You are configuring a Dataplex Data Quality task. You need to ensure the task runs on a schedule and produces a report in BigQuery. Which resource is required to execute this task?
A team requires access to a specific Dataplex Zone. You want to grant them the ability to view metadata and query the data without granting them full owner permissions. Which role should you assign at the Lake level?
You are migrating data into a Dataplex Lake. You need to ensure that the data is partitioned by date to improve query performance. Where do you define this partitioning?
You are using Dataplex to manage data across multiple projects. You need to organize your BigQuery datasets and Cloud Storage buckets into a single logical entity for governance. Which Dataplex resource should you create?
You have multiple departments using the same Dataplex Lake. You need to ensure that each department can only see data belonging to their own projects. What should you configure?
You are using Dataplex Data Quality tasks to validate a BigQuery table that receives daily incremental loads. The table is partitioned by ingestion date and contains a column named customer_id that must never be null. You need the task to run every night, scan only the most recently loaded partition, and write the results to a BigQuery table so that your operations team can query failures. What should you do?
A data engineer needs to copy a 500 GB dataset from an on-premises data center to Cloud Storage. The data center has a reliable 1 Gbps internet connection, and the transfer must complete within two days. Which Google Cloud service should they use?
You are a data practitioner at a retail company. Your team has a BigQuery dataset that contains customer transaction records spanning 5 years. Analysts frequently run queries filtered by transaction_date, but most queries only look at the last 30 days. Query costs are high because BigQuery scans the entire table for each query. You want to reduce cost and improve performance with minimal changes to existing queries. What should you do?
A financial services company stores transaction records in BigQuery. Compliance requires that individual rows be deleted upon customer request, and the table is partitioned by transaction date. Which approach satisfies the deletion requirement while minimizing cost?
A financial services company stores transaction records in BigQuery. A compliance officer must be able to see only the account_id and transaction_date columns for rows where region equals 'EMEA', while analysts on the same team must continue to see all columns and rows. You want to enforce this at query time without creating a separate copy of the data. What should you do?
You are managing a BigQuery dataset that contains a table with a column of type STRING that stores JSON-formatted data. Analysts frequently need to extract specific fields from this JSON column in their queries. You want to provide them with a way to query these fields efficiently without repeatedly parsing the JSON. What should you do?
A company stores customer transaction data in BigQuery. The security team requires that analysts can query the data but cannot see individual credit card numbers. Analysts should still be able to run aggregations and joins. What should the data engineer implement?
Your analytics team queries a BigQuery table that grows by about 40 GB per day. Most queries filter on a single `event_date` column, but the table is not partitioned, so each query scans the full table and costs are climbing. You want the lowest-effort change that reduces bytes scanned for these date-filtered queries without rewriting the ingestion pipeline. What should you do?
You are managing a Cloud Storage bucket that contains sensitive customer data. Your security team requires that all data written to the bucket be encrypted with a Customer-Managed Encryption Key (CMEK) using Cloud KMS. You need to ensure that any object uploaded without a CMEK is automatically rejected. What should you do?
You manage a Cloud SQL for PostgreSQL instance that holds order data. A downstream BigQuery pipeline must read this data nightly, and the security team requires that credentials never be stored in pipeline code and that the connection be authorized through Google Cloud identity. Which two actions should you take? (Choose two.)
Your team ingests application logs into BigQuery every hour. Analysts complain that queries joining the logs table with a slow-changing reference table run slowly and cost more than expected. The reference table has about 50,000 rows and changes a few times per day. You want to reduce the bytes scanned by these joins without changing the analysts' SQL. What should you do?
A company is designing a data lake on Google Cloud using Cloud Storage and wants to ensure that data is organized and governed effectively. Which two practices should they follow? (Choose two.)
A data engineer must move a 2 TB on-premises Oracle table into BigQuery nightly. The source is behind a firewall, there is limited bandwidth, and the team wants minimal custom code and a fully managed, repeatable pipeline. Which approach best fits these constraints?
You are a data engineer at a financial services company. You need to ingest data from an on-premises Oracle database into BigQuery. The data volume is large, and you require incremental updates. You want to use a fully managed Google Cloud service that supports change data capture (CDC) and can handle schema changes automatically. Which TWO of the following are true about using Datastream for this purpose? (Choose two.)
A media company ingests clickstream events into a Pub/Sub topic. Analysts want near-real-time dashboards in BigQuery, and the team wants to avoid managing load jobs or writing custom subscriber code. Which approach should they use?
You are using Dataplex to manage a data lake that spans multiple Cloud Storage buckets and BigQuery datasets. You need to provide a group of data scientists with read-only access to all data and metadata within a specific zone, but they should not be able to modify any assets or access other zones. Which IAM role should you grant them on the zone?
