Ultimate Guide to the Associate-Data-Practitioner - Latest Nov 14, 2025 Edition Available Now
2025 Updated Verified Pass Associate-Data-Practitioner Exam - Real Questions and Answers
NEW QUESTION # 57
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. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS(model_uype='logisric_reg') AS SELECT * from cusromer_data;
- B. CREATE OR REPLACE MODEL churn_prediction_model options(model_type='logistic_reg*) as select ' except(churned) FROM customer data;
- C. CREATE OR REPLACE MODEL churn_prediction_model options (model type='logistic_reg') AS select churned as label FROM customer_data;
- D. CREATE OR REPLACE MODEL churn_prediction_model OPTIONS (rr.odel_type=' logisric_reg *) AS select * except(churned), churned AS label FROM customer_data;
Answer: D
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why B is correct:BigQuery ML requires the target label to be explicitly named label.
EXCEPT(churned) selects all columns except the churned column, which becomes the features.
churned AS label renames the churned column to label, which is required for BigQuery ML.
logistic_reg is the correct model_type option.
Why other options are incorrect:A: Does not rename the target column to label. Also has a typo in the model type.
C: Only selects the target label, not the features.
D: Has a syntax error with the single quote before except.
NEW QUESTION # 58
You are migrating data from a legacy on-premises MySQL database to Google Cloud. The database contains various tables with different data types and sizes, including large tables with millions of rows and transactional dat a. You need to migrate this data while maintaining data integrity, and minimizing downtime and cost. What should you do?
- A. Export the MySQL database to CSV files, transfer the files to Cloud Storage by using Storage Transfer Service, and load the files into a Cloud SQL for MySQL instance.
- B. Use Cloud Data Fusion to migrate the MySQL database to MySQL on Compute Engine.
- C. Use Database Migration Service to replicate the MySQL database to a Cloud SQL for MySQL instance.
- D. Set up a Cloud Composer environment to orchestrate a custom data pipeline. Use a Python script to extract data from the MySQL database and load it to MySQL on Compute Engine.
Answer: C
Explanation:
Using Database Migration Service (DMS) to replicate the MySQL database to a Cloud SQL for MySQL instance is the best approach. DMS is a fully managed service designed for migrating databases to Google Cloud with minimal downtime and cost. It supports continuous data replication, ensuring data integrity during the migration process, and handles schema and data transfer efficiently. This solution is particularly suited for large tables and transactional data, as it maintains real-time synchronization between the source and target databases, minimizing downtime for the migration.
NEW QUESTION # 59
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 ## of data to Cloud Storage securely, and only have a few days to move the data. What should you do?
- A. Connect to Google Cloud using VPN. Use Storage Transfer Service to move the data to Cloud Storage.
- B. Request multiple Transfer Appliances, copy the data to the appliances, and ship the appliances back to Google Cloud to upload the data to Cloud Storage.
- C. Connect to Google Cloud using VPN. Use the gcloud storage command to move the data to Cloud Storage.
- D. Connect to Google Cloud using Dedicated Interconnect. Use the gcloud storage command to move the data to Cloud Storage.
Answer: B
Explanation:
UsingTransfer Appliancesis 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.
NEW QUESTION # 60
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?
- A. Create a private exchange using Analytics Hub with data egress restriction, and grant access to the team members.
- B. Enable domain restricted sharing on the project. Grant the team members the BigQuery Data Viewer IAM role on the dataset.
- C. Create authorized views in the team's Google Cloud project that is only accessible by the team.
- D. Export the dataset to a Cloud Storage bucket in the team's Google Cloud project that is only accessible by the team.
Answer: A
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.
NEW QUESTION # 61
Your team uses Google Sheets to track budget data that is updated daily. The team wants to compare budget data against actual cost data, which is stored in a BigQuery table. You need to create a solution that calculates the difference between each day's budget and actual costs. You want to ensure that your team has access to daily-updated results in Google Sheets. What should you do?
- A. Download the budget data as a CSV file, and upload the CSV file to create a new BigQuery table. Join the actual cost table with the new BigQuery table, and save the results as a CSV file. Open the CSV file in Google Sheets.
- B. Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table, and open it by using Connected Sheets.
- C. Download the budget data as a CSV file and upload the CSV file to a Cloud Storage bucket. Create a new BigQuery table from Cloud Storage, and join the actual cost table with it. Open the joined BigQuery table by using Connected Sheets.
- D. Create a BigQuery external table by using the Drive URI of the Google sheet, and join the actual cost table with it. Save the joined table as a CSV file and open the file in Google Sheets.
Answer: B
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why D is correct:Creating a BigQuery external table directly from the Google Sheet allows for real-time updates.
Joining the external table with the actual cost table in BigQuery performs the calculation.
