Data Operations Manager Test

As a DBA, you receive an alert notifying you that the production database has gone offline due to a severe issue. Fortunately, you have a proper backup strategy in place. The backups are performed as follows:

1. Full database backup every Sunday at 2:00 AM.
2. Differential backup every day at 2:00 AM, except Sunday.
3. Transaction log backup every hour.

Today is Wednesday, and the failure occurred at 10:15 AM. You have the following backup files available:

1. Full backup: Full_Backup_Sun.bak taken on Sunday 2:00 AM.
2. Differential backups: Diff_Backup_Mon.bak, Diff_Backup_Tue.bak, Diff_Backup_Wed.bak taken at 2:00 AM on their respective days.
3. Transaction log backups: Hourly backups from Sunday 3:00 AM until Wednesday 10:00 AM, like TLog_Backup_Wed_09.bak, TLog_Backup_Wed_10.bak.

Given the RPO (Recovery Point Objective) of 15 minutes, which of the following sequences of restore operations would ensure minimal data loss?

A: Full_Backup_Sun.bak, Diff_Backup_Wed.bak, then all Transaction Log backups from Wednesday.

B: Full_Backup_Sun.bak, Diff_Backup_Tue.bak, then all Transaction Log backups from Tuesday and Wednesday.

C: Full_Backup_Sun.bak, Diff_Backup_Wed.bak, then Transaction Log backups from Wednesday 2:00 AM to 10:00 AM.

D: Full_Backup_Sun.bak, then all Transaction Log backups from Sunday to Wednesday 10:00 AM.

E: Full_Backup_Sun.bak, Diff_Backup_Mon.bak, Diff_Backup_Tue.bak, Diff_Backup_Wed.bak, then Transaction Log backups from Wednesday 2:00 AM to 10:00 AM.

You are managing a SQL Server database for a large e-commerce platform. The database contains the following tables:

Users often run a query to retrieve all orders from a specific date along with customer details and a breakdown of each order. Lately, this query has been performing poorly, especially on days with a high volume of orders.

Given this schema, which of the following changes would MOST LIKELY enhance the performance of this query?

A: Create a non-clustered index on Orders(OrderDate, OrderID) and a clustered index on OrderDetails(OrderID).
B: Create a clustered index on Orders(CustomerID, OrderDate) and a non-clustered index on OrderDetails(ProductName).
C: Increase the size of the OrderDetails(ProductName) column and add more RAM to the SQL Server machine.
D: Create a clustered index on Orders(OrderDate) and a non-clustered index on OrderDetails(OrderID, Quantity).
E: Partition the Orders table on OrderDate and create a non-clustered index on OrderDetails(DetailID, Price).

You are managing a SQL Server instance that is experiencing performance degradation. After some analysis, you realize that the TempDB is under heavy stress due to numerous long-running transactions. Users have reported that some SELECT queries on a large table, named SalesData, are slower than expected.

You consider implementing Snapshot Isolation to mitigate blocking issues. You're aware that Snapshot Isolation uses TempDB to store row versions.

Given the situation, which combination of actions will help alleviate the stress on TempDB and enhance the performance of SELECT queries on SalesData?

A: Move TempDB to a faster storage subsystem and enable Snapshot Isolation for SalesData.
B: Increase the number of TempDB data files, shrink TempDB size, and enable Snapshot Isolation for the database.
C: Implement Read Committed Snapshot Isolation (RCSI) for the database and partition the SalesData table.
D: Reduce the TempDB size, implement table partitioning on SalesData, and enable Read Uncommitted isolation level for the SELECT queries.
E: Create a non-clustered index on frequently queried columns of SalesData and enable row versioning for the entire database.

You are a DBA at a large company managing an SQL Server database which is crucial for daily operations. The database is configured with the Full recovery model. The database is experiencing considerable transaction log growth during business hours, which is impacting the disk space and performance.

