Dec 04, 2025 Detailed New Associate-Data-Practitioner Exam Questions for Concept Clearance
Associate-Data-Practitioner Exam Preparation Material with New Associate-Data-Practitioner Dumps Questions.
NEW QUESTION # 54
Your company uses Looker as its primary business intelligence platform. You want to use LookML to visualize the profit margin for each of your company's products in your Looker Explores and dashboards. You need to implement a solution quickly and efficiently. What should you do?
- A. Apply a filter to only show products with a positive profit margin.
- B. Create a derived table that pre-calculates the profit margin for each product, and include it in the Looker model.
- C. Create a new dimension that categorizes products based on their profit margin ranges (e.g., high, medium, low).
- D. Define a new measure that calculates the profit margin by using the existing revenue and cost fields.
Answer: D
Explanation:
Defining a new measure in LookML to calculate the profit margin using the existing revenue and cost fields is the most efficient and straightforward solution. This approach allows you to dynamically compute the profit margin directly within your Looker Explores and dashboards without needing to pre-calculate or create additional tables. The measure can be defined using LookML syntax, such as:
measure: profit_margin {
type: number
sql: (revenue - cost) / revenue ;;
value_format: "0.0%"
}
This method is quick to implement and integrates seamlessly into your existing Looker model, enabling accurate visualization of profit margins across your products.
NEW QUESTION # 55
Your company is building a near real-time streaming pipeline to process JSON telemetry data from small appliances. You need to process messages arriving at a Pub/Sub topic, capitalize letters in the serial number field, and write results to BigQuery. You want to use a managed service and write a minimal amount of code for underlying transformations. What should you do?
- A. Use a Pub/Sub to Cloud Storage subscription, write a Cloud Run service that is triggered when objects arrive in the bucket, performs the transformations, and writes the results to BigQuery.
- B. Use a Pub/Sub push subscription, write a Cloud Run service that accepts the messages, performs the transformations, and writes the results to BigQuery.
- C. Use the "Pub/Sub to BigQuery" Dataflow template with a UDF, and write the results to BigQuery.
- D. Use a Pub/Sub to BigQuery subscription, write results directly to BigQuery, and schedule a transformation query to run every five minutes.
Answer: C
Explanation:
Using the "Pub/Sub to BigQuery" Dataflow template with a UDF (User-Defined Function) is the optimal choice because it combines near real-time processing, minimal code for transformations, and scalability. The UDF allows for efficient implementation of custom transformations, such as capitalizing letters in the serial number field, while Dataflow handles the rest of the managed pipeline seamlessly.
NEW QUESTION # 56
Your company uses Looker to visualize and analyze sales dat
a. You need to create a dashboard that displays sales metrics, such as sales by region, product category, and time period. Each metric relies on its own set of attributes distributed across several tables. You need to provide users the ability to filter the data by specific sales representatives and view individual transactions. You want to follow the Google-recommended approach. What should you do?
- A. Use Looker's custom visualization capabilities to create a single visualization that displays all the sales metrics with filtering and drill-down functionality.
- B. Use BigQuery to create multiple materialized views, each focusing on a specific sales metric. Build the dashboard using these views.
- C. Create a single Explore with all sales metrics. Build the dashboard using this Explore.
- D. Create multiple Explores, each focusing on each sales metric. Link the Explores together in a dashboard using drill-down functionality.
Answer: C
Explanation:
Creating a single Explore with all the sales metrics is the Google-recommended approach. This Explore should be designed to include all relevant attributes and dimensions, enabling users to analyze sales data by region, product category, time period, and other filters like sales representatives. With a well-structured Explore, you can efficiently build a dashboard that supports filtering and drill-down functionality. This approach simplifies maintenance, provides a consistent data model, and ensures users have the flexibility to interact with and analyze the data seamlessly within a unified framework.
NEW QUESTION # 57
You need to design a data pipeline to process large volumes of raw server log data stored in Cloud Storage.
