Question 1

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?

Accepted Answer

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 2

Your organization plans to move their on-premises environment to Google Cloud. Your organization’s
network bandwidth is less than 1 Gbps. You need to move over 500 ТВ of data to Cloud Storage
securely, and only have a few days to move the dat
a. What should you do?

Accepted Answer

A

Explanation: Using Transfer Appliances is the best solution for securely and efficiently moving over 500 TB of data to Cloud Storage within a limited timeframe, especially with network bandwidth below 1 Gbps. Transfer Appliances are physical devices provided by Google Cloud to securely transfer large amounts of data. After copying the data to the appliances, they are shipped back to Google, where the data is uploaded to Cloud Storage. This approach bypasses bandwidth limitations and ensures the data is migrated quickly and securely.

Question 3

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?

Accepted Answer

B

Explanation: Configuring a lifecycle management policy on 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." Reference: Google Cloud Documentation - "Cloud Storage Lifecycle" (https://cloud.google.com/storage/docs/lifecycle).

Question 4

You are predicting customer churn for a subscription-based service. You have a 50 PB historical
customer dataset in BigQuery that includes demographics, subscription information, and
engagement metrics. You want to build a churn prediction model with minimal overhead. You want
to follow the Google-recommended approach. What should you do?

Accepted Answer

D

Explanation: Using the BigQuery Python client library to query and preprocess data directly in BigQuery and then leveraging BigQueryML to train the churn prediction model is the Google-recommended approach for this scenario. BigQueryML allows you to build machine learning models directly within BigQuery using SQL, eliminating the need to export data or manage additional infrastructure. This minimizes overhead, scales effectively for a dataset as large as 50 PB, and simplifies the end-to-end process of building and training the churn prediction model.

Question 5

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?

Accepted Answer

B

Explanation: Online transactions: Storing the transactional data in BigQuery is 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 in Cloud Storage is 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 in BigQuery is 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.

Question 6

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?

Accepted Answer

B

Explanation: The storage system must balance cost, simplicity, and access patterns: high initial access, decreasing over time, with some files remaining popular. Google Cloud Storage offers tailored options for this: Option A: Custom Object Lifecycle Management (OLM) policies (e.g., transition to Nearline after 30 days, Archive after 90 days) are effective but static. They don’t adapt to actual usage, so popular old files in Archive would incur high retrieval costs. Option B: Autoclass automatically adjusts storage classes (Standard, Nearline, Coldline, Archive) based on object access patterns, not just age. It keeps frequently accessed files in Standard (low latency/cost for access) and moves inactive ones to cheaper classes, minimizing costs while preserving simplicity. This fits the “some files remain popular” nuance. Option C: A Cloud Scheduler job to manually change classes daily is complex (requires scripting, monitoring), error-prone, and less cost-effective than automated solutions like Autoclass or OLM. Option D: Defaulting to Archive is cheapest for storage but disastrous for access—retrieval costs and latency would skyrocket for initial high-access periods. Why B is Best: Autoclass simplifies management (no rules to define) and optimizes costs dynamically. For videos, where access varies unpredictably, it ensures popular files stay accessible without manual intervention, aligning with Google’s cost-optimization guidance. Extract from Google Documentation: From "Autoclass in Cloud Storage" (https://cloud.google.com/storage/docs/autoclass): "Autoclass automatically transitions objects to the most cost-effective storage class based on access patterns, simplifying management and reducing costs for workloads with variable access, such as media files." Reference: Google Cloud Documentation - "Cloud Storage Autoclass" (https://cloud.google.com/storage/docs/autoclass).

Question 7

Your retail company wants to predict customer churn using historical purchase data stored in BigQuery. The dataset includes customer demographics, purchase history, and a label indicating whether the customer churned or not. You want to build a machine learning model to identify customers at risk of churning. You need to create and train a logistic regression model for predicting customer churn, using the customer_data table with the churned column as the target label. Which BigQuery ML query should you use? A) https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/GOOGLE_Cloud_Associate-Data-Practitioner_/page_2_img_1.jpg B) https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/GOOGLE_Cloud_Associate-Data-Practitioner_/page_3_img_1.jpg C) https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/GOOGLE_Cloud_Associate-Data-Practitioner_/page_3_img_2.jpg D) https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/GOOGLE_Cloud_Associate-Data-Practitioner_/page_3_img_3.jpg

Accepted Answer

B

Explanation: In BigQuery ML, when creating a logistic regression model to predict customer churn, the correct query should: Exclude the target label column (in this case, churned) from the feature columns, as it is used for training and not as a feature input. Rename the target label column to label, as BigQuery ML requires the target column to be named label. The chosen query satisfies these requirements: SELECT * EXCEPT(churned), churned AS label: Excludes churned from features and renames it to label. The OPTIONS(model_type='logistic_reg') specifies that a logistic regression model is being trained. This setup ensures the model is correctly trained using the features in the dataset while targeting the churned column for predictions.

Question 8

Another team in your organization is requesting access to a BigQuery dataset. You need to share the
dataset with the team while minimizing the risk of unauthorized copying of dat
a. You also want to create a reusable framework in case you need to share this data with other teams
in the future. What should you do?