The following operations are performed on this database:

1. A large ETL process that runs every night, which transforms and loads data into several tables.
2. A data archiving job that runs every night, which removes old data from several tables.
3. Frequent read/write operations during the day as part of normal business operations.

Given this scenario, which of the following strategies could help manage the transaction log growth effectively?

A: Switch to the Simple recovery model.
B: Schedule frequent log backup and cleanups during business hours.
C: Shrink the transaction log file size during business hours.
D: Increase the database file size.

A data engineer is tasked with merging and transforming data from two sources for a business analytics report. Source 1 is a SQL database 'Employee' with fields EmployeeID (int), Name (varchar), DepartmentID (int), and JoinDate (date). Source 2 is a CSV file 'Department' with fields DepartmentID (int), DepartmentName (varchar), and Budget (float). The objective is to create a summary table that lists EmployeeID, Name, DepartmentName, and YearsInCompany. The YearsInCompany should be calculated based on the JoinDate and the current date, rounded down to the nearest whole number. Consider the following initial SQL query:

Which of the following modifications ensures accurate data transformation as per the requirements?

A: Change FLOOR to CEILING in the calculation of YearsInCompany.
B: Add WHERE e.JoinDate IS NOT NULL before the JOIN clause.
C: Replace JOIN with LEFT JOIN and use COALESCE(d.DepartmentName, 'Unknown').
D: Change the YearsInCompany calculation to YEAR(CURRENT_DATE) - YEAR(e.JoinDate).
E: Use DATEDIFF(YEAR, e.JoinDate, CURRENT_DATE) for YearsInCompany calculation.

Jaylo is hired as Data warehouse engineer at Affflex Inc. Jaylo is tasked with designing an ETL process for loading data from SQL server database into a large fact table. Here are the specifications of the system:

1. Orders data from SQL to be stored in fact table in the warehouse each day with prior day’s order data
2. Loading new data must take as less time as possible
3. Remove data that is more then 2 years old
4. Ensure the data loads correctly
5. Minimize record locking and impact on transaction log

Which of the following should be part of Jaylo’s ETL design?

A: Partition the destination fact table by date
B: Partition the destination fact table by customer
C: Insert new data directly into fact table
D: Delete old data directly from fact table
E: Use partition switching and staging table to load new data
F: Use partition switching and staging table to remove old data

In an ETL process designed for a retail company, a complex SQL transformation is applied to the 'Sales' table. The 'Sales' table has fields SaleID, ProductID, Quantity, SaleDate, and Price. The goal is to generate a report that shows the total sales amount and average sale amount per product, aggregated monthly. The following SQL code snippet is used in the transformation step:

What specific function does this SQL code perform in the context of the ETL process, and how does it contribute to the reporting goal?

A: The code calculates the total and average sales amount for each product annually.
B: It aggregates sales data by month and product, computing total and average sales amounts.
C: This query generates a daily breakdown of sales, both total and average, for each product.
D: The code is designed to identify the best-selling products on a monthly basis by sales amount.
E: It calculates the overall sales and average price per product, without considering the time dimension.

Silverman Sachs is a trading firm and deals with daily trade data for various stocks. They have the following fact table in their data warehouse:

Table: Trades
Indexes: None
Columns: TradeID, TradeDate, Open, Close, High, Low, Volume

Here are three common queries that are run on the data:

Dhavid Polomon is hired as an ETL Developer and is tasked with implementing an indexing strategy for the Trades fact table. Here are the specifications of the indexing strategy:

- All three common queries must use a columnstore index
- Minimize number of indexes
- Minimize size of indexes

Which of the following strategies should Dhavid pick:

A: Create three columnstore indexes: 
1. Containing TradeDate and Close
2. Containing TradeDate, High and Low
3. Container TradeDate and Volume

B: Create two columnstore indexes:
1. Containing TradeID, TradeDate, Volume and Close
2. Containing TradeID, TradeDate, High and Low

