Practice Free Associate Data Practitioner Exam Online Questions
Question #25
Which types of Zone are available for Add Zone? (Select all that apply.)
- A . Authoritative Zone
- B . Forward Zone
- C . Primary Zone
- D . Delegation
Correct Answer: A, B, C, D
A, B, C, D
Explanation:
Comprehensive and Detailed In-Depth
In NIOS Grid Manager (Data Management > DNS > Add Zone), administrators can create various zone types to manage DNS resolution.
All listed options are valid:
A (Authoritative Zone): A zone where the Infoblox appliance is the authoritative source for DNS records (e.g., example.com with A, MX records). Correct.
B (Forward Zone): A zone configured to forward queries to external DNS servers (e.g., forwarding "internal.com" to a corporate DNS). Correct.
C (Primary Zone): Often synonymous with Authoritative Zone in Infoblox, it’s a master zone hosting original DNS data (distinct from secondary zones). Correct.
D (Delegation): A zone delegated to another name server (e.g., "sub.example.com" delegated to different NS records). Correct.
Clarification: In NIOS, "Authoritative" and "Primary" are sometimes used interchangeably, but both are options in the Add Zone wizard, alongside Forward and Delegation zones.
Practical Example: In an INE lab, you might add an Authoritative Zone for "lab.com," a Forward Zone for external lookups, and a Delegation for a subdomain, testing DNS troubleshooting across these types.
Reference: Infoblox NIOS Administrator Guide C DNS Zone Management; INE Course Content: NIOS DDI DNS Troubleshooting.
A, B, C, D
Explanation:
Comprehensive and Detailed In-Depth
In NIOS Grid Manager (Data Management > DNS > Add Zone), administrators can create various zone types to manage DNS resolution.
All listed options are valid:
A (Authoritative Zone): A zone where the Infoblox appliance is the authoritative source for DNS records (e.g., example.com with A, MX records). Correct.
B (Forward Zone): A zone configured to forward queries to external DNS servers (e.g., forwarding "internal.com" to a corporate DNS). Correct.
C (Primary Zone): Often synonymous with Authoritative Zone in Infoblox, it’s a master zone hosting original DNS data (distinct from secondary zones). Correct.
D (Delegation): A zone delegated to another name server (e.g., "sub.example.com" delegated to different NS records). Correct.
Clarification: In NIOS, "Authoritative" and "Primary" are sometimes used interchangeably, but both are options in the Add Zone wizard, alongside Forward and Delegation zones.
Practical Example: In an INE lab, you might add an Authoritative Zone for "lab.com," a Forward Zone for external lookups, and a Delegation for a subdomain, testing DNS troubleshooting across these types.
Reference: Infoblox NIOS Administrator Guide C DNS Zone Management; INE Course Content: NIOS DDI DNS Troubleshooting.
Question #25
Which types of Zone are available for Add Zone? (Select all that apply.)
- A . Authoritative Zone
- B . Forward Zone
- C . Primary Zone
- D . Delegation
Correct Answer: A, B, C, D
A, B, C, D
Explanation:
Comprehensive and Detailed In-Depth
In NIOS Grid Manager (Data Management > DNS > Add Zone), administrators can create various zone types to manage DNS resolution.
All listed options are valid:
A (Authoritative Zone): A zone where the Infoblox appliance is the authoritative source for DNS records (e.g., example.com with A, MX records). Correct.
B (Forward Zone): A zone configured to forward queries to external DNS servers (e.g., forwarding "internal.com" to a corporate DNS). Correct.
C (Primary Zone): Often synonymous with Authoritative Zone in Infoblox, it’s a master zone hosting original DNS data (distinct from secondary zones). Correct.
D (Delegation): A zone delegated to another name server (e.g., "sub.example.com" delegated to different NS records). Correct.
Clarification: In NIOS, "Authoritative" and "Primary" are sometimes used interchangeably, but both are options in the Add Zone wizard, alongside Forward and Delegation zones.
Practical Example: In an INE lab, you might add an Authoritative Zone for "lab.com," a Forward Zone for external lookups, and a Delegation for a subdomain, testing DNS troubleshooting across these types.
