{"id":13437,"date":"2026-09-16T08:53:06","date_gmt":"2026-09-16T08:53:06","guid":{"rendered":"https:\/\/www.examlabs.com\/certification\/?p=13437"},"modified":"2026-09-16T08:53:06","modified_gmt":"2026-09-16T08:53:06","slug":"databricks-certified-data-engineer-professional-practice-test-questions-and-exam-dumps-part-17-q321-340","status":"publish","type":"post","link":"https:\/\/www.examlabs.com\/certification\/databricks-certified-data-engineer-professional-practice-test-questions-and-exam-dumps-part-17-q321-340\/","title":{"rendered":"Databricks Certified Data Engineer Professional Practice Test Questions and Exam Dumps Part 17 Q321-340"},"content":{"rendered":"

View Full <\/b>Databricks Certified Data Engineer Professional Exam Dumps<\/b><\/a> and Practice Test Dumps<\/b><\/p>\n

 <\/p>\n

Question 321. Which Spark SQL function returns the number of characters in a string?<\/b><\/h3>\n

1)<\/b> char_length()<\/span><\/p>\n

2)<\/b> string_size()<\/span><\/p>\n

3)<\/b> text_length()<\/span><\/p>\n

4)<\/b> count_chars()<\/span><\/p>\n

Answer: 1) <\/b>char_length()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>char_length()<\/span> function returns the number of characters contained in a string. It is useful when data engineers need to validate text length, create data-quality rules, or analyze textual attributes. For example, a pipeline can identify records whose customer names exceed a permitted length. Character length should be distinguished from byte length because certain Unicode characters may require multiple bytes for storage. Using a built-in SQL function allows Spark to perform the calculation as part of the distributed transformation rather than requiring custom application-side processing.<\/span><\/p>\n

Question 322. Which Spark SQL function returns the number of bytes in a string?<\/b><\/h3>\n

1)<\/b> byte_length()<\/span><\/p>\n

2)<\/b> bytes()<\/span><\/p>\n

3)<\/b> string_bytes()<\/span><\/p>\n

4)<\/b> length_bytes()<\/span><\/p>\n

Answer: 2) <\/b>byte_length()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>byte_length()<\/span> function returns the number of bytes required to represent a string. This differs from a character-counting function because characters can occupy different numbers of bytes depending on their encoding. This distinction can be important when validating storage limits, preparing data for external systems, or checking payload sizes. A string containing non-ASCII characters may therefore have a larger byte length than its character count. Data engineers should use the appropriate measurement depending on whether the requirement concerns characters or physical byte representation.<\/span><\/p>\n

Question 323. Which Spark SQL function removes leading and trailing whitespace from a string?<\/b><\/h3>\n

1)<\/b> clean()<\/span><\/p>\n

2)<\/b> trim()<\/span><\/p>\n

3)<\/b> strip_spaces()<\/span><\/p>\n

4)<\/b> remove_spaces()<\/span><\/p>\n

Answer: 2) <\/b>trim()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>trim()<\/span> function removes leading and trailing whitespace from a string. It is commonly used during data cleansing when source systems contain inconsistent spacing around names, identifiers, categories, or other textual values. For example, values such as <\/span>” Customer “<\/span> can be normalized before comparison or joining. <\/span>trim()<\/span> does not remove whitespace occurring in the middle of a string. Applying this transformation during ingestion or standardization can reduce problems with duplicate values and failed matches caused by accidental spaces.<\/span><\/p>\n

Question 324. Which Spark SQL function removes leading whitespace from a string?<\/b><\/h3>\n

1)<\/b> ltrim()<\/span><\/p>\n

2)<\/b> left_trim()<\/span><\/p>\n

3)<\/b> trim_left()<\/span><\/p>\n

4)<\/b> strip_left()<\/span><\/p>\n

Answer: 1) <\/b>ltrim()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>ltrim()<\/span> function removes whitespace from the beginning of a string while leaving trailing whitespace unchanged. It is useful when source data contains unwanted spaces before meaningful values but the transformation should not alter the end of the field. Data engineers can use <\/span>ltrim()<\/span> as part of standardization pipelines before filtering, joining, or validating text fields. If whitespace must be removed from both ends, <\/span>trim()<\/span> is more appropriate. Choosing the precise string function helps avoid unintentionally changing valid formatting in downstream data.<\/span><\/p>\n

Question 325. Which Spark SQL function removes trailing whitespace from a string?<\/b><\/h3>\n