The data needs to be cleaned, transformed, and aggregated before being loaded into BigQuery for analysis.
The transformation involves complex data manipulation using Spark scripts that your team developed. You need to implement a solution that leverages your team's existing skillset, processes data at scale, and minimizes cost. What should you do?
- A. Use Cloud Data Fusion to visually design and manage the pipeline.
- B. Use Dataproc to run the transformations on a cluster.
- C. Use Dataflow with a custom template for the transformation logic.
- D. Use Dataform to define the transformations in SQLX.
Answer: B
Explanation:
Comprehensive and Detailed In-Depth Explanation:
The pipeline must handle large-scale log processing with existing Spark scripts, prioritizing skillset reuse, scalability, and cost. Let's break it down:
* Option A: Dataflow uses Apache Beam, not Spark, requiring script rewrites (losing skillset leverage).
Custom templates scale well but increase development cost and effort.
* Option B: Cloud Data Fusion is a visual ETL tool, not Spark-based. It doesn't reuse existing scripts, requiring redesign, and is less cost-efficient for complex, code-driven transformations.
* Option C: Dataform uses SQLX for BigQuery ELT, not Spark. It's unsuitable for pre-load transformations of raw logs and doesn't leverage Spark skills.
NEW QUESTION # 58
You have created a LookML model and dashboard that shows daily sales metrics for five regional managers to use. You want to ensure that the regional managers can only see sales metrics specific to their region. You need an easy-to-implement solution. What should you do?
- A. Create five different Explores with the sql_always_filter Explore filter applied on the region_name dimension. Set each region_name value to the corresponding region for each manager.
- B. Create a sales_region user attribute, and assign each manager's region as the value of their user attribute. Add an access_filter Explore filter on the region_name dimension by using the sales_region user attribute.
- C. Create separate Looker dashboards for each regional manager. Set the default dashboard filter to the corresponding region for each manager.
- D. Create separate Looker instances for each regional manager. Copy the LookML model and dashboard to each instance. Provision viewer access to the corresponding manager.
Answer: B
Explanation:
Using a sales_region user attribute is the best solution because it allows you to dynamically filter data based on each manager's assigned region. By adding an access_filter Explore filter on the region_name dimension that references the sales_region user attribute, each manager sees only the sales metrics specific to their region. This approach is easy to implement, scalable, and avoids duplicating dashboards or Explores, making it both efficient and maintainable.
NEW QUESTION # 59
Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?
- A. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
- B. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
- C. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.
- D. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
Answer: D
Explanation:
UsingDataflowto subscribe to the Pub/Sub topic and transform the activity logs is the best approach for this scenario. Dataflow is a managed service designed for processing and transforming streaming data in real time.
It allows you to aggregate metrics from the raw activity logs efficiently and load the transformed data into a BigQuery table for reporting. This solution ensures scalability, supports real-time processing, and enables querying of both raw and aggregated data in BigQuery, providing the flexibility and insights needed for the dashboard.
NEW QUESTION # 60
Your organization sends IoT event data to a Pub/Sub topic. Subscriber applications read and perform transformations on the messages before storing them in the data warehouse. During particularly busy times when more data is being written to the topic, you notice that the subscriber applications are not acknowledging messages within the deadline. You need to modify your pipeline to handle these activity spikes and continue to process the messages. What should you do?
- A. Seek back to the last acknowledged message.
- B. Retry messages until they are acknowledged.
B Implement flow control on the subscribers - C. Forward unacknowledged messages to a dead-letter topic.
Answer: C
Explanation:
Implementingflow control on the subscribersallows the subscriber applications to manage message processing during activity spikes by controlling the rate at which messages are pulled and processed. This prevents overwhelming the subscribers and ensures that messages are acknowledged within the deadline. Flow control helps maintain the stability of your pipeline during high-traffic periods without dropping or delaying messages unnecessarily.
