached to the dashboard might first be checking to determine if new data is available
- C. The Job associated with updating the dashboard might be using a non-pooled endpoint
- D. The queries attached to the dashboard might all be connected to their own, unstarted Databricks clusters
- E. The SQL endpoint being used by each of the queries might need a few minutes to start up
正解: C
質問 28
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?
- A. UNION
- B. MERGE
- C. OUTER JOIN
- D. INNER JOIN
- E. LEFT JOIN
正解: D
質問 29
A data engineer is designing a data pipeline. The source system generates files in a shared directory that is also
used by other processes. As a result, the files should be kept as is and will accumulate in the directory. The
data engineer needs to identify which files are new since the previous run in the pipeline, and set up the
pipeline to only ingest those new files with each run.
Which of the following tools can the data engineer use to solve this problem?
- A. Auto Loader
- B. Delta Lake
- C. Unity Catalog
- D. Databricks SQL
- E. Data Explorer
正解: A
質問 30
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