Connected Sheets allows the team to access and analyze the results directly in Google Sheets, with the data being updated.
Why other options are incorrect:A: Saving as a CSV file loses the live connection and daily updates.
B: Downloading and uploading as a CSV file adds unnecessary steps and loses the live connection.
C: Same issue as B, losing the live connection.
NEW QUESTION # 62
You work for an ecommerce company that has a BigQuery dataset that contains customer purchase history, demographics, and website interactions. You need to build a machine learning (ML) model to predict which customers are most likely to make a purchase in the next month. You have limited engineering resources and need to minimize the ML expertise required for the solution. What should you do?
- A. Use Vertex AI Workbench to develop a custom model for purchase prediction.
- B. Use BigQuery ML to create a logistic regression model for purchase prediction.
- C. Use Colab Enterprise to develop a custom model for purchase prediction.
- D. Export the data to Cloud Storage, and use AutoML Tables to build a classification model for purchase prediction.
Answer: B
Explanation:
Using BigQuery ML is the best solution in this case because:
Ease of use: BigQuery ML allows users to build machine learning models using SQL, which requires minimal ML expertise.
Integrated platform: Since the data already exists in BigQuery, there's no need to move it to another service, saving time and engineering resources.
Logistic regression: This is an appropriate model for binary classification tasks like predicting the likelihood of a customer making a purchase in the next month.
NEW QUESTION # 63
You created a curated dataset of market trends in BigQuery that you want to share with multiple external partners. You want to control the rows and columns that each partner has access to. You want to follow Google-recommended practices. What should you do?
- A. Create a separate project for each partner and copy the dataset into each project. Publish each dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
- B. Publish the dataset in Analytics Hub. Grant dataset-level access to each partner by using subscriptions.
- C. Create a separate Cloud Storage bucket for each partner. Export the dataset to each bucket and assign each partner to their respective bucket. Grant bucket-level access by using 1AM roles.
- D. Grant each partner read access to the BigQuery dataset by using 1AM roles.
Answer: B
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why A is correct:Analytics Hub allows you to share datasets with external partners while maintaining control over access.
Subscriptions allow granular control.
Why other options are incorrect:B: Cloud storage is for files, not bigquery datasets.
C: IAM roles do not allow for granular row and column level control.
D: Creating a separate project for each partner is complex and not scalable.
NEW QUESTION # 64
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 separate Looker instances for each regional manager. Copy the LookML model and dashboard to each instance. Provision viewer access to the corresponding 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 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.
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 # 65
You are constructing a data pipeline to process sensitive customer data stored in a Cloud Storage bucket. You need to ensure that this data remains accessible, even in the event of a single-zone outage. What should you do?
- A. Set up a Cloud CDN in front of the bucket.
- B. Enable Object Versioning on the bucket.
- C. Store the data in a multi-region bucket.
- D. Store the data in Nearline storaqe.
Answer: C
Explanation:
Storing the data in amulti-region bucketensures high availability and durability, even in the event of a single- zone outage. Multi-region buckets replicate data across multiple locations within the selected region, providing resilience against zone-level failures and ensuring that the data remains accessible. This approach is particularly suitable for sensitive customer data that must remain available without interruptions.
A single-zone outage requires high availability across zones or regions. Cloud Storage offers location-based redundancy options:
* Option A: Cloud CDN caches content for web delivery but doesn't protect against underlying storage outages-it's for performance, not availability of the source data.
* Option B: Object Versioning retains old versions of objects, protecting against overwrites or deletions, but doesn't ensure availability during a zone failure (still tied to one location).
* Option C: Multi-region buckets (e.g., us or eu) replicate data across multiple regions, ensuring accessibility even if a single zone or region fails. This provides the highest availability for sensitive data in a pipeline.
NEW QUESTION # 66
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. Use the bq load command to load the Parquet files into BigQuery, and perform SQL joins to analyze the data.
- B. Create a Dataproc cluster, and write a PySpark job to join the data from BigQuery to the files in Cloud Storage.
- C. Create external tables over the files in Cloud Storage, and perform SQL joins to tables in BigQuery to analyze the data.
- D. 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.
Answer: C
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 # 67
Your retail company collects customer data from various sources:
You are designing a data pipeline to extract this dat
a. Which Google Cloud storage system(s) should you select for further analysis and ML model training?
- A. 1. Online transactions: Cloud SQL for MySQL
2. Customer feedback: BigQuery
3. Social media activity: Cloud Storage - B. 1. Online transactions: Cloud Storage
2. Customer feedback: Cloud Storage
3. Social media activity: Cloud Storage - C. 1. Online transactions: BigQuery
2. Customer feedback: Cloud Storage
3. Social media activity: BigQuery - D. 1. Online transactions: Bigtable
2. Customer feedback: Cloud Storage
3. Social media activity: CloudSQL for MySQL
Answer: C
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.