Reference: Infoblox NIOS Administrator Guide C DNS Zone Management; INE Course Content: NIOS DDI DNS Troubleshooting.
A, B, C, D
Explanation:
Comprehensive and Detailed In-Depth
In NIOS Grid Manager (Data Management > DNS > Add Zone), administrators can create various zone types to manage DNS resolution.
All listed options are valid:
A (Authoritative Zone): A zone where the Infoblox appliance is the authoritative source for DNS records (e.g., example.com with A, MX records). Correct.
B (Forward Zone): A zone configured to forward queries to external DNS servers (e.g., forwarding "internal.com" to a corporate DNS). Correct.
C (Primary Zone): Often synonymous with Authoritative Zone in Infoblox, it’s a master zone hosting original DNS data (distinct from secondary zones). Correct.
D (Delegation): A zone delegated to another name server (e.g., "sub.example.com" delegated to different NS records). Correct.
Clarification: In NIOS, "Authoritative" and "Primary" are sometimes used interchangeably, but both are options in the Add Zone wizard, alongside Forward and Delegation zones.
Practical Example: In an INE lab, you might add an Authoritative Zone for "lab.com," a Forward Zone for external lookups, and a Delegation for a subdomain, testing DNS troubleshooting across these types.
Reference: Infoblox NIOS Administrator Guide C DNS Zone Management; INE Course Content: NIOS DDI DNS Troubleshooting.
Question #3
You are designing a BigQuery data warehouse with a team of experienced SQL developers. You need to recommend a cost-effective, fully-managed, serverless solution to build ELT processes with SQL pipelines. Your solution must include source code control, environment parameterization, and data quality checks.
What should you do?
- A . Use Cloud Data Fusion to visually design and manage the pipelines.
- B . Use Dataform to build, orchestrate, and monitor the pipelines.
- C . Use Dataproc to run MapReduce jobs for distributed data processing.
- D . Use Cloud Composer to orchestrate and run data workflows.
Correct Answer: B
B
Explanation:
Comprehensive and Detailed In-Depth
The solution must support SQL-based ELT, be serverless and cost-effective, and include advanced features like version control and quality checks. Let’s dive in:
Option A: Cloud Data Fusion is a visual ETL tool, not SQL-centric (uses plugins), and isn’t fully serverless (requires instance management). It lacks native source code control and parameterization.
Option B: Dataform is a serverless, SQL-based ELT platform for BigQuery. It uses SQLX scripts, integrates with Git for version control, supports environment variables (parameterization), and offers assertions for data quality―all meeting the requirements cost-effectively.
Option C: Dataproc is for Spark/MapReduce, not SQL ELT, and requires cluster management, contradicting serverless and cost goals.
Option D: Cloud Composer orchestrates workflows (Python DAGs), not SQL pipelines natively. It’s
managed but not optimized for ELT within BigQuery alone.
Why B is Best: Dataform leverages your team’s SQL skills, runs in BigQuery (no extra infrastructure), and provides Git integration (e.g., GitHub), parameterization (e.g., DECLARE env STRING DEFAULT ‘prod’;), and quality checks (e.g., assert ‘no_nulls’ AS SELECT COUNT(*) FROM table WHERE col IS NULL). It’s the perfect fit.
Extract from Google Documentation: From "Dataform Overview"
(https://cloud.google.com/dataform/docs): "Dataform is a fully managed, serverless solution for building SQL-based ELT pipelines in BigQuery, with built-in Git version control, environment parameterization, and data quality assertions for robust data warehouse management."
Reference: Google Cloud Documentation – "Dataform" (https://cloud.google.com/dataform).
B
Explanation:
Comprehensive and Detailed In-Depth
The solution must support SQL-based ELT, be serverless and cost-effective, and include advanced features like version control and quality checks. Let’s dive in:
Option A: Cloud Data Fusion is a visual ETL tool, not SQL-centric (uses plugins), and isn’t fully serverless (requires instance management). It lacks native source code control and parameterization.
