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NEW QUESTION 30
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. Data Explorer
- C. Delta Lake
- D. Unity Catalog
- E. Databricks SQL
Answer: A
NEW QUESTION 31
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 increase the cluster size of 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
- D. They can turn on the Serverless feature for the SQL endpoint and change the Spot In-stance Policy to
"Reliability Optimized" - E. They can turn on the Auto Stop feature for the SQL endpoint
Answer: A
NEW QUESTION 32
Which of the following statements describes Delta Lake?
- A. Delta Lake is an open source platform to help manage the complete machine learning lifecycle
- B. Delta Lake is an open format storage layer that delivers reliability, security, and per-formance
- C. Delta Lake is an open format storage layer that processes data
- D. Delta Lake is an open source data storage format for distributed data
- E. Delta Lake is an open source analytics engine used for big data workloads
Answer: B
Explanation:
Explanation
Delta Lake
NEW QUESTION 33
A data engineer has developed a code block to perform a streaming read on a data source. The code block is
below:
1. (spark
2. .read
3. .schema(schema)
4. .format("cloudFiles")
5. .option("cloudFiles.format", "json")
6. .load(dataSource)
7. )
The code block is returning an error.
Which of the following changes should be made to the code block to configure the block to successfully
perform a streaming read?
- A. A new .stream line should be added after the .load(dataSource) line
- B. The .read line should be replaced with .readStream
- C. A new .stream line should be added after the .read line
- D. The .format("cloudFiles") line should be replaced with .format("stream")
- E. A new .stream line should be added after the spark line
Answer: B
NEW QUESTION 34
Which of the following data workloads will utilize a Silver table as its source?
- A. A job that ingests raw data from a streaming source into the Lakehouse
- B. A job that enriches data by parsing its timestamps into a human-readable format
- C. A job that cleans data by removing malformatted records
- D. A job that aggregates cleaned data to create standard summary statistics
- E. A job that queries aggregated data that already feeds into a dashboard
Answer: D
NEW QUESTION 35
What are the advantages of the Hashing Features?
- A. Requires the less memory
- B. Easily reverse engineer vectors to determine which original feature mapped to a vector location
- C. Less pass through the training data
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 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 Compute page
- C. They need to create a Delta Live Tables pipeline from the Jobs 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: C
NEW QUESTION 37
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")
Answer: A
NEW QUESTION 38
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 pass the table into PySpark and develop a robustly tested module on the existing query
- B. They could replace the string-formatted date in the table with a timestamp-formatted date
- 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 wrap the query using PySpark and use Python's string variable system to automatically
update the table name
Answer: E
NEW QUESTION 39
A data engineer has created a Delta table as part of a data pipeline. Downstream data analysts now need
SELECT permission on the Delta table.
Assuming the data engineer is the Delta table owner, which part of the Databricks Lakehouse Plat-form can
the data engineer use to grant the data analysts the appropriate access?
- A. Data Explorer
- B. Repos
- C. Databricks Filesystem
- D. Jobs
B Dashboards
Answer: B
NEW QUESTION 40
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. LEFT JOIN
- B. OUTER JOIN
- C. INNER JOIN
- D. UNION
- E. MERGE
Answer: C
NEW QUESTION 41
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(processingTime="once")
- B. .trigger(continuous="once")
- C. .processingTime(1)
- D. .processingTime("once")
- E. .trigger(once=True)
Answer: E
NEW QUESTION 42
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. raw_df.saveAsTable("raw_df")
- E. There is no way to share data between PySpark and SQL
Answer: B
NEW QUESTION 43
A denote the event 'student is female' and let B denote the event 'student is French'. In a class of 100 students
suppose 60 are French, and suppose that 10 of the French students are females. Find the probability that if I
pick a French student, it will be a girl, that is, find P(A|B).
- A. 1/3
- B. 1/6
- C. 2/6
- D. 2/3
Answer: B
Explanation:
Explanation
Since 10 out of 100 students are both French and female, then
P(AandB)=10100
Also. 60 out of the 100 students are French, so
P(B)=60100
So the required probability is:
P(A|B)=P(AandB)P(B)=10/10060/100=16
NEW QUESTION 44
Which of the following locations hosts the driver and worker nodes of a Databricks-managed clus-ter?
- A. JDBC data source
- B. Data plane
- C. Databricks web application
- D. Databricks Filesystem
- E. Control plane
Answer: B
Explanation:
Explanation
See the Databricks high-level architecture
NEW QUESTION 45
In which phase of the data analytics lifecycle do Data Scientists spend the most time in a project?
- A. Data Preparation
- B. Discovery
- C. Model Building
- D. Communicate Results
Answer: A
NEW QUESTION 46
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. It requires the lesser memory to store the coefficients for the model
- B. It reduces the non-significant features e.g. punctuations
- C. It creates the smaller models
- D. Noisy features are removed
Answer: A
Explanation:
Explanation
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
an impact.
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 47
Which of the following Structured Streaming queries is performing a hop from a Bronze table to a Silver
table?
- A. 1. (spark.readStream.load(rawSalesLocation)
2. .writeStream
3. .option("checkpointLocation", checkpointPath)
4. .outputMode("append")
5. .table("uncleanedSales")
6. ) - 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.table("sales")
2. .groupBy("store")
3. .agg(sum("sales"))
4. .writeStream
5. .option("checkpointLocation", checkpointPath)
6. .outputMode("complete")
7. .table("aggregatedSales")
8.) - 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. .agg(sum("sales"),
3. sum("units"))
4. .writeStream
5. .option("checkpointLocation", checkpointPath)
6. .outputMode("complete")
7. .table("aggregatedSales")
8. )
Answer: B
NEW QUESTION 48
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