A data engineer loads CSV files into a BigQuery table every hour using a load job. Some rows contain a date string in the format `MM/DD/YYYY` in a column defined as STRING, and downstream users need to query it as a DATE. The engineer wants the conversion to happen automatically at load time without changing the source files or running a separate transformation job. What should the engineer do?
A platform team manages a BigQuery dataset containing customer records that include names, email addresses, and account numbers. Leadership requires that sensitive fields be masked for most analysts, that the masking be centrally defined and reusable across tables, and that authorized users still see real values when needed. Which TWO approaches satisfy these requirements? (Choose two.)
Your team manages a BigQuery dataset that contains sensitive customer data. You need to allow analysts to query the data while ensuring they cannot view individual sensitive values, and you also need to know which columns contain sensitive data so that policies can be applied consistently. Which two actions should you take? (Choose two.)
A retail analytics team stores daily sales data in BigQuery. The table is partitioned by DATE(transaction_time) and clustered by store_id. Analysts frequently run queries that filter on a specific store_id but do not filter on transaction_time. They report that these queries scan the entire table and are slow. What should you recommend to improve query performance for these store-specific queries?
A financial services company uses Cloud Storage to store sensitive customer transaction logs. Compliance requires that all data be encrypted at rest with customer-managed encryption keys (CMEK) and that key rotation be automated every 90 days. The company already uses Cloud KMS. They want to ensure that new objects uploaded to a specific bucket are automatically encrypted with a CMEK. What should they do?
You have sensitive data in Cloud Storage that needs to be deleted after 30 days to comply with regulatory requirements. What is the most efficient way to manage this?
You need to ensure that PII (Personally Identifiable Information) in BigQuery is automatically detected and tagged across your organization. Which Dataplex feature should you enable on your Lake?
You need to control access to specific columns in a BigQuery table using Dataplex-integrated policy tags. What must you create before you can apply a policy tag to a column?
You want to enforce that all data in a specific Dataplex Zone must be encrypted with a Customer-Managed Encryption Key (CMEK). How do you enforce this?
You are configuring a Dataplex asset that points to a GCS bucket. You want to ensure that only files in a specific folder are included in the asset. How do you specify this?
You need to organize your data governance strategy. Which TWO of the following are valid ways to use Dataplex Zones to improve your data organization? (Choose two)
You are implementing a security strategy for your data lake. Which THREE of the following are supported ways to control access to data within a Dataplex Lake? (Choose three)
A retail analytics team stores raw clickstream events in a Cloud Storage bucket. They want to query this data with BigQuery using standard SQL without loading it into BigQuery native storage, and they want to minimize cost by avoiding duplicate storage. Which Google Cloud feature should they use?
You are the data engineer for an online retailer. A Cloud Storage bucket named orders-raw contains daily CSV exports from an on-premises order management system. Each file is named with a date stamp, and new files arrive once per day. You need to load this data into BigQuery with minimal ongoing operational effort, and the load must automatically pick up each new file as it arrives. What should you do?
A marketing analyst needs to run a one-time SQL query that joins a BigQuery table containing campaign performance data with a small CSV file stored in a Cloud Storage bucket. The CSV is updated weekly by an external partner. The analyst wants the query to always reflect the latest CSV without loading it into BigQuery each week. What should the analyst do?
You manage a BigQuery dataset that contains a table with millions of rows. Analysts frequently query only the most recent 30 days of data, but full-table scans are expensive. You need to reduce query costs and improve performance for these time-based queries with minimal changes to the table structure. What should you do?
You are designing a data lake in Cloud Storage and need to ensure that data is organized for efficient querying by BigQuery and other analytics services. You plan to use a Hive-style partitioned directory structure. Which two practices should you follow? (Choose two.)
You are designing a BigQuery table for a fleet of IoT devices that emit one reading per second. The table will hold years of data, and most queries filter by device identifier and a time range. You want to reduce the bytes scanned by these queries and control long-term storage cost. (Choose two.)
Your organization stores sensitive customer data in a Cloud Storage bucket. You need to ensure that data is automatically classified and tagged as 'PII' when it is written, and that the tags are available for audit and access control decisions. You want to minimize manual effort and use Google Cloud native tooling. What should you do?
A data engineer needs to load a 500 GB CSV file from a Cloud Storage bucket into a BigQuery table once per day. The CSV has a header row and uses comma delimiters. The engineer wants the simplest managed approach that requires no code and handles schema autodetection. What should the engineer use?
