View Full Databricks Certified Associate Developer for Apache Spark Exam Dumps  and Practice Test Dumps

 

Question 181.

Which Spark SQL function converts all alphabetic characters in a string to lowercase?

1. initcap()
   2. lower()
   3. upper()
   4. trim()

Correct Answer: 2. lower()

Explanation:

lower() converts alphabetic characters in a string expression to lowercase. It is commonly used for text normalization before comparisons, joins, grouping, or deduplication. upper() converts text to uppercase, initcap() capitalizes the beginning of words, and trim() removes leading and trailing whitespace. Lowercasing values can help reduce mismatches caused by inconsistent capitalization across source systems.

Question 182.

Which Spark SQL function returns the number of characters in a string?

1. size()
   2. count()
   3. length()
   4. array_size()

Correct Answer: 3. length()

Explanation:

length() returns the number of characters in a string expression. It can also be used with supported binary values. size() is generally used for arrays or maps, while count() is an aggregation function. length() is useful for validation, data-quality checks, filtering, and identifying values that do not meet expected formatting requirements.

Question 183.

Which Spark SQL function can replace one set of characters with another set on a character-by-character basis?

1. translate()
   2. regexp_extract()
   3. split()
   4. concat_ws()

Correct Answer: 1. translate()

Explanation:

translate() replaces individual characters in a string according to corresponding characters in a replacement string. It is useful for simple character-level substitutions that do not require regular expressions. regexp_extract() extracts pattern matches, split() divides strings into arrays, and concat_ws() concatenates values using a separator. translate() is appropriate when deterministic character-by-character replacement is needed.

Question 184.

Which Spark SQL function can repeat a string a specified number of times?

1. reverse()
   2. concat()
   3. format_string()
   4. repeat()

Correct Answer: 4. repeat()

Explanation:

repeat() returns a string consisting of the input string repeated a specified number of times. It can be useful for formatting, generating test values, or building repeated patterns. reverse() reverses character order, concat() combines multiple expressions, and format_string() constructs formatted strings. repeat() is therefore the direct function when repeated string content is required.

Question 185.

Which Spark SQL function reverses the order of characters in a string?

1. reverse()
   2. substring()
   3. lower()
   4. translate()

Correct Answer: 1. reverse()

Explanation:

reverse() reverses the order of characters in a string expression. It can also operate on supported array expressions depending on context. substring() extracts part of a string, lower() changes letter case, and translate() replaces characters. reverse() is useful for transformations or validations that specifically require values to be processed in reverse order.

Question 186.

Which function can locate the position of a substring inside another string?

1. substring()
   2. instr()
   3. split()
   4. regexp_replace()

Correct Answer: 2. instr()

Explanation:

instr() returns the position of the first occurrence of a substring within another string. It can help test whether text contains a pattern or locate where a specific token begins. substring() extracts characters by position, split() divides text into an array, and regexp_replace() performs replacement based on a regular expression. instr() is therefore appropriate for simple substring-position searches.

Question 187.

Which Spark SQL function can format values according to a printf-style format string?

1. concat_ws()
   2. lit()
   3. format_string()
   4. regexp_extract()

Correct Answer: 3. format_string()

Explanation:

format_string() builds a formatted string using printf-style placeholders and one or more input expressions. It is useful for creating display values, labels, or structured text from multiple columns. concat_ws() concatenates values with a separator, lit() creates a constant expression, and regexp_extract() extracts regex matches. format_string() is therefore appropriate when output must follow a specific formatting pattern.

Question 188.

Which Spark SQL function can return the ASCII numeric value of the first character in a string?

1. char()
   2. length()
   3. encode()
   4. ascii()

Correct Answer: 4. ascii()

Explanation:

ascii() returns the numeric ASCII value of the first character in a string expression. It can be useful for character-level validation or transformations involving character codes. length() returns string length, encode() converts strings to binary using a character encoding, and char() serves a different purpose. ascii() is therefore the appropriate function when the numeric code of the first character is needed.

Question 189.

Which Spark SQL function can encode a string into binary data using a specified character encoding?

1. encode()
   2. decode()
   3. base64()
   4. unbase64()

Correct Answer: 1. encode()

Explanation:

encode() converts a string expression into binary data using a specified character encoding such as UTF-8. decode() performs the reverse operation by converting binary data into a string. base64() and unbase64() perform Base64 representation conversion rather than general character-set encoding. encode() is useful when interacting with binary formats or systems that require explicit character encoding.

Question 190.

Which Spark SQL function converts encoded binary data back into a string using a specified character set?

1. encode()
   2. decode()
   3. unbase64()
   4. translate()

Correct Answer: 2. decode()

Explanation:

decode() converts binary data into a string using a specified character encoding. It is the counterpart to encode(), which converts strings into binary form. unbase64() decodes Base64 content into binary rather than directly handling arbitrary character encodings, while translate() performs character substitutions. decode() is therefore the correct function when binary text data must be interpreted using a known encoding.

Question 191.