NEW QUESTION # 61
Your company's customer support audio files are stored in a Cloud Storage bucket. You plan to analyze the audio files' metadata and file content within BigQuery to create inference by using BigQuery ML. You need to create a corresponding table in BigQuery that represents the bucket containing the audio files. What should you do?
- A. Create an object table.
- B. Create a native table.
- C. Create an external table.
- D. Create a temporary table.
Answer: A
Explanation:
To analyze audio files stored in a Cloud Storage bucket and represent them in BigQuery, you should create an object table. Object tables in BigQuery are designed to represent objects stored in Cloud Storage, including their metadata. This enables you to query the metadata of audio files directly from BigQuery without duplicating the data. Once the object table is created, you can use it in conjunction with other BigQuery ML workflows for inference and analysis.
NEW QUESTION # 62
You work for a global financial services company that trades stocks 24/7. You have a Cloud SGL for PostgreSQL user database. You need to identify a solution that ensures that the database is continuously operational, minimizes downtime, and will not lose any data in the event of a zonal outage. What should you do?
- A. Create a read replica in the same region but in a different zone.
- B. Continuously back up the Cloud SGL instance to Cloud Storage. Create a Compute Engine instance with PostgreSCL in a different region. Restore the backup in the Compute Engine instance if a failure occurs.
- C. Configure and create a high-availability Cloud SQL instance with the primary instance in zone A and a secondary instance in any zone other than zone A.
- D. Create a read replica in another region. Promote the replica to primary if a failure occurs.
Answer: C
Explanation:
Configuring a high-availability (HA) Cloud SQL instance ensures continuous operation, minimizes downtime, and prevents data loss in the event of a zonal outage. In this setup, the primary instance is located in one zone (e.g., zone A), and a synchronous secondary instance is located in a different zone within the same region. This configuration ensures that all data is replicated to the secondary instance in real-time. In the event of a failure in the primary zone, the system automatically promotes the secondary instance to primary, ensuring seamless failover with no data loss and minimal downtime. This is the recommended approach for mission-critical, highly available databases.
NEW QUESTION # 63
You work for a financial services company that handles highly sensitive dat a. Due to regulatory requirements, your company is required to have complete and manual control of data encryption. Which type of keys should you recommend to use for data storage?
- A. Use a dedicated third-party key management system (KMS) chosen by the company.
- B. Use customer-supplied encryption keys (CSEK).
- C. Use customer-managed encryption keys (CMEK).
- D. Use Google-managed encryption keys (GMEK).
Answer: B
Explanation:
For regulatory requirements that mandate complete and manual control of data encryption, you should use customer-supplied encryption keys (CSEK). With CSEK, your company provides the encryption keys for data storage, and Google Cloud does not store or manage these keys. This approach ensures that your organization retains full control and responsibility over the encryption process, meeting strict regulatory compliance requirements.
NEW QUESTION # 64
You manage an ecommerce website that has a diverse range of products. You need to forecast future product demand accurately to ensure that your company has sufficient inventory to meet customer needs and avoid stockouts. Your company's historical sales data is stored in a BigQuery table. You need to create a scalable solution that takes into account the seasonality and historical data to predict product demand. What should you do?
- A. Use the historical sales data to train and create a BigQuery ML logistic regression model. Use the ML.PREDICT function call to output the predictions into a new BigQuery table.
- B. Use Colab Enterprise to create a Jupyter notebook. Use the historical sales data to train a custom prediction model in Python.
- C. Use the historical sales data to train and create a BigQuery ML time series model. Use the ML.
FORECAST function call to output the predictions into a new BigQuery table. - D. Use the historical sales data to train and create a BigQuery ML linear regression model. Use the ML.
PREDICT function call to output the predictions into a new BigQuery table.