NEW QUESTION # 68
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 an external table.
- C. Create a native 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 # 69
You have a BigQuery dataset containing sales data. This data is actively queried for the first 6 months. After that, the data is not queried but needs to be retained for 3 years for compliance reasons. You need to implement a data management strategy that meets access and compliance requirements, while keeping cost and administrative overhead to a minimum. What should you do?
- A. Set up a scheduled query to export the data to Cloud Storage after 6 months. Write a stored procedure to delete the data from BigQuery after 3 years.
- B. Use BigQuery long-term storage for the entire dataset. Set up a Cloud Run function to delete the data from BigQuery after 3 years.
- C. Store all data in a single BigQuery table without partitioning or lifecycle policies.
- D. Partition a BigQuery table by month. After 6 months, export the data to Coldline storage. Implement a lifecycle policy to delete the data from Cloud Storage after 3 years.
Answer: D
Explanation:
Partitioning the BigQuery table by month allows efficient querying of recent data for the first 6 months, reducing query costs. After 6 months, exporting the data toColdline storageminimizes storage costs for data that is rarely accessed but needs to be retained for compliance. Implementing a lifecycle policy in Cloud Storage automates the deletion of the data after 3 years, ensuring compliance while reducing administrative overhead. This approach balances cost efficiency and compliance requirements effectively.
NEW QUESTION # 70
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 a lifecycle management policy on each bucket to downgrade the storage class and remove objects based on age.
- B. Configure the buckets to use the Archive storage class.
- C. Configure the buckets to use the Standard storage class and enable Object Versioning.
- D. Configure the buckets to use the Autoclass feature.
Answer: A
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.
NEW QUESTION # 71
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 SQL for MySQL
2. Customer feedback: BigQuery
3. Social media activity: Cloud Storage - B. 1. Online transactions: Cloud Storage
2. Customer feedback: Cloud Storage
3. Social media activity: Cloud Storage - C. 1. Online transactions: BigQuery
2. Customer feedback: Cloud Storage
3. Social media activity: BigQuery - D. 1. Online transactions: Bigtable
2. Customer feedback: Cloud Storage
3. Social media activity: CloudSQL for MySQL
Answer: C
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 # 72
Your retail organization stores sensitive application usage data in Cloud Storage. You need to encrypt the data without the operational overhead of managing encryption keys. What should you do?
- A. Use Google-managed encryption keys (GMEK).
- B. Use customer-supplied encryption keys (CSEK) for the sensitive data and customer-managed encryption keys (CMEK) for the less sensitive data.
- C. Use customer-supplied encryption keys (CSEK).
- D. Use customer-managed encryption keys (CMEK).
Answer: A
Explanation:
Using Google-managed encryption keys (GMEK) is the best choice when you want to encrypt sensitive data in Cloud Storage without the operational overhead of managing encryption keys. GMEK is the default encryption mechanism in Google Cloud, and it ensures that data is automatically encrypted at rest with no additional setup or maintenance required. It provides strong security while eliminating the need for manual key management.
Google Cloud encrypts all data at rest by default, and the simplest way to avoid key management overhead is to use Google-managed encryption keys (GMEK).
* Option A: GMEK is fully managed by Google, requiring no user intervention, and meets the requirement of no operational overhead while ensuring encryption.
* Option B: CMEK requires managing keys in Cloud KMS, adding operational overhead.
* Option C: CSEK requires users to supply and manage keys externally, increasing complexity significantly.
NEW QUESTION # 73
Your company stores historical data in Cloud Storage. You need to ensure that all data is saved in a bucket for at least three years. What should you do?
- A. Enable Object Versioning.
- B. Change the bucket storage class to Archive.
- C. Set a bucket retention policy.
- D. Set temporary object holds.
Answer: C
Explanation:
Comprehensive and Detailed in Depth Explanation:
Why C is correct:Bucket retention policies are specifically designed to enforce a minimum retention period for objects within a Cloud Storage bucket. This ensures that data cannot be deleted or overwritten before the specified period.
Why other options are incorrect:A: Object versioning allows you to keep multiple versions of an object, but it doesn't guarantee a minimum retention period.
B: Changing the storage class to Archive is for cost optimization, not data retention enforcement.
D: Object holds are for legal holds, not general retention.
NEW QUESTION # 74
You are a data analyst working with sensitive customer data in BigQuery. You need to ensure that only authorized personnel within your organization can query this data, while following the principle of least privilege. What should you do?
- A. Enable access control by using IAM roles.
- B. Update dataset privileges by using the SQL GRANT statement.
- C. Export the data to Cloud Storage, and use signed URLs to authorize access.
- D. Encrypt the data by using customer-managed encryption keys (CMEK).