1)<\/b> rtrim()<\/span><\/p>\n

2)<\/b> right_trim()<\/span><\/p>\n

3)<\/b> trim_right()<\/span><\/p>\n

4)<\/b> strip_end()<\/span><\/p>\n

Answer: 1) <\/b>rtrim()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>rtrim()<\/span> function removes whitespace from the end of a string. This can be useful when data imported from fixed-width files, legacy databases, or manually maintained sources contains unnecessary trailing spaces. Removing trailing whitespace can improve equality comparisons and prevent apparently identical values from being treated differently. Unlike <\/span>trim()<\/span>, <\/span>rtrim()<\/span> does not remove leading whitespace. Data engineers can select the function that matches the exact cleansing requirement, particularly when preserving part of the original formatting is important.<\/span><\/p>\n

Question 326. Which Spark SQL function repeats a string a specified number of times?<\/b><\/h3>\n

1)<\/b> repeat()<\/span><\/p>\n

2)<\/b> duplicate()<\/span><\/p>\n

3)<\/b> replicate()<\/span><\/p>\n

4)<\/b> copy_string()<\/span><\/p>\n

Answer: 1) <\/b>repeat()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>repeat()<\/span> function creates a new string by repeating an input string a specified number of times. Although it is less common in analytical transformations than functions such as <\/span>concat()<\/span> or <\/span>substring()<\/span>, it can be useful when generating formatted values, test data, or fixed textual patterns. The operation occurs as part of Spark’s expression evaluation, allowing it to be incorporated into distributed transformations. Data engineers should use it carefully when the repetition count can become large because the resulting string may significantly increase memory and output size.<\/span><\/p>\n

Question 327. Which Spark SQL function reverses the characters in a string?<\/b><\/h3>\n

1)<\/b> reverse()<\/span><\/p>\n

2)<\/b> reverse_string()<\/span><\/p>\n

3)<\/b> flip()<\/span><\/p>\n

4)<\/b> backward()<\/span><\/p>\n

Answer: 1) <\/b>reverse()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>reverse()<\/span> function returns a string with its character order reversed. It can also be used with supported collection types to reverse their element order. In data engineering, string reversal may be useful for specialized parsing, pattern processing, or transformation requirements. It is not normally required for routine cleansing, but understanding built-in functions helps avoid unnecessary custom UDF implementations. Built-in Spark functions are generally preferable when available because Spark can optimize them more effectively than arbitrary application-level code.<\/span><\/p>\n

Question 328. Which Spark SQL function returns the ASCII value of the first character in a string?<\/b><\/h3>\n

1)<\/b> ascii()<\/span><\/p>\n

2)<\/b> char_code()<\/span><\/p>\n

3)<\/b> ord()<\/span><\/p>\n

4)<\/b> character_code()<\/span><\/p>\n

Answer: 4) <\/b>ascii()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>ascii()<\/span> function returns the numeric ASCII value associated with the first character of a string. It can be useful when processing legacy text data, validating character patterns, or implementing transformations that depend on character codes. The function is specifically associated with ASCII character values, so it should not be treated as a general Unicode character conversion mechanism. Data engineers working with multilingual data should carefully consider character encoding requirements before relying on ASCII-based logic.<\/span><\/p>\n

Question 329. Which Spark SQL function returns the Unicode character corresponding to a numeric code?<\/b><\/h3>\n

1)<\/b> chr()<\/span><\/p>\n

2)<\/b> unicode_char()<\/span><\/p>\n

3)<\/b> code_to_char()<\/span><\/p>\n

4)<\/b> char_from_code()<\/span><\/p>\n

Answer: 1) <\/b>chr()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>chr()<\/span> function returns the character corresponding to a numeric code value. It can be useful when transformations need to construct text programmatically from character codes. This can support specialized parsing, formatting, and data-generation scenarios. Data engineers should distinguish this operation from hashing or encoding functions because it produces a character rather than a secure digest or transport representation. When working with international text, understanding the expected character encoding and supported code values is important for producing consistent results.<\/span><\/p>\n

Question 330. Which Spark SQL function finds the position of a substring within another string?<\/b><\/h3>\n

1)<\/b> locate()<\/span><\/p>\n

2)<\/b> position_of()<\/span><\/p>\n

3)<\/b> find_text()<\/span><\/p>\n

4)<\/b> search_string()<\/span><\/p>\n

Answer: 1) <\/b>locate()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>locate()<\/span> function searches for a substring within another string and returns its position. It is useful when data engineers need to identify whether a particular pattern occurs within a text field or determine where parsing should begin. For example, a pipeline can locate a delimiter before extracting a portion of an identifier. The function is preferable to writing custom parsing code for straightforward substring-position requirements. Data engineers should also account for the indexing convention used by the function when interpreting its returned position.<\/span><\/p>\n