Answer: C
Explanation:
Comprehensive and Detailed In-Depth Explanation:
Forecasting product demand with seasonality requires a time series model, and BigQuery ML offers a scalable, serverless solution. Let's analyze:
* Option A: BigQuery ML's time series models (e.g., ARIMA_PLUS) are designed for forecasting with seasonality and trends. The ML.FORECAST function generates predictions based on historical data, storing them in a table. This is scalable (no infrastructure) and integrates natively with BigQuery, ideal for ecommerce demand prediction.
* Option B: Colab Enterprise with a custom Python model (e.g., Prophet) is flexible but requires coding, maintenance, and potentially exporting data, reducing scalability compared to BigQuery ML's in-place processing.
* Option C: Linear regression predicts continuous values but doesn't handle seasonality or time series patterns effectively, making it unsuitable for demand forecasting.
NEW QUESTION # 65
Your organization has a petabyte of application logs stored as Parquet files in Cloud Storage. You need to quickly perform a one-time SQL-based analysis of the files and join them to data that already resides in BigQuery. What should you do?
- A. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
- B. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
- C. Launch a Cloud Data Fusion environment, use plugins to connect to BigQuery and Cloud Storage, and use the SQL join operation to analyze the data.
- D. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
Answer: D
Explanation:
Creating external tables over the Parquet files in Cloud Storage allows you to perform SQL-based analysis and joins with data already in BigQuery without needing to load the files into BigQuery. This approach is efficient for a one-time analysis as it avoids the time and cost associated with loading large volumes of data into BigQuery. External tables provide seamless integration with Cloud Storage, enabling quick and cost-effective analysis of data stored in Parquet format.
NEW QUESTION # 66
Your retail company collects customer data from various sources:
Online transactions: Stored in a MySQL database
Customer feedback: Stored as text files on a company server
Social media activity: Streamed in real-time from social media platforms
You are designing a data pipeline to extract this data. Which Google Cloud storage system(s) should you select for further analysis and ML model training?
- A. 1. Online transactions: Cloud Storage
2. Customer feedback: Cloud Storage
3. Social media activity: Cloud Storage - B. 1. Online transactions: Cloud SQL for MySQL
2. Customer feedback: BigQuery
3. Social media activity: Cloud Storage - C. 1. Online transactions: Bigtable
2. Customer feedback: Cloud Storage
3. Social media activity: CloudSQL for MySQL - D. 1. Online transactions: BigQuery
2. Customer feedback: Cloud Storage
3. Social media activity: BigQuery
Answer: D
Explanation:
Online transactions:Storing the transactional data inBigQueryis ideal because BigQuery is a serverless data warehouse optimized for querying and analyzing structured data at scale. It supports SQL queries and is suitable for structured transactional data.
Customer feedback:Storing customer feedback inCloud Storageis appropriate as it allows you to store unstructured text files reliably and at a low cost. Cloud Storage also integrates well with data processing and ML tools for further analysis.
Social media activity:Storing real-time social media activity inBigQueryis optimal because BigQuery supports streaming inserts, enabling real-time ingestion and analysis of data. This allows immediate analysis and integration into dashboards or ML pipelines.
NEW QUESTION # 67
Your organization's ecommerce website collects user activity logs using a Pub/Sub topic. Your organization's leadership team wants a dashboard that contains aggregated user engagement metrics. You need to create a solution that transforms the user activity logs into aggregated metrics, while ensuring that the raw data can be easily queried. What should you do?
- A. Create a BigQuery subscription to the Pub/Sub topic, and load the activity logs into the table. Create a materialized view in BigQuery using SQL to transform the data for reporting
- B. Create an event-driven Cloud Run function to trigger a data transformation pipeline to run. Load the transformed activity logs into a BigQuery table for reporting.
- C. Create a Cloud Storage subscription to the Pub/Sub topic. Load the activity logs into a bucket using the Avro file format. Use Dataflow to transform the data, and load it into a BigQuery table for reporting.