Accepted Answer

B

Explanation: Using Analytics Hub to create a private exchange with data egress restrictions ensures controlled sharing of the dataset while minimizing the risk of unauthorized copying. This approach allows you to provide secure, managed access to the dataset without giving direct access to the raw data. The egress restriction ensures that data cannot be exported or copied outside the designated boundaries. Additionally, this solution provides a reusable framework that simplifies future data sharing with other teams or projects while maintaining strict data governance. Extract from Google Documentation: From "Analytics Hub Overview" (https://cloud.google.com/analytics-hub/docs): "Analytics Hub enables secure, controlled data sharing with private exchanges. Combine with organization policies like restrictDataEgress to prevent data copying, providing a reusable framework for sharing BigQuery datasets across teams." Reference: Google Cloud Documentation - "Analytics Hub" (https://cloud.google.com/analytics-hub).

Question 9

Your company has several retail locations. Your company tracks the total number of sales made at each location each day. You want to use SQL to calculate the weekly moving average of sales by location to identify trends for each store. Which query should you use? A) B) C) D)

Accepted Answer

C

Explanation: To calculate the weekly moving average of sales by location: The query must group by store_id (partitioning the calculation by each store). The ORDER BY date ensures the sales are evaluated chronologically. The ROWS BETWEEN 6 PRECEDING AND CURRENT ROW specifies a rolling window of 7 rows (1 week if each row represents daily data). The AVG(total_sales) computes the average sales over the defined rolling window. Chosen query meets these requirements: PARTITION BY store_id groups the calculation by each store. https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/GOOGLE_Cloud_Associate-Data-Practitioner_/page_6_img_1.jpg ORDER BY date orders the rows correctly for the rolling average. https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/GOOGLE_Cloud_Associate-Data-Practitioner_/page_6_img_2.jpg ROWS BETWEEN 6 PRECEDING AND CURRENT ROW ensures the 7-day moving average. https://kxbjsyuhceggsyvxdkof.supabase.co/storage/v1/object/public/file-images/GOOGLE_Cloud_Associate-Data-Practitioner_/page_6_img_3.jpg Extract from Google Documentation: From "Analytic Functions in BigQuery" (https://cloud.google.com/bigquery/docs/reference/standard-sql/analytic-function-concepts): "Use ROWS BETWEEN n PRECEDING AND CURRENT ROW with ORDER BY a time column to compute moving averages over a fixed number of rows, such as a 7-day window, partitioned by a grouping key like store_id." Reference: Google Cloud Documentation - "BigQuery Window Functions" (https://cloud.google.com/bigquery/docs/reference/standard-sql/window-function-calls).

Question 10

You work for a home insurance company. You are frequently asked to create and save risk reports
with charts for specific areas using a publicly available storm event dataset. You want to be able to
quickly create and re-run risk reports when new data becomes available. What should you do?

Accepted Answer

B

Explanation: Copying the storm event dataset into your BigQuery project and using BigQuery Studio to query and visualize the data in Looker Studio is the best approach. This solution allows you to create reusable and automated workflows for generating risk reports. BigQuery handles the querying efficiently, and Looker Studio provides powerful tools for creating and sharing dynamic charts and dashboards. This setup ensures that reports can be easily re-run with updated data, minimizing manual effort and providing a scalable, interactive solution for visualizing risk reports.

Question 11

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?

Accepted Answer

A

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.

Question 12

Your organization uses Dataflow pipelines to process real-time financial transactions. You discover
that one of your Dataflow jobs has failed. You need to troubleshoot the issue as quickly as possible.
What should you do?

Accepted Answer

C

Explanation: To troubleshoot a failed Dataflow job as quickly as possible, you should navigate to the Dataflow Jobs page in the Google Cloud console. The console provides access to detailed job logs and worker logs, which can help you identify the cause of the failure. The graphical interface also allows you to visualize pipeline stages, monitor performance metrics, and pinpoint where the error occurred, making it the most efficient way to diagnose and resolve the issue promptly. Extract from Google Documentation: From "Monitoring Dataflow Jobs" (https://cloud.google.com/dataflow/docs/guides/monitoring-jobs): "To troubleshoot a failed Dataflow job quickly, go to the Dataflow Jobs page in the Google Cloud Console, where you can view job logs and worker logs to identify errors and their root causes." Reference: Google Cloud Documentation - "Dataflow Monitoring" (https://cloud.google.com/dataflow/docs/guides/monitoring-jobs).

Question 13

Your organization needs to store historical customer order dat
a. The data will only be accessed once a month for analysis and must be readily available within a
few seconds when it is accessed. You need to choose a storage class that minimizes storage costs
while ensuring that the data can be retrieved quickly. What should you do?

Accepted Answer

A

Explanation: Using Nearline storage in Cloud Storage is the best option for data that is accessed infrequently (such as once a month) but must be readily available within seconds when needed. Nearline offers a balance between low storage costs and quick retrieval times, making it ideal for scenarios like monthly analysis of historical data. It is specifically designed for infrequent access patterns while avoiding the higher retrieval costs and longer access times of Coldline or Archive storage.

Question 14

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?

Accepted Answer

A

Explanation: Pushing event information to a Pub/Sub topic and then creating a Dataflow job using the Dataflow job builder is 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 is A . 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.

Question 15

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?

Accepted Answer

B

Explanation: Implementing flow control on the subscribers allows 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.

Free Google Cloud Associate-Data-Practitioner Actual Exam Questions