Question 331. Which Spark SQL function extracts a substring using a starting position and length?<\/b><\/h3>\n

1)<\/b> substring()<\/span><\/p>\n

2)<\/b> slice_text()<\/span><\/p>\n

3)<\/b> extract_string()<\/span><\/p>\n

4)<\/b> text_segment()<\/span><\/p>\n

Answer: 1) <\/b>substring()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>substring()<\/span> function extracts part of a string based on a starting position and a specified length. It is frequently used to parse fixed-format identifiers, product codes, dates stored as text, and other structured strings. Using <\/span>substring()<\/span> within Spark SQL allows the transformation to run across distributed data without requiring a Python loop. Data engineers should verify the function’s position semantics and ensure that the source strings follow the expected format. For irregular text, functions such as regular expressions may be more suitable.<\/span><\/p>\n

Question 332. Which Spark SQL function extracts the portion of a string located before a specified delimiter?<\/b><\/h3>\n

1)<\/b> substring_index()<\/span><\/p>\n

2)<\/b> split_before()<\/span><\/p>\n

3)<\/b> prefix_until()<\/span><\/p>\n

4)<\/b> delimiter_extract()<\/span><\/p>\n

Answer: 3) <\/b>substring_index()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>substring_index()<\/span> function returns a substring based on occurrences of a specified delimiter. It is particularly useful for parsing structured text such as email addresses, domain names, file paths, or compound identifiers. A positive count extracts content from the beginning of the string, while a negative count works from the opposite side. This provides a convenient alternative to more complicated expressions when the input follows a predictable delimiter-based structure. Data engineers should validate assumptions about delimiter frequency before applying the function broadly.<\/span><\/p>\n

Question 333. Which Spark SQL function returns the first non-null value from a list of expressions?<\/b><\/h3>\n

1)<\/b> coalesce()<\/span><\/p>\n

2)<\/b> first_valid()<\/span><\/p>\n

3)<\/b> non_null()<\/span><\/p>\n

4)<\/b> first_value()<\/span><\/p>\n

Answer: 1) <\/b>coalesce()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>coalesce()<\/span> function returns the first expression in its argument list that is not null. It is useful for handling fallback values when a preferred source column is unavailable. For example, a pipeline might select a primary contact number and use a secondary number when the first value is null. <\/span>coalesce()<\/span> is also useful for providing default values during transformations. It should not be confused with the DataFrame <\/span>coalesce()<\/span> operation, which changes the number of partitions; the SQL function deals with values rather than Spark partitions.<\/span><\/p>\n

Question 334. Which Spark SQL function returns the minimum value among multiple expressions?<\/b><\/h3>\n

1)<\/b> least()<\/span><\/p>\n

2)<\/b> minimum()<\/span><\/p>\n

3)<\/b> min_value()<\/span><\/p>\n

4)<\/b> smallest()<\/span><\/p>\n

Answer: 1) <\/b>least()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>least()<\/span> function compares multiple expressions and returns the smallest value. It is useful when a row contains several candidate values and the transformation must select the minimum one. For example, a data engineer may compare multiple date or numeric fields to determine the earliest applicable value. This is different from the aggregate <\/span>min()<\/span> function, which calculates a minimum across rows in a group or dataset. Understanding the distinction helps prevent incorrect query results when performing row-level versus aggregate calculations.<\/span><\/p>\n

Question 335. Which Spark SQL function returns the maximum value among multiple expressions?<\/b><\/h3>\n

1)<\/b> greatest()<\/span><\/p>\n

2)<\/b> maximum()<\/span><\/p>\n

3)<\/b> max_value()<\/span><\/p>\n

4)<\/b> largest()<\/span><\/p>\n

Answer: 1) <\/b>greatest()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>greatest()<\/span> function compares multiple expressions within a row and returns the highest value. It is useful for selecting the latest date, largest measurement, or highest value from several candidate columns. This is a row-level operation and should not be confused with the aggregate <\/span>max()<\/span> function, which finds the maximum across rows. Data engineers can use <\/span>greatest()<\/span> when business logic requires comparison among multiple attributes belonging to the same record. Correct handling of null values should also be considered when designing the transformation.<\/span><\/p>\n

Question 336. Which Spark SQL function returns a random value for each row?<\/b><\/h3>\n

1)<\/b> random_value()<\/span><\/p>\n

2)<\/b> rand()<\/span><\/p>\n

3)<\/b> random()<\/span><\/p>\n

4)<\/b> generate_random()<\/span><\/p>\n

Answer: 2) <\/b>rand()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>rand()<\/span> function generates pseudorandom values, commonly producing values between zero and one. It can be useful for sampling, testing, generating randomized data, or assigning records to randomized groups. A seed can be supplied when reproducible pseudorandom behavior is required. Because random expressions do not represent stable business identifiers, they should not be used as permanent keys unless the workflow explicitly controls and persists the generated values. Data engineers should also understand that random generation can affect reproducibility when no fixed seed is supplied.<\/span><\/p>\n

