PDE practice questions 311–320

1. Question 311 · 340 in bank

   You're using Bigtable for a real-time application, and you have a heavy load that is a mix of read and writes. You've recently identified an additional use case and need to perform hourly an analytical job to calculate certain statistics across the whole database. You need to ensure both the reliability of your production application as well as the analytical workload. What should you do?

   • AExport Bigtable dump to GCS and run your analytical job on top of the exported files.
   • BAdd a second cluster to an existing instance with a multi-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
   • CAdd a second cluster to an existing instance with a single-cluster routing, use live-traffic app profile for your regular workload and batch-analytics profile for the analytics workload.
   • DIncrease the size of your existing cluster twice and execute your analytics workload on your new resized cluster.

   Show answer

   Answer: C

   Explanation

   Answer is C When you use a single cluster to run a batch analytics job that performs numerous large reads alongside an application that performs a mix of reads and writes, the large batch job can slow things down for the application's users. With replication, you can use app profiles with single-cluster routing to route batch analytics jobs and application traffic to different clusters, so that batch jobs don't affect your applications' users. https://cloud.google.com/bigtable/docs/replication-overview#use-cases

   Source: Adapted from community discussion — [Removed]

   References

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

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2. Question 312 · 340 in bank

   You are designing an Apache Beam pipeline to enrich data from Cloud Pub/Sub with static reference data from BigQuery. The reference data is small enough to fit in memory on a single worker. The pipeline should write enriched results to BigQuery for analysis. Which job type and transforms should this pipeline use?

   • ABatch job, PubSubIO, side-inputs
   • BStreaming job, PubSubIO, JdbcIO, side-outputs
   • CStreaming job, PubSubIO, BigQueryIO, side-inputs
   • DStreaming job, PubSubIO, BigQueryIO, side-outputs

   Show answer

   Answer: C

   Explanation

   C is the answer. https://cloud.google.com/dataflow/docs/tutorials/ecommerce-java#side-input-pattern In streaming analytics applications, data is often enriched with additional information that might be useful for further analysis. For example, if you have the store ID for a transaction, you might want to add information about the store location. This additional information is often added by taking an element and bringing in information from a lookup table.

   Source: Adapted from community discussion — zellck

   References

   • Google Cloud documentation
   • Google Cloud documentation

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3. Question 313 · 340 in bank

   Select 2

   You have a data pipeline that writes data to Cloud Bigtable using well-designed row keys. You want to monitor your pipeline to determine when to increase the size of your Cloud Bigtable cluster. Which two actions can you take to accomplish this? (Choose two.)

   • AReview Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Read pressure index is above 100.
   • BReview Key Visualizer metrics. Increase the size of the Cloud Bigtable cluster when the Write pressure index is above 100.
   • CMonitor the latency of write operations. Increase the size of the Cloud Bigtable cluster when there is a sustained increase in write latency.
   • DMonitor storage utilization. Increase the size of the Cloud Bigtable cluster when utilization increases above 70% of max capacity.
   • EMonitor latency of read operations. Increase the size of the Cloud Bigtable cluster of read operations take longer than 100 ms.

   Show answer

   Answer: CD

   Explanation

   Answer is C & D. C –> Adding more nodes to a cluster (not replication) can improve the write performance https://cloud.google.com/bigtable/docs/performance D –> since Google recommends adding nodes when storage utilization is > 70% https://cloud.google.com/bigtable/docs/modifying-instance#nodes

   Source: Adapted from community discussion — jvg637

   References

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

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4. Question 314 · 340 in bank

   You want to analyze hundreds of thousands of social media posts daily at the lowest cost and with the fewest steps. You have the following requirements: ✑ You will batch-load the posts once per day and run them through the Cloud Natural Language API. ✑ You will extract topics and sentiment from the posts. ✑ You must store the raw posts for archiving and reprocessing. ✑ You will create dashboards to be shared with people both inside and outside your organization. You need to store both the data extracted from the API to perform analysis as well as the raw social media posts for historical archiving. What should you do?

   • AStore the social media posts and the data extracted from the API in BigQuery.
   • BStore the social media posts and the data extracted from the API in Cloud SQL.
   • CStore the raw social media posts in Cloud Storage, and write the data extracted from the API into BigQuery.
   • DFeed to social media posts into the API directly from the source, and write the extracted data from the API into BigQuery.

   Show answer

   Answer: C

   Explanation

   Answer should be C, becose they ask you to save a copy of the raw posts for archival, which may not be possible if you directly feed the posts to the API.

   Source: Adapted from community discussion — psu

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5. Question 315 · 340 in bank

   You store historic data in Cloud Storage. You need to perform analytics on the historic data. You want to use a solution to detect invalid data entries and perform data transformations that will not require programming or knowledge of SQL. What should you do?