Which Spark SQL function converts binary data into a Base64-encoded string?

1. encode()
   2. decode()
   3. base64()
   4. hex()

Correct Answer: 3. base64()

Explanation:

base64() converts binary input into a Base64-encoded string representation. This can be useful when binary content must be represented safely as text. encode() handles character encoding, decode() converts binary data back to strings, and hex() produces hexadecimal representation. Base64 is an encoding format rather than encryption, so it should not be treated as a security mechanism.

Question 192.

Which Spark SQL function converts a Base64-encoded string back into binary data?

1. decode()
   2. base64()
   3. encode()
   4. unbase64()

Correct Answer: 4. unbase64()

Explanation:

unbase64() decodes a Base64-formatted string and returns the underlying binary value. base64() performs the opposite transformation. encode() and decode() work with character encodings rather than Base64 representation. unbase64() is useful when data has been transmitted or stored as Base64 text and must be converted back into its binary form for further processing.

Question 193.

Which Spark SQL function can convert binary data to a hexadecimal string?

1. hex()
   2. unhex()
   3. base64()
   4. crc32()

Correct Answer: 1. hex()

Explanation:

hex() converts supported input values into a hexadecimal string representation. It is useful for displaying binary data, identifiers, or encoded values in a compact textual form. unhex() performs the reverse transformation, base64() uses Base64 representation, and crc32() calculates a checksum. hex() is therefore the appropriate function when hexadecimal output is required.

Question 194.

Which function converts a hexadecimal string into binary data?

1. hex()
   2. unhex()
   3. decode()
   4. hash()

Correct Answer: 2. unhex()

Explanation:

unhex() converts a hexadecimal string back into its binary representation. It is the inverse of hex(). decode() interprets binary data using a character encoding, while hash() calculates a hash value. unhex() is useful when hexadecimal text from files, logs, or external systems must be restored to the underlying binary data for further processing.

Question 195.

Which Spark SQL function returns true when a string matches a regular-expression pattern?

1. regexp_extract()
   2. regexp_replace()
   3. rlike()
   4. split()

Correct Answer: 3. rlike()

Explanation:

rlike() evaluates whether a string matches a regular-expression pattern and returns a Boolean result. It is commonly used in filtering or validation when text must satisfy a pattern. regexp_extract() retrieves matching content, regexp_replace() performs replacement, and split() divides strings. rlike() is therefore appropriate when the goal is simply to test whether a regular expression matches.

Question 196.

Which method is commonly used to filter rows using an SQL-style condition string?

1. groupBy()
   2. selectExpr()
   3. withColumn()
   4. where()

Correct Answer: 4. where()

Explanation:

where() filters DataFrame rows using a Boolean expression or SQL-style condition string. It is functionally similar to filter(). groupBy() creates groups for aggregation, selectExpr() performs SQL-style projections, and withColumn() adds or replaces columns. where() is convenient for developers familiar with SQL syntax and is often used to restrict records before more expensive operations.

Question 197.

Which DataFrame method can return the first row of a DataFrame?

1. first()
   2. collect()
   3. head(0)
   4. limit(0)

Correct Answer: 1. first()

Explanation:

first() returns the first row of a DataFrame and triggers computation as an action. It is useful when only one row needs to be inspected. collect() returns all rows to the driver and may be unsafe for large datasets. head() can also retrieve rows depending on how it is called, but first() is the clearest direct method for returning a single first Row object.

Question 198.

Which method can return the first n rows from a DataFrame to the driver?

1. limit(n) only
   2. take(n)
   3. repartition(n)
   4. sample(n)

Correct Answer: 2. take(n)

Explanation:

take(n) is an action that returns the first n rows to the driver as Row objects. It is useful for inspecting a small subset without collecting the full dataset. limit(n) creates a DataFrame containing at most n rows but does not itself necessarily return them to the driver until an action is invoked. repartition() changes partition count, while sample() performs probabilistic sampling.

Question 199.

Which DataFrame method returns an approximate set of descriptive statistics including percentiles?

1. describe()
   2. explain()
   3. summary()
   4. printSchema()

Correct Answer: 3. summary()

Explanation:

summary() returns descriptive statistics and can include metrics such as count, mean, standard deviation, minimum, maximum, and selected percentiles. describe() provides a more basic set of descriptive statistics, explain() displays execution plans, and printSchema() shows schema structure. summary() is useful for quick exploratory profiling when broader statistical information is needed.

Question 200.

Which DataFrame method can return selected approximate quantiles for a numeric column?

1. describe()
   2. summary()
   3. corr()
   4. approxQuantile()

Correct Answer: 4. approxQuantile()

Explanation:

approxQuantile() computes approximate quantiles for one or more numeric columns using configurable probabilities and relative error. It is useful for large datasets where exact quantile calculation may be unnecessarily expensive. describe() and summary() provide general descriptive statistics, while corr() calculates correlation. approxQuantile() is therefore appropriate when specific percentile or quantile estimates are needed efficiently at scale.