Option B: Dataform is a serverless, SQL-based ELT platform for BigQuery. It uses SQLX scripts, integrates with Git for version control, supports environment variables (parameterization), and offers assertions for data quality―all meeting the requirements cost-effectively.
Option C: Dataproc is for Spark/MapReduce, not SQL ELT, and requires cluster management, contradicting serverless and cost goals.
Option D: Cloud Composer orchestrates workflows (Python DAGs), not SQL pipelines natively. It’s
managed but not optimized for ELT within BigQuery alone.
Why B is Best: Dataform leverages your team’s SQL skills, runs in BigQuery (no extra infrastructure), and provides Git integration (e.g., GitHub), parameterization (e.g., DECLARE env STRING DEFAULT ‘prod’;), and quality checks (e.g., assert ‘no_nulls’ AS SELECT COUNT(*) FROM table WHERE col IS NULL). It’s the perfect fit.
Extract from Google Documentation: From "Dataform Overview"
(https://cloud.google.com/dataform/docs): "Dataform is a fully managed, serverless solution for building SQL-based ELT pipelines in BigQuery, with built-in Git version control, environment parameterization, and data quality assertions for robust data warehouse management."
Reference: Google Cloud Documentation – "Dataform" (https://cloud.google.com/dataform).
Question #4
You need to design a data pipeline that ingests data from CSV, Avro, and Parquet files into Cloud Storage. The data includes raw user input. You need to remove all malicious SQL injections before storing the data in BigQuery.
Which data manipulation methodology should you choose?
- A . EL
- B . ELT
- C . ETL
- D . ETLT
Correct Answer: C
C
Explanation:
The ETL (Extract, Transform, Load) methodology is the best approach for this scenario because it allows you to extract data from the files, transform it by applying the necessary data cleansing (including removing malicious SQL injections), and then load the sanitized data into BigQuery. By transforming the data before loading it into BigQuery, you ensure that only clean and safe data is stored, which is critical for security and data quality.
C
Explanation:
The ETL (Extract, Transform, Load) methodology is the best approach for this scenario because it allows you to extract data from the files, transform it by applying the necessary data cleansing (including removing malicious SQL injections), and then load the sanitized data into BigQuery. By transforming the data before loading it into BigQuery, you ensure that only clean and safe data is stored, which is critical for security and data quality.
Question #5
You are designing a pipeline to process data files that arrive in Cloud Storage by 3:00 am each day.
Data processing is performed in stages, where the output of one stage becomes the input of the next.
Each stage takes a long time to run. Occasionally a stage fails, and you have to address the problem. You need to ensure that the final output is generated as quickly as possible.
What should you do?
- A . Design a Spark program that runs under Dataproc. Code the program to wait for user input when an error is detected. Rerun the last action after correcting any stage output data errors.
- B . Design the pipeline as a set of PTransforms in Dataflow. Restart the pipeline after correcting any stage output data errors.
- C . Design the workflow as a Cloud Workflow instance. Code the workflow to jump to a given stage based on an input parameter. Rerun the workflow after correcting any stage output data errors.
- D . Design the processing as a directed acyclic graph (DAG) in Cloud Composer. Clear the state of the failed task after correcting any stage output data errors.
Correct Answer: D
D
Explanation:
Using Cloud Composer to design the processing pipeline as a Directed Acyclic Graph (DAG) is the most suitable approach because:
Fault tolerance: Cloud Composer (based on Apache Airflow) allows for handling failures at specific stages. You can clear the state of a failed task and rerun it without reprocessing the entire pipeline.
Stage-based processing: DAGs are ideal for workflows with interdependent stages where the output of one stage serves as input to the next.
Efficiency: This approach minimizes downtime and ensures that only failed stages are rerun, leading to faster final output generation.
D
Explanation:
Using Cloud Composer to design the processing pipeline as a Directed Acyclic Graph (DAG) is the most suitable approach because:
Fault tolerance: Cloud Composer (based on Apache Airflow) allows for handling failures at specific stages. You can clear the state of a failed task and rerun it without reprocessing the entire pipeline.
Stage-based processing: DAGs are ideal for workflows with interdependent stages where the output of one stage serves as input to the next.