- D. Create a Dataflow subscription to the Pub/Sub topic, and transform the activity logs. Load the transformed data into a BigQuery table for reporting.
Answer: D
Explanation:
Using Dataflow to subscribe to the Pub/Sub topic and transform the activity logs is the best approach for this scenario. Dataflow is a managed service designed for processing and transforming streaming data in real time. It allows you to aggregate metrics from the raw activity logs efficiently and load the transformed data into a BigQuery table for reporting. This solution ensures scalability, supports real-time processing, and enables querying of both raw and aggregated data in BigQuery, providing the flexibility and insights needed for the dashboard.
NEW QUESTION # 68
You are responsible for managing Cloud Storage buckets for a research company. Your company has well- defined data tiering and retention rules. You need to optimize storage costs while achieving your data retention needs. What should you do?
- A. Configure the buckets to use the Standard storage class and enable Object Versioning.
- B. Configure the buckets to use the Archive storage class.
- C. Configure the buckets to use the Autoclass feature.
- D. Configure a lifecycle management policy on each bucket to downgrade the storage class and remove objects based on age.
Answer: D
Explanation:
Configuring alifecycle management policyon each Cloud Storage bucket allows you to automatically transition objects to lower-cost storage classes (such as Nearline, Coldline, or Archive) based on their age or other criteria. Additionally, the policy can automate the removal of objects once they are no longer needed, ensuring compliance with retention rules and optimizing storage costs. This approach aligns well with well- defined data tiering and retention needs, providing cost efficiency and automation.
Extract from Google Documentation: From "Object Lifecycle Management" (https://cloud.google.com
/storage/docs/lifecycle):"Use lifecycle management policies to automatically transition objects to lower-cost storage classes (e.g., Nearline, Coldline) and delete them based on age, optimizing costs according to your specific tiering and retention requirements."
NEW QUESTION # 69
Your company has developed a website that allows users to upload and share video files. These files are most frequently accessed and shared when they are initially uploaded. Over time, the files are accessed and shared less frequently, although some old video files may remain very popular.
You need to design a storage system that is simple and cost-effective. What should you do?
- A. Create a single-region bucket with Archive as the default storage class.
- B. Create a single-region bucket with Autoclass enabled.
- C. Create a single-region bucket. Configure a Cloud Scheduler job that runs every 24 hours and changes the storage class based on upload date.
- D. Create a single-region bucket with custom Object Lifecycle Management policies based on upload date.
Answer: D
Explanation:
Creating a single-region bucket with custom Object Lifecycle Management policies based on upload date is the most appropriate solution. This approach allows you to automatically transition objects to less expensive storage classes as their access frequency decreases over time. For example, frequently accessed files can remain in the Standard storage class initially, then transition to Nearline, Coldline, or Archive storage as their popularity wanes. This strategy ensures a cost-effective and efficient storage system while maintaining simplicity by automating the lifecycle management of video files.
NEW QUESTION # 70
Your organization has a BigQuery dataset that contains sensitive employee information such as salaries and performance reviews. The payroll specialist in the HR department needs to have continuous access to aggregated performance data, but they do not need continuous access to other sensitive dat a. You need to grant the payroll specialist access to the performance data without granting them access to the entire dataset using the simplest and most secure approach. What should you do?
- A. Create a SQL query with the aggregated performance data. Export the results to an Avro file in a Cloud Storage bucket. Share the bucket with the payroll specialist.
- B. Create row-level and column-level permissions and policies on the table that contains performance data in the dataset. Provide the payroll specialist with the appropriate permission set.
- C. Use authorized views to share query results with the payroll specialist.
- D. Create a table with the aggregated performance data. Use table-level permissions to grant access to the payroll specialist.
Answer: C
Explanation:
Using authorized views is the simplest and most secure way to grant the payroll specialist access to aggregated performance data without exposing the entire dataset. Authorized views allow you to create a view in BigQuery that contains only the query results for the aggregated performance data. The payroll specialist can query the view without being granted access to the underlying sensitive data. This approach ensures security, adheres to the principle of least privilege, and eliminates the need to manage complex row-level or column-level permissions.