Answer: A
Explanation:
Comprehensive and Detailed In-Depth Explanation:
BigQuery uses IAM for access control, adhering to least privilege by granting only necessary permissions.
* Option A: IAM roles (e.g., roles/bigquery.dataViewer for read-only) restrict query access to authorized users, aligning with Google's security best practices.
* Option B: BigQuery doesn't support SQL GRANT for dataset privileges; access is managed via IAM or authorized views.
* Option C: Exporting to Cloud Storage with signed URLs bypasses BigQuery's native controls and adds complexity.
NEW QUESTION # 75
You work for a healthcare company that has a large on-premises data system containing patient records with personally identifiable information (PII) such as names, addresses, and medical diagnoses. You need a standardized managed solution that de-identifies PII across all your data feeds prior to ingestion to Google Cloud. What should you do?
- A. Use Cloud Run functions to create a serverless data cleaning pipeline. Store the cleaned data in BigQuery.
- B. Use Cloud Data Fusion to transform the data. Store the cleaned data in BigQuery.
- C. Load the data into BigQuery, and inspect the data by using SQL queries. Use Dataflow to transform the data and remove any errors.
- D. Use Apache Beam to read the data and perform the necessary cleaning and transformation operations. Store the cleaned data in BigQuery.
Answer: B
Explanation:
Using Cloud Data Fusion is the best solution for this scenario because:
Standardized managed solution: Cloud Data Fusion provides a visual interface for building data pipelines and includes prebuilt connectors and transformations for data cleaning and de-identification.
Compliance: It ensures sensitive data such as PII is de-identified prior to ingestion into Google Cloud, adhering to regulatory requirements for healthcare data.
Ease of use: Cloud Data Fusion is designed for transforming and preparing data, making it a managed and user-friendly tool for this purpose.
NEW QUESTION # 76
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. Retry messages until they are acknowledged.
B Implement flow control on the subscribers - B. Seek back to the last acknowledged message.
- 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 # 77
Your company's ecommerce website collects product reviews from customers. The reviews are loaded as CSV files daily to a Cloud Storage bucket. The reviews are in multiple languages and need to be translated to Spanish. You need to configure a pipeline that is serverless, efficient, and requires minimal maintenance. What should you do?
- A. Load the data into BigQuery using Dataproc. Use Apache Spark to translate the reviews by invoking the Cloud Translation API. Set BigQuery as the sink.U
- B. Use a Dataflow templates pipeline to translate the reviews using the Cloud Translation API. Set BigQuery as the sink.
- C. Load the data into BigQuery using a Cloud Run function. Create a BigQuery remote function that invokes the Cloud Translation API. Use a scheduled query to translate new reviews.
- D. Load the data into BigQuery using a Cloud Run function. Use the BigQuery ML create model statement to train a translation model. Use the model to translate the product reviews within BigQuery.
Answer: C
Explanation:
Loading the data into BigQuery using a Cloud Run function and creating a BigQuery remote function that invokes the Cloud Translation API is a serverless and efficient approach. With this setup, you can use a scheduled query in BigQuery to invoke the remote function and translate new product reviews on a regular basis. This solution requires minimal maintenance, as BigQuery handles storage and querying, and the Cloud Translation API provides accurate translations without the need for custom ML model development.
NEW QUESTION # 78
Your organization has decided to migrate their existing enterprise data warehouse to BigQuery. The existing data pipeline tools already support connectors to BigQuery. You need to identify a data migration approach that optimizes migration speed. What should you do?
- A. Create a temporary file system to facilitate data transfer from the existing environment to Cloud Storage. Use Storage Transfer Service to migrate the data into BigQuery.
- B. Use the BigQuery Data Transfer Service to recreate the data pipeline and migrate the data into BigQuery.
- C. Use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping.
- D. Use the Cloud Data Fusion web interface to build data pipelines. Create a directed acyclic graph (DAG) that facilitates pipeline orchestration.
Answer: C
Explanation:
Since your existing data pipeline tools already support connectors to BigQuery, the most efficient approach is to use the existing data pipeline tool's BigQuery connector to reconfigure the data mapping. This leverages your current tools, reducing migration complexity and setup time, while optimizing migration speed. By reconfiguring the data mapping within the existing pipeline, you can seamlessly direct the data into BigQuery without needing additional services or intermediary steps.
NEW QUESTION # 79
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. ETLT
- B. EL
- C. ETL
- D. ELT
Answer: 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.
NEW QUESTION # 80
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 Dataflow and pre-built templates.
- B. Use Cloud Composer with pre-built operators.
- C. Use Dataform with assertions.
- D. Deploy Cloud Data Fusion and included plugins.
Answer: B
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 # 81
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Google Associate-Data-Practitioner Exam Syllabus Topics:
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