PDE practice questions 201–210

1. Question 201 · 340 in bank

   You work for a large ecommerce company. You store your customer's order data in Bigtable. You have a garbage collection policy set to delete the data after 30 days and the number of versions is set to 1. When the data analysts run a query to report total customer spending, the analysts sometimes see customer data that is older than 30 days. You need to ensure that the analysts do not see customer data older than 30 days while minimizing cost and overhead. What should you do?

   • ASet the expiring values of the column families to 29 days and keep the number of versions to 1.
   • BUse a timestamp range filter in the query to fetch the customer's data for a specific range.
   • CSchedule a job daily to scan the data in the table and delete data older than 30 days.
   • DSet the expiring values of the column families to 30 days and set the number of versions to 2.

   Show answer

   Answer: B

   Explanation

   Agree. https://cloud.google.com/bigtable/docs/garbage-collection#data-removed "Because it can take up to a week for expired data to be deleted, you should never rely solely on garbage collection policies to ensure that read requests return the desired data. Always apply a filter to your read requests that excludes the same values as your garbage collection rules. You can filter by limiting the number of cells per column or by specifying a timestamp range."

   Source: Adapted from community discussion — AllenChen123

   References

   • Google Cloud documentation

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2. Question 202 · 340 in bank

   You are using a Dataflow streaming job to read messages from a message bus that does not support exactly-once delivery. Your job then applies some transformations, and loads the result into BigQuery. You want to ensure that your data is being streamed into BigQuery with exactly-once delivery semantics. You expect your ingestion throughput into BigQuery to be about 1.5 GB per second. What should you do?

   • AUse the BigQuery Storage Write API and ensure that your target BigQuery table is regional.
   • BUse the BigQuery Storage Write API and ensure that your target BigQuery table is multiregional.
   • CUse the BigQuery Streaming API and ensure that your target BigQuery table is regional.
   • DUse the BigQuery Streaming API and ensure that your target BigQuery table is multiregional.

   Show answer

   Answer: B

   Explanation

   streamed into BigQuery with exactly-once delivery semantics >>> Storage Write API ingestion throughput into BigQuery to be about 1.5 GB per second >>> multiregional (check throughput rate here >>> https://cloud.google.com/bigquery/quotas#write-api-limits)

   Source: Adapted from community discussion — m_a_p_s

   References

   • Google Cloud documentation
   • Google Cloud documentation

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3. Question 203 · 340 in bank

   You have created an external table for Apache Hive partitioned data that resides in a Cloud Storage bucket, which contains a large number of files. You notice that queries against this table are slow. You want to improve the performance of these queries. What should you do?

   • AChange the storage class of the Hive partitioned data objects from Coldline to Standard.
   • BCreate an individual external table for each Hive partition by using a common table name prefix. Use wildcard table queries to reference the partitioned data.
   • CUpgrade the external table to a BigLake table. Enable metadata caching for the table.
   • DMigrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.

   Show answer

   Answer: C

   Explanation

   - BigLake Table: BigLake allows for more efficient querying of data lakes stored in Cloud Storage. It can handle large datasets more effectively than standard external tables. - Metadata Caching: Enabling metadata caching can significantly improve query performance by reducing the time taken to read and process metadata from a large number of files.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation
   • Google Cloud documentation

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4. Question 204 · 340 in bank

   You have a network of 1000 sensors. The sensors generate time series data: one metric per sensor per second, along with a timestamp. You already have 1 TB of data, and expect the data to grow by 1 GB every day. You need to access this data in two ways. The first access pattern requires retrieving the metric from one specific sensor stored at a specific timestamp, with a median single-digit millisecond latency. The second access pattern requires running complex analytic queries on the data, including joins, once a day. How should you store this data?

   • AStore your data in BigQuery. Concatenate the sensor ID and timestamp, and use it as the primary key.
   • BStore your data in Bigtable. Concatenate the sensor ID and timestamp and use it as the row key. Perform an export to BigQuery every day.
   • CStore your data in Bigtable. Concatenate the sensor ID and metric, and use it as the row key. Perform an export to BigQuery every day.
   • DStore your data in BigQuery. Use the metric as a primary key.

   Show answer

   Answer: B

   Explanation

   - Bigtable excels at incredibly fast lookups by row key, often reaching single-digit millisecond latencies. - Constructing the row key with sensor ID and timestamp enables efficient retrieval of specific sensor readings at exact timestamps. - Bigtable's wide-column design effectively stores time series data, allowing for flexible addition of new metrics without schema changes. - Bigtable scales horizontally to accommodate massive datasets (petabytes or more), easily handling the expected data growth.

   Source: Adapted from community discussion — raaad

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5. Question 205 · 340 in bank

   You have 100 GB of data stored in a BigQuery table. This data is outdated and will only be accessed one or two times a year for analytics with SQL. For backup purposes, you want to store this data to be immutable for 3 years. You want to minimize storage costs. What should you do?

