answer:This task involves programming and the chosen language here will be Python due to its simplicity and high readability.```python# Define a python dictionary that stores the datacountry_info = { "country": "Brazil", "capital": "Brasília", "population": 208, "world_population": 7800 # values are in millions }# Calculate the population percentage pop_percentage = (country_info["population"] / country_info["world_population"]) * 100print(f'The population of {country_info["country"]} is {pop_percentage}% of the world population.')```In the code snippet above, a dictionary is first defined to hold the data. `country_info` has four keys: `country`, `capital`, `population`, and `world_population`, with their corresponding values. The population of Brazil as a percentage of the world population is computed by dividing the population of Brazil by the world population, and then multiplying by 100(to convert it to percentage). Finally, this percentage is printed using a formatted string which represents the solution to the problem.

answer:Most SharePoint developers adhere to the principle of not modifying anything in the 12 hive (also known as the SharePoint Root) directly. They advocate for deploying any changes through a SharePoint solution (.wsp file) for several reasons:1. Any change directly made to the files in the 12 hive is not supported by Microsoft. If something goes wrong, you can't turn to Microsoft for support. 2. Changing files directly in the 12 hive violates the best practices for SharePoint development, which advise against modifying any out-of-the-box files.3. The changes you make won't be tracked or versioned. If you update SharePoint or make other changes, you could lose your changes. 4. It makes it much more difficult to manage and replicate changes across multiple environments or servers.So, while there could be some cases where a quick change to the 12 hive might seem easier or faster, it is generally considered a bad practice that could result in a variety of problems down the line. It's better to spend the extra time to create a solution that can be managed, versioned, and supported properly. The use of features and solutions is encouraged for extending SharePoint functionality.

answer:Firstly, the helper function `merge_sort()` is not implemented. We do not need this function actually as Python provides built-in sorting functionalities. We can simply use `sorted()` function but should be careful with the order of the lists as `merge_sort()` is suggested which preserves the original order. Secondly, the `prime_check()` method is also not implemented. We need to create a function to check whether a number is prime or not. Recall that a prime number (greater than 1) has no positive divisors other than 1 and itself. The number 2 is the only even prime number as for all other even numbers, they are divisible by 2.Thirdly, throw is not a python keyword for raising exceptions. The correct keyword is raise. There is also no such thing as CustomError in Python, we can raise a ValueError instead.Lastly, the function doesn't actually return anything, it should return the `list_prime_even` and that were sorted in descending order. Here is the corrected code:```pythondef complex_prime_sort(l1: list, l2: list): def prime_check(x: int): if x < 2: return False for i in range(2, int(x 0.5) + 1): if x % i == 0: return False return True list_prime_even = [] for num in l1 + l2: if num < 0: raise ValueError("Negative number is not allowed") if num % 2 == 0 and prime_check(num): list_prime_even.append(num) return sorted(list_prime_even, reverse=True)print(complex_prime_sort([-1, 2, -4, 5, 6], [5, 3, -5, 2, -3, 3, 8, 0, 14, 4, -10]))```This code raises a ValueError for negative numbers. It filters both lists for even prime numbers, adding them to a list that is, upon completion, returned in descending order. In general, it adheres to the requested behavior.

answer:Here's how you can approach this problem:The first step is to create a function and make sure it can handle all test cases, including edge cases.The function will create two empty lists. It will then iterate over the elements in the input list and for each element, it will compute the square and the factorial and append these values to the respective lists.Let's code this:```pythonimport mathdef square_and_factorial(nums): squares = [num 2 for num in nums] factorials = [math.factorial(num) for num in nums] return squares, factorials```Now let's test the function with the given test case:```pythonnums = [2, 3, 4]squares, factorials = square_and_factorial(nums)print("Squares: ", squares)print("Factorials: ", factorials)```The output is:Squares: [4, 9, 16]Factorials: [2, 6, 24]Now let's test the function with some edge cases:```pythonnums = []squares, factorials = square_and_factorial(nums)print("Squares: ", squares)print("Factorials: ", factorials)```The output should be:Squares: []Factorials: []Now let's test with a single element:```pythonnums = [1]squares, factorials = square_and_factorial(nums)print("Squares: ", squares)print("Factorials: ", factorials)```The output will be:Squares: [1]Factorials: [1]

answer:Your Python script has numerous typographical and spelling errors, which appear like they might have been the product of OCR (Optical Character Recognition) software failing to accurately convert images of your code into text. Here is the corrected script:```pythonfrom bert_serving.client import BertClientimport psycopg2import pandas as pdfrom sklearn.model_selection import train_test_split# Connect to PostgreSQLdef create_conn(): return psycopg2.connect( database="yourdatabase", user="yourusername", password="yourpassword", host="localhost", # replace as required port="5432" # replace as required )# Fetch data from PostgreSQLdef fetch_data(conn): cur = conn.cursor() cur.execute("YOUR SQL QUERY HERE") # replace with your sql query rows = cur.fetchall() return pd.DataFrame(rows, columns=['col1', 'col2']) # replace as required# Preprocessingdef preprocess_data(df): # basic text pre-processing goes here return df# BERT featuresdef get_bert_features(df, column): bc = BertClient(check_length=False) return bc.encode(df[column].tolist())# Fetch and preprocess_dataconn = create_conn()df = fetch_data(conn)df = preprocess_data(df)# Get BERT featuresdf['features'] = get_bert_features(df, 'your text column')# Split into_train and_testtrain, test = train_test_split(df, test_size=0.2)```Please replace placeholders like "yourdatabase", "yourusername", "yourpassword", "localhost", "col1", "col2", and "your text column" with real identifiers based on your specific setup.Also, make sure that you have all the third-party packages (like bert_serving, psycopg2, pandas, sklearn) installed in your environment. You can do this by using pip:```bashpip install bert-serving-server bert-serving-client psycopg2-binary pandas scikit-learn```

answer:SELECT *FROM employeesWHERE (remuneration > 80000) AND (industry_experience < 2) AND (remuneration IS NOT NULL) AND (industry_experience IS NOT NULL); This query uses column-specific AND conditions to filter only for those rows that meet all our given criteria.However, regarding performance efficiency, it's crucial to mention that optimizing SQL entirely depends on several factors such as indexes, data types, DBMS used, amount of data, hardware resources, etc. For example, if "remuneration" and "industry_experience" columns are frequently used in WHERE conditions, creating indexes on these columns might improve the performance. It is always a good practice to monitor query performance and optimize based on requirements and performance analysis.