   • AUse Cloud Dataflow with Beam to detect errors and perform transformations.
   • BUse Cloud Dataprep with recipes to detect errors and perform transformations.
   • CUse Cloud Dataproc with a Hadoop job to detect errors and perform transformations.
   • DUse federated tables in BigQuery with queries to detect errors and perform transformations.

   Show answer

   Answer: B

   Explanation

   Yes B. Honest speaking, sometime I thought the answers being posted here were intentionally to mislead people whose do not have proper knowledge on the subject, but just memorizing answers to pass the exam.

   Source: Adapted from community discussion — rickywck

   References

   • Google Cloud documentation
   • Google Cloud documentation

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6. Question 316 · 340 in bank

   Your company needs to upload their historic data to Cloud Storage. The security rules don't allow access from external IPs to their on-premises resources. After an initial upload, they will add new data from existing on-premises applications every day. What should they do?

   • AExecute gsutil rsync from the on-premises servers.
   • BUse Dataflow and write the data to Cloud Storage.
   • CWrite a job template in Dataproc to perform the data transfer.
   • DInstall an FTP server on a Compute Engine VM to receive the files and move them to Cloud Storage.

   Show answer

   Answer: A

   Explanation

   should be A, dataflow is on cloud is external; "don't allow access from external IPs to their on-premises resources" so no dataflow.

   Source: Adapted from community discussion — itche_scratche

   References

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

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7. Question 317 · 340 in bank

   You have a query that filters a BigQuery table using a WHERE clause on timestamp and ID columns. By using bq query `"-dry_run you learn that the query triggers a full scan of the table, even though the filter on timestamp and ID select a tiny fraction of the overall data. You want to reduce the amount of data scanned by BigQuery with minimal changes to existing SQL queries. What should you do?

   • ACreate a separate table for each ID.
   • BUse the LIMIT keyword to reduce the number of rows returned.
   • CRecreate the table with a partitioning column and clustering column.
   • DUse the bq query --maximum_bytes_billed flag to restrict the number of bytes billed.

   Show answer

   Answer: C

   Explanation

   C is the answer. https://cloud.google.com/bigquery/docs/partitioned-tables A partitioned table is a special table that is divided into segments, called partitions, that make it easier to manage and query your data. By dividing a large table into smaller partitions, you can improve query performance, and you can control costs by reducing the number of bytes read by a query. https://cloud.google.com/bigquery/docs/clustered-tables lustered tables in BigQuery are tables that have a user-defined column sort order using clustered columns. Clustered tables can improve query performance and reduce query costs.

   Source: Adapted from community discussion — zellck

   References

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

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8. Question 318 · 340 in bank

   Select 2

   Your company uses Looker Studio connected to BigQuery for reporting. Users are experiencing slow dashboard load times due to complex queries on a large table. The queries involve aggregations and filtering on several columns. You need to optimize query performance to decrease the dashboard load times. What should you do?

   • AConfigure Looker Studio to use a shorter data refresh interval to ensure fresh data is always displayed.
   • BCreate a materialized view in BigQuery that pre-calculates the aggregations and filters used in the Looker Studio dashboards.
   • CImplement row-level security in BigQuery to restrict data access and reduce the amount of data processed by the queries.
   • DUse BigQuery BI Engine to accelerate query performance by caching frequently accessed data.

   Show answer

   Answer: BD

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9. Question 319 · 340 in bank

   Your company is planning to migrate a large on-premises data warehouse to BigQuery. The data is currently stored in a proprietary, vendor-specific format. You need to perform a batch migration of this data to BigQuery. What should you do?

   • AUse the bq command-line tool to load the data directly from the on-premises data warehouse.
   • BExport the data to CSV files, upload the files to Cloud Storage, then load the files into BigQuery.
   • CUse the BigQuery Data Transfer Service.
   • DUse Datastream to replicate the data in real time.

   Show answer

   Answer: B

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10. Question 320 · 340 in bank

    Your company wants to implement a Retrieval-Augmented Generation (RAG) system to allow employees to query an extensive knowledge base of internal documents, such as policy manuals and project reports. You need to prepare this unstructured text for embedding to be used in the RAG system. What should you do to ensure the system can retrieve the most relevant information?

    • AUse Cloud Data Loss Prevention (Cloud DLP) to scan and redact sensitive information within the documents before processing.
    • BStore the documents as compressed files in a traditional relational database to enable more efficient storage and retrieval.
    • CConvert the unstructured documents into high-dimensional numerical vectors that capture the semantic meaning and relationships of the text.
    • DIndex each word from the documents into a search engine to enable keyword-based search.

    Show answer

    Answer: C

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PDE practice, question 311. 340 questions available.