   • A1. Create a BigQuery table clone. 2. Query the clone when you need to perform analytics.
   • B1. Create a BigQuery table snapshot. 2. Restore the snapshot when you need to perform analytics.
   • C1. Perform a BigQuery export to a Cloud Storage bucket with archive storage class. 2. Enable versioning on the bucket. 3. Create a BigQuery external table on the exported files.
   • D1. Perform a BigQuery export to a Cloud Storage bucket with archive storage class. 2. Set a locked retention policy on the bucket. 3. Create a BigQuery external table on the exported files.

   Show answer

   Answer: D

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6. Question 206 · 340 in bank

   You have thousands of Apache Spark jobs running in your on-premises Apache Hadoop cluster. You want to migrate the jobs to Google Cloud. You want to use managed services to run your jobs instead of maintaining a long-lived Hadoop cluster yourself. You have a tight timeline and want to keep code changes to a minimum. What should you do?

   • AMove your data to BigQuery. Convert your Spark scripts to a SQL-based processing approach.
   • BRewrite your jobs in Apache Beam. Run your jobs in Dataflow.
   • CCopy your data to Compute Engine disks. Manage and run your jobs directly on those instances.
   • DMove your data to Cloud Storage. Run your jobs on Dataproc.

   Show answer

   Answer: D

   Explanation

   D) That is what Dataproc is made for. It is a fully managed and highly scalable service for running Apache Hadoop, Apache Spark, etc.

   Source: Adapted from community discussion — ML6

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7. Question 207 · 340 in bank

   You are administering shared BigQuery datasets that contain views used by multiple teams in your organization. The marketing team is concerned about the variability of their monthly BigQuery analytics spend using the on-demand billing model. You need to help the marketing team establish a consistent BigQuery analytics spend each month. What should you do?

   • ACreate a BigQuery Enterprise reservation with a baseline of 250 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly.
   • BEstablish a BigQuery quota for the marketing team, and limit the maximum number of bytes scanned each day.
   • CCreate a BigQuery reservation with a baseline of 500 slots with no autoscaling for the marketing team, and bill them back accordingly.
   • DCreate a BigQuery Standard pay-as-you go reservation with a baseline of 0 slots and autoscaling set to 500 for the marketing team, and bill them back accordingly.

   Show answer

   Answer: C

   Explanation

   Reservations guarantee a fixed number of slots (computational resources) for BigQuery queries, ensuring a predictable monthly cost, addressing the marketing team's concern about variability.

   Source: Adapted from community discussion — raaad

   References

   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation
   • Google Cloud documentation

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8. Question 208 · 340 in bank

   You are part of a healthcare organization where data is organized and managed by respective data owners in various storage services. As a result of this decentralized ecosystem, discovering and managing data has become difficult. You need to quickly identify and implement a cost-optimized solution to assist your organization with the following: • Data management and discovery • Data lineage tracking • Data quality validation How should you build the solution?

   • AUse BigLake to convert the current solution into a data lake architecture.
   • BBuild a new data discovery tool on Google Kubernetes Engine that helps with new source onboarding and data lineage tracking.
   • CUse BigQuery to track data lineage, and use Dataprep to manage data and perform data quality validation.
   • DUse Dataplex to manage data, track data lineage, and perform data quality validation.

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   Answer: D

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9. Question 209 · 340 in bank

   You have data located in BigQuery that is used to generate reports for your company. You have noticed some weekly executive report fields do not correspond to format according to company standards. For example, report errors include different telephone formats and different country code identifiers. This is a frequent issue, so you need to create a recurring job to normalize the data. You want a quick solution that requires no coding. What should you do?

   • AUse Cloud Data Fusion and Wrangler to normalize the data, and set up a recurring job.
   • BUse Dataflow SQL to create a job that normalizes the data, and that after the first run of the job, schedule the pipeline to execute recurrently.
   • CCreate a Spark job and submit it to Dataproc Serverless.
   • DUse BigQuery and GoogleSQL to normalize the data, and schedule recurring queries in BigQuery.

   Show answer

   Answer: A

   Explanation

   Wouldn't writing the SQL transformation be considered coding? The question specifically states that a solution requiring no coding is needed.

   Source: Adapted from community discussion — RenePetersen

   References

   • Google Cloud documentation

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10. Question 210 · 340 in bank

    Your company is streaming real-time sensor data from their factory floor into Bigtable and they have noticed extremely poor performance. How should the row key be redesigned to improve Bigtable performance on queries that populate real-time dashboards?

    • AUse a row key of the form <timestamp>.
    • BUse a row key of the form <sensorid>.
    • CUse a row key of the form <timestamp>#<sensorid>.
    • DUse a row key of the form >#<sensorid>#<timestamp>.

    Show answer

    Answer: D

    Explanation

    Description: Best practices of bigtable states that rowkey should not be only timestamp or have timestamp at starting. It’s better to have sensorid and timestamp as rowkey

    Source: Adapted from community discussion — [Removed]

    References

    • Google Cloud documentation

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PDE practice, question 201. 340 questions available.