Question 337. Which Spark SQL function calculates the natural logarithm of a numeric expression?<\/b><\/h3>\n

1)<\/b> ln()<\/span><\/p>\n

2)<\/b> natural_log()<\/span><\/p>\n

3)<\/b> logarithm()<\/span><\/p>\n

4)<\/b> logn()<\/span><\/p>\n

Answer: 2) <\/b>ln()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>ln()<\/span> function calculates the natural logarithm of a numeric expression. It can be useful in statistical transformations, mathematical modeling, normalization techniques, and analytical calculations. The input generally needs to be appropriate for logarithmic computation, so data engineers should account for zero, negative, or invalid values before applying the function. Using Spark’s built-in mathematical expression allows the calculation to be distributed efficiently across records. When a different logarithm base is required, another suitable logarithmic function or transformation should be selected.<\/span><\/p>\n

Question 338. Which Spark SQL function calculates the base-10 logarithm of a numeric expression?<\/b><\/h3>\n

1)<\/b> log10()<\/span><\/p>\n

2)<\/b> base10()<\/span><\/p>\n

3)<\/b> decimal_log()<\/span><\/p>\n

4)<\/b> ten_log()<\/span><\/p>\n

Answer: 1) <\/b>log10()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>log10()<\/span> function calculates the base-10 logarithm of a numeric expression. It is commonly used in analytical calculations where values span several orders of magnitude, such as certain measurement, statistical, or data-normalization tasks. Before applying logarithmic transformations, data engineers should validate that the input values are within a mathematically valid range. Built-in Spark functions allow these calculations to execute as part of distributed query plans. Using the correct logarithm base is important because <\/span>log10()<\/span> produces different results from the natural logarithm.<\/span><\/p>\n

Question 339. Which Spark SQL function returns the square root of a numeric expression?<\/b><\/h3>\n

1)<\/b> sqrt()<\/span><\/p>\n

2)<\/b> square_root()<\/span><\/p>\n

3)<\/b> root()<\/span><\/p>\n

4)<\/b> sqrt_value()<\/span><\/p>\n

Answer: 1) <\/b>sqrt()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>sqrt()<\/span> function calculates the square root of a numeric expression. It can be used in statistical calculations, mathematical transformations, distance formulas, and analytical models. Data engineers should ensure that the input values are valid for the operation and handle unsuitable values according to the requirements of the pipeline. Using the built-in function avoids unnecessary custom UDF logic and allows Spark to incorporate the calculation directly into the execution plan. This can be especially useful when the transformation needs to be applied to very large datasets.<\/span><\/p>\n

Question 340. Which Spark SQL function returns the absolute value of a numeric expression?<\/b><\/h3>\n

1)<\/b> abs()<\/span><\/p>\n

2)<\/b> absolute()<\/span><\/p>\n

3)<\/b> positive()<\/span><\/p>\n

4)<\/b> magnitude()<\/span><\/p>\n

Answer: 1) <\/b>abs()<\/b><\/p>\n

Explanation:<\/b><\/p>\n

The <\/span>abs()<\/span> function returns the absolute value of a numeric expression, converting negative values into their corresponding positive magnitude while leaving nonnegative values unchanged. It is useful when the direction of a difference is irrelevant and only the magnitude matters. Common examples include calculating absolute deviations, differences between measurements, or distance-related values. Because <\/span>abs()<\/span> is a built-in Spark SQL function, it can be applied efficiently across distributed data. Data engineers should still consider the numeric type and potential overflow behavior for very large values.<\/span><\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

View Full Databricks Certified Data Engineer Professional Exam Dumps and Practice Test Dumps   Question 321. Which Spark SQL function returns the number of characters in a string? 1) char_length() 2) string_size() 3) text_length() 4) count_chars() Answer: 1) char_length() Explanation: The char_length() function returns the number of characters contained in a string. It is useful […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":[],"categories":[1648,1647],"tags":[],"_links":{"self":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13437"}],"collection":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/comments?post=13437"}],"version-history":[{"count":1,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13437\/revisions"}],"predecessor-version":[{"id":13454,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/posts\/13437\/revisions\/13454"}],"wp:attachment":[{"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/media?parent=13437"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/categories?post=13437"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.examlabs.com\/certification\/wp-json\/wp\/v2\/tags?post=13437"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}