Efficiency: This approach minimizes downtime and ensures that only failed stages are rerun, leading to faster final output generation.
Question #6
You are using your own data to demonstrate the capabilities of BigQuery to your organization’s leadership team. You need to perform a one-time load of the files stored on your local machine into BigQuery using as little effort as possible.
What should you do?
- A . Write and execute a Python script using the BigQuery Storage Write API library.
- B . Create a Dataproc cluster, copy the files to Cloud Storage, and write an Apache Spark job using the spark-bigquery-connector.
- C . Execute the bq load command on your local machine.
- D . Create a Dataflow job using the Apache Beam FileIO and BigQueryIO connectors with a local runner.
Correct Answer: C
C
Explanation:
Comprehensive and Detailed In-Depth
A one-time load with minimal effort points to a simple, out-of-the-box tool. The files are local, so the solution must bridge on-premises to BigQuery easily.
Option A: A Python script with the Storage Write API requires coding, setup (authentication, libraries), and debugging―more effort than necessary for a one-time task.
Option B: Dataproc with Spark involves cluster creation, file transfer to Cloud Storage, and job scripting―far too complex for a simple load.
Option C: The bq load command (part of the Google Cloud SDK) is a CLI tool that uploads local files (e.g., CSV, JSON) directly to BigQuery with one command (e.g., bq load –source_format=CSV dataset.table file.csv). It’s pre-built, requires no coding, and leverages existing SDK installation, minimizing effort.
Option D: Dataflow with Beam requires pipeline development and isn’t designed for local execution to BigQuery without Cloud Storage staging, adding complexity.
Why C is Best: For a demo, bq load is the fastest, simplest way to get data into BigQuery. Install the SDK, run a command, and you’re done―no infrastructure or coding needed. It’s Google’s go-to for ad-hoc loads.
Extract from Google Documentation: From "Loading Data into BigQuery with the bq Command-Line Tool" (https://cloud.google.com/bigquery/docs/bq-command-line-tool#loading_data): "The bq load command allows you to upload data files from your local machine to BigQuery with a single command, providing a quick and easy way to perform one-time data loads."
Reference: Google Cloud Documentation – "bq Command-Line Tool"
(https://cloud.google.com/bigquery/docs/bq-command-line-tool).
C
Explanation:
Comprehensive and Detailed In-Depth
A one-time load with minimal effort points to a simple, out-of-the-box tool. The files are local, so the solution must bridge on-premises to BigQuery easily.
Option A: A Python script with the Storage Write API requires coding, setup (authentication, libraries), and debugging―more effort than necessary for a one-time task.
Option B: Dataproc with Spark involves cluster creation, file transfer to Cloud Storage, and job scripting―far too complex for a simple load.
Option C: The bq load command (part of the Google Cloud SDK) is a CLI tool that uploads local files (e.g., CSV, JSON) directly to BigQuery with one command (e.g., bq load –source_format=CSV dataset.table file.csv). It’s pre-built, requires no coding, and leverages existing SDK installation, minimizing effort.
Option D: Dataflow with Beam requires pipeline development and isn’t designed for local execution to BigQuery without Cloud Storage staging, adding complexity.
Why C is Best: For a demo, bq load is the fastest, simplest way to get data into BigQuery. Install the SDK, run a command, and you’re done―no infrastructure or coding needed. It’s Google’s go-to for ad-hoc loads.
Extract from Google Documentation: From "Loading Data into BigQuery with the bq Command-Line Tool" (https://cloud.google.com/bigquery/docs/bq-command-line-tool#loading_data): "The bq load command allows you to upload data files from your local machine to BigQuery with a single command, providing a quick and easy way to perform one-time data loads."
Reference: Google Cloud Documentation – "bq Command-Line Tool"
(https://cloud.google.com/bigquery/docs/bq-command-line-tool).
Question #7
Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive data.
What should you do?
- A . Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
- B . Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.