NEW QUESTION # 71
Your company is adopting BigQuery as their data warehouse platform. Your team has experienced Python developers. You need to recommend a fully-managed tool to build batch ETL processes that extract data from various source systems, transform the data using a variety of Google Cloud services, and load the transformed data into BigQuery. You want this tool to leverage your team's Python skills. What should you do?
- A. Use Cloud Composer with pre-built operators.
- B. Use Dataflow and pre-built templates.
- C. Deploy Cloud Data Fusion and included plugins.
- D. Use Dataform with assertions.
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:
The tool must be fully managed, support batch ETL, integrate with multiple Google Cloud services, and leverage Python skills.
* Option A: Dataform is SQL-focused for ELT within BigQuery, not Python-centric, and lacks broad service integration for extraction.
* Option B: Cloud Data Fusion is a visual ETL tool, not Python-focused, and requires more UI-based configuration than coding.
* Option C: Cloud Composer (managed Apache Airflow) is fully managed, supports batch ETL via DAGs, integrates with various Google Cloud services (e.g., BigQuery, GCS) through operators, and allows custom Python code in tasks. It's ideal for Python developers per the "Cloud Composer" documentation.
NEW QUESTION # 72
You need to create a data pipeline that streams event information from applications in multiple Google Cloud regions into BigQuery for near real-time analysis. The data requires transformation before loading. You want to create the pipeline using a visual interface. What should you do?
- A. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations.
- B. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
- C. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub.
- D. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery.
Answer: B
Explanation:
Pushing event information to aPub/Sub topicand then creating aDataflow job using the Dataflow job builderis the most suitable solution. The Dataflow job builder provides a visual interface to design pipelines, allowing you to define transformations and load data into BigQuery. This approach is ideal for streaming data pipelines that require near real-time transformations and analysis. It ensures scalability across multiple regions and integrates seamlessly with Pub/Sub for event ingestion and BigQuery for analysis.
The best solution for creating a data pipeline with a visual interface for streaming event information from multiple Google Cloud regions into BigQuery for near real-time analysis with transformations isA. Push event information to a Pub/Sub topic. Create a Dataflow job using the Dataflow job builder.
Here's why:
* Pub/Sub and Dataflow:
* Pub/Sub is ideal for real-time message ingestion, especially from multiple regions.
* Dataflow, particularly with the Dataflow job builder, provides a visual interface for creating data pipelines that can perform real-time stream processing and transformations.
* The Dataflow job builder allows creating pipelines with visual tools, fulfilling the requirement of a visual interface.
* Dataflow is built for real time streaming and applying transformations.
Let's break down why the other options are less suitable:
* B. Push event information to Cloud Storage, and create an external table in BigQuery. Create a BigQuery scheduled job that executes once each day to apply transformations:
* This is a batch processing approach, not real-time.
* Cloud Storage and scheduled jobs are not designed for near real-time analysis.
* This does not meet the real time requirement of the question.
* C. Push event information to a Pub/Sub topic. Create a Cloud Run function to subscribe to the Pub/Sub topic, apply transformations, and insert the data into BigQuery:
* While Cloud Run can handle transformations, it requires more coding and is less scalable and manageable than Dataflow for complex streaming pipelines.
* Cloud run does not provide a visual interface.
* D. Push event information to a Pub/Sub topic. Create a BigQuery subscription in Pub/Sub:
* BigQuery subscriptions in Pub/Sub are for direct loading of Pub/Sub messages into BigQuery, without the ability to perform transformations.
* This option does not provide any transformation functionality.
Therefore, Pub/Sub for ingestion and Dataflow with its job builder for visual pipeline creation and transformations is the most appropriate solution.
NEW QUESTION # 73
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