You need to grant a data analyst read-only access to a specific BigQuery dataset. The analyst should be able to query tables but must not be able to modify data or view other datasets in the project. Which IAM role should you grant on the dataset?
You are the data engineer for a retail company on Google Cloud. Analysts query a BigQuery table that contains 400 TB of point-of-sale records and almost always filter by transaction_date. Query costs have grown sharply because every query scans the full table. You need the most cost-effective change that reduces bytes scanned for date-filtered queries without altering the table schema. What should you do?
A healthcare analytics team stores patient encounter records in Cloud Storage as CSV files and needs to run SQL over them in BigQuery without loading or duplicating the data. The files must remain the system of record, and the team wants to avoid storage charges for a second copy. Which BigQuery feature should they use?
You have a BigQuery table that is partitioned by day on a DATE column. You need to ensure that data older than 90 days is automatically deleted to reduce storage costs, while keeping the table structure and recent data intact. What should you do?
You need to load a large CSV file from Cloud Storage into BigQuery. The file contains a header row, and you want to automatically detect the schema. You also need to ensure that if any row has a type mismatch, the load job fails and reports the error. Which load configuration should you use?
A healthcare organization needs to analyze sensitive patient data in BigQuery while ensuring that analysts can see aggregated results but cannot view individual patient identifiers. Which Google Cloud feature should they implement?
Your analytics team stores clickstream events in Cloud Storage as newline-delimited JSON files. They want to query this data with BigQuery using SQL without loading it into native BigQuery storage, and they want the table definition to refresh automatically when new files land in the bucket. What should you configure?
You manage a BigQuery dataset that contains a table with 500 TB of historical order data. Analysts frequently run queries that filter on the `order_date` column. Query costs are rising because every query scans the entire table. You want to reduce the bytes scanned and cost without changing the table name or existing SQL. What should you do?
You are designing a data lake in Cloud Storage and need to ensure that analysts can query the data using BigQuery without moving or copying the data. You also need to enforce fine-grained access control at the column level for sensitive fields. Which two actions should you take? (Choose two.)
Your company stores sensitive customer records in a Cloud Storage bucket. A compliance officer requires that every object in the bucket be automatically classified and that any object containing credit card numbers be flagged for review. You want a managed Google Cloud service that samples and inspects the data without writing custom code. What should you use?
You have a BigQuery table that is partitioned by ingestion time. You need to reduce storage costs by automatically deleting data older than 90 days. You want to ensure that the deletion happens without manual intervention and that the partitions are completely removed. What should you do?
You are designing a data governance strategy for a BigQuery environment. You need to automatically apply policy tags to columns that contain sensitive data so that only authorized users can query those columns. Which two actions should you take? (Choose two.)
A compliance officer requires that a Cloud Storage bucket holding customer records retain every object version for seven years, and that no user, including project owners, be able to delete or overwrite those versions during that period. You need a configuration that enforces this even against accidental or malicious deletion attempts. What should you configure?
A retail company stores point-of-sale events in Pub/Sub and wants them available for SQL analysis in BigQuery with minimal operational overhead and near-real-time latency. The team has no servers to manage and wants the simplest supported path. Which approach should they use?
You are a data analyst at a marketing firm. You need to query a large dataset stored in BigQuery, but you want to minimize query costs. The dataset contains a column named event_time of type TIMESTAMP. Most of your queries filter on event_time for a specific day. What should you do to reduce the amount of data scanned by your queries?
A retail company ingests point-of-sale events into BigQuery. Analysts frequently filter by store_id and by transaction date, and the table grows by several hundred gigabytes per month. You want to reduce the bytes scanned by these filtered queries with the least effort. What should you do?
You are loading a daily CSV file from Cloud Storage into a BigQuery table. The file contains a header row, uses comma delimiters, and has some rows with extra trailing columns. You want the load to tolerate these malformed rows by skipping them and recording how many were skipped, without failing the whole job. Which load configuration should you use?
You are a data engineer at a healthcare company. You need to create a Dataflow pipeline that reads from a Pub/Sub subscription and writes to BigQuery. The pipeline must handle late-arriving data and ensure exactly-once processing. You also need to minimize cost. Which of the following should you use?
Your organization stores raw event data in a Cloud Storage bucket that receives new files throughout the day. Analysts want to query this data with SQL in BigQuery without loading it into native BigQuery storage first. The data is in Parquet format and partitioned by date in the folder structure. What should you create?
A fintech firm streams transaction events into a Pub/Sub topic. The analytics team needs near-real-time aggregates in BigQuery and must also retain the raw events for seven years in Cloud Storage for compliance. They want a managed service with minimal code. Which pipeline should you recommend?