C: Create one columnstore index that contains TradeDate, Close, High, Low and Volume

D: Create one columnstore index that contains TradeID, Close, High, Low, Volume and Trade Date

You are designing a data model for a healthcare system with the following requirements:

A: A separate table for each entity with foreign keys as specified, and a DoctorPatient table linking Doctors to Patients.
B: A separate table for each entity with foreign keys as specified, without additional tables.
C: A combined PatientDoctor table replacing Patient and Doctor, and separate tables for Appointment and Prescription.
D: A separate table for each entity with foreign keys, and a PatientPrescription table to track prescriptions directly linked to patients.
E: A single table combining Patient, Doctor, Appointment, and Prescription into one.
F: A separate table for each entity with foreign keys as specified, and an AppointmentDetails table linking Appointments to Prescriptions.

Look at the given ER diagram. What do you think is the least number of tables we would need to represent M, N, P, R1 and R2?

Consider a healthcare database with a table named PatientRecords that stores patient visit information. The table has the following attributes:

- VisitID
- PatientID
- PatientName
- DoctorID
- DoctorName
- VisitDate
- Diagnosis
- Treatment
- TreatmentCost

In this table:

- Each VisitID uniquely identifies a patient's visit and is associated with one PatientID.
- PatientID is associated with exactly one PatientName.
- Each DoctorID is associated with a unique DoctorName.
- TreatmentCost is a fixed cost based on the Treatment.

Evaluating the PatientRecords table, which of the following statements most accurately describes its normalization state and the required actions for higher normalization?

A: The table is in 1NF. To achieve 2NF, remove partial dependencies by separating Patient information (PatientID, PatientName) and Doctor information (DoctorID, DoctorName) into different tables.
B: The table is in 2NF. To achieve 3NF, remove transitive dependencies by creating separate tables for Patients (PatientID, PatientName), Doctors (DoctorID, DoctorName), and Visits (VisitID, PatientID, DoctorID, VisitDate, Diagnosis, Treatment, TreatmentCost).
C: The table is in 3NF. To achieve BCNF, adjust for functional dependencies such as moving DoctorName to a separate Doctors table.
D: The table is in 1NF. To achieve 3NF, create separate tables for Patients, Doctors, and Visits, and remove TreatmentCost as it is a derived attribute.
E: The table is in 2NF. To achieve 4NF, address any multi-valued dependencies by separating Visit details and Treatment details.
F: The table is in 3NF. To achieve 4NF, remove multi-valued dependencies related to VisitID.

Based on the ER diagram, which of the following statements is accurate and requires specific knowledge of the ER diagram's details?

A: A Student can major in multiple Departments.
B: An Instructor can belong to multiple Departments.
C: A Course can be offered by multiple Departments.
D: Enrollment records can link a Student to multiple Courses in a single semester.
E: Each Course must be associated with an Enrollment record.
F: A Department can offer courses without having any instructors.

You are working on a Linux system and have recently installed a new program named myprogram. The executable is located in /opt/myprogram/bin/. You want to be able to run this program from any directory in your shell without specifying the full path.

You executed the following command:
export PATH="/opt/myprogram/bin"

However, when you try to run the program using myprogram, you get the following error message:
-bash: myprogram: command not found

Which of the following commands can fix this issue and allow you to run the program?

A: export PATH=$PATH:/opt/myprogram/bin/
B: export PATH="/opt/myprogram:$PATH"
C: export PATH="/opt/myprogram/bin:$PATH"
D: ln -s /opt/myprogram/bin/myprogram /usr/local/bin/myprogram

Consider the following directory structure:

You start at /home/user and execute the following commands:

What will be the resulting directory structure?

How many times will the following code will print "Mellow Yellow"?

Our software engineering intern, Wu is looking to use port 4545 on localhost to connect to a remote server called woot.bananas.com on port 80. Which command would you recommend for this?