- C . Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
- D . Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
Correct Answer: B
B
Explanation:
Creating a materialized view with the limited data in a new dataset and granting the data analyst the BigQuery Data Viewer role on the dataset and the BigQuery Job User role in the project ensures that the analyst can query only the non-sensitive data without access to sensitive datasets. Materialized views allow you to predefine what subset of data is visible, providing a secure and efficient way to control access while maintaining compliance with data governance policies. This approach follows the principle of least privilege while meeting the requirements.
B
Explanation:
Creating a materialized view with the limited data in a new dataset and granting the data analyst the BigQuery Data Viewer role on the dataset and the BigQuery Job User role in the project ensures that the analyst can query only the non-sensitive data without access to sensitive datasets. Materialized views allow you to predefine what subset of data is visible, providing a secure and efficient way to control access while maintaining compliance with data governance policies. This approach follows the principle of least privilege while meeting the requirements.
Question #7
Your organization has highly sensitive data that gets updated once a day and is stored across multiple datasets in BigQuery. You need to provide a new data analyst access to query specific data in BigQuery while preventing access to sensitive data.
What should you do?
- A . Grant the data analyst the BigQuery Job User IAM role in the Google Cloud project.
- B . Create a materialized view with the limited data in a new dataset. Grant the data analyst BigQuery Data Viewer IAM role in the dataset and the BigQuery Job User IAM role in the Google Cloud project.
- C . Create a new Google Cloud project, and copy the limited data into a BigQuery table. Grant the data analyst the BigQuery Data Owner IAM role in the new Google Cloud project.
- D . Grant the data analyst the BigQuery Data Viewer IAM role in the Google Cloud project.
Correct Answer: B
B
Explanation:
Creating a materialized view with the limited data in a new dataset and granting the data analyst the BigQuery Data Viewer role on the dataset and the BigQuery Job User role in the project ensures that the analyst can query only the non-sensitive data without access to sensitive datasets. Materialized views allow you to predefine what subset of data is visible, providing a secure and efficient way to control access while maintaining compliance with data governance policies. This approach follows the principle of least privilege while meeting the requirements.
B
Explanation:
Creating a materialized view with the limited data in a new dataset and granting the data analyst the BigQuery Data Viewer role on the dataset and the BigQuery Job User role in the project ensures that the analyst can query only the non-sensitive data without access to sensitive datasets. Materialized views allow you to predefine what subset of data is visible, providing a secure and efficient way to control access while maintaining compliance with data governance policies. This approach follows the principle of least privilege while meeting the requirements.
Question #9
Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner’s data in its own BigQuery dataset. Each partner should be able to access only their data. You want to share the data while following Google-recommended practices.
What should you do?
- A . Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.
- B . Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub/Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.
- C . Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
- D . Grant the partners the bigquery.user IAM role on the BigQuery project.
Correct Answer: A
A
Explanation:
Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google-recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.
A
Explanation:
Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google-recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.
Question #9
Your organization has several datasets in BigQuery. The datasets need to be shared with your external partners so that they can run SQL queries without needing to copy the data to their own projects. You have organized each partner’s data in its own BigQuery dataset. Each partner should be able to access only their data. You want to share the data while following Google-recommended practices.
What should you do?
- A . Use Analytics Hub to create a listing on a private data exchange for each partner dataset. Allow each partner to subscribe to their respective listings.
- B . Create a Dataflow job that reads from each BigQuery dataset and pushes the data into a dedicated Pub/Sub topic for each partner. Grant each partner the pubsub. subscriber IAM role.
- C . Export the BigQuery data to a Cloud Storage bucket. Grant the partners the storage.objectUser IAM role on the bucket.
- D . Grant the partners the bigquery.user IAM role on the BigQuery project.
Correct Answer: A
A
Explanation:
Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google-recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.
A
Explanation:
Using Analytics Hub to create a listing on a private data exchange for each partner dataset is the Google-recommended practice for securely sharing BigQuery data with external partners. Analytics Hub allows you to manage data sharing at scale, enabling partners to query datasets directly without needing to copy the data into their own projects. By creating separate listings for each partner dataset and allowing only the respective partner to subscribe, you ensure that partners can access only their specific data, adhering to the principle of least privilege. This approach is secure, efficient, and designed for scenarios involving external data sharing.