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NEW QUESTION # 27
Your company is running their first dynamic campaign, serving different offers by analyzing real-time data during the holiday season. The data scientists are collecting terabytes of data that rapidly grows every hour during their 30-day campaign. They are using Google Cloud Dataflow to preprocess the data and collect the feature (signals) data that is needed for the machine learning model in Google Cloud Bigtable. The team is observing suboptimal performance with reads and writes of their initial load of 10 TB of dat
a. They want to improve this performance while minimizing cost. What should they do?
- A. Redesign the schema to use row keys based on numeric IDs that increase sequentially per user viewing the offers.
- B. Redesign the schema to use a single row key to identify values that need to be updated frequently in the cluster.
- C. Redefine the schema by evenly distributing reads and writes across the row space of the table.
- D. The performance issue should be resolved over time as the site of the BigDate cluster is increased.
Answer: C
NEW QUESTION # 28
The CUSTOM tier for Cloud Machine Learning Engine allows you to specify the number of which types of cluster nodes?
- A. Parameter servers
- B. Workers and parameter servers
- C. Masters, workers, and parameter servers
- D. Workers
Answer: B
Explanation:
The CUSTOM tier is not a set tier, but rather enables you to use your own cluster specification. When you use this tier, set values to configure your processing cluster according to these guidelines:
You must set TrainingInput.masterType to specify the type of machine to use for your master node.
You may set TrainingInput.workerCount to specify the number of workers to use.
You may set TrainingInput.parameterServerCount to specify the number of parameter servers to use.
You can specify the type of machine for the master node, but you can't specify more than one master node.
NEW QUESTION # 29
Your company produces 20,000 files every hour. Each data file is formatted as a comma separated values (CSV) file that is less than 4 KB. All files must be ingested on Google Cloud Platform before they can be processed. Your company site has a 200 ms latency to Google Cloud, and your Internet connection bandwidth is limited as 50 Mbps. You currently deploy a secure FTP (SFTP) server on a virtual machine in Google Compute Engine as the data ingestion point. A local SFTP client runs on a dedicated machine to transmit the CSV files as is. The goal is to make reports with data from the previous day available to the executives by
10:00 a.m. each day. This design is barely able to keep up with the current volume, even though the bandwidth utilization is rather low.
You are told that due to seasonality, your company expects the number of files to double for the next three months. Which two actions should you take? (choose two.)
- A. Redesign the data ingestion process to use gsutil tool to send the CSV files to a storage bucket in parallel.
- B. Introduce data compression for each file to increase the rate file of file transfer.
- C. Assemble 1,000 files into a tape archive (TAR) file. Transmit the TAR files instead, and disassemble the CSV files in the cloud upon receiving them.
- D. Create an S3-compatible storage endpoint in your network, and use Google Cloud Storage Transfer Service to transfer on-premices data to the designated storage bucket.
- E. Contact your internet service provider (ISP) to increase your maximum bandwidth to at least 100 Mbps.
Answer: A,D
NEW QUESTION # 30
Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub streaming data, one of the important business requirements is to be able to periodically identify the inputs and their timings during their campaign. Engineers have decided to use windowing and transformation in Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?
- A. They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created
- B. They have not applied a global windowing function, which causes the job to fail when the pipeline is created
- C. They have not set the triggers to accommodate the data coming in late, which causes the job to fail
- D. They have not assigned the timestamp, which causes the job to fail
Answer: A
NEW QUESTION # 31
You need to move 2 PB of historical data from an on-premises storage appliance to Cloud Storage within six months, and your outbound network capacity is constrained to 20 Mb/sec. How should you migrate this data to Cloud Storage?
- A. Use trickle or ionice along with gsutil cp to limit the amount of bandwidth gsutil utilizes to less than 20 Mb/ sec so it does not interfere with the production traffic
- B. Use Transfer Appliance to copy the data to Cloud Storage
- C. Create a private URL for the historical data, and then use Storage Transfer Service to copy the data to Cloud Storage
- D. Use gsutil cp -Jto compress the content being uploaded to Cloud Storage
Answer: B
Explanation:
Explanation
NEW QUESTION # 32
Your weather app queries a database every 15 minutes to get the current temperature. The frontend is powered by Google App Engine and server millions of users. How should you design the frontend to respond to a database failure?
- A. Reduce the query frequency to once every hour until the database comes back online.
- B. Retry the query every second until it comes back online to minimize staleness of data.
- C. Issue a command to restart the database servers.
- D. Retry the query with exponential backoff, up to a cap of 15 minutes.
Answer: D
NEW QUESTION # 33
You are designing a Dataflow pipeline for a batch processing job. You want to mitigate multiple zonal failures at job submission time. What should you do?
- A. Create an Eventarc trigger to resubmit the job in case of zonal failure when submitting the job.
- B. Specify a worker region by using the -region flag.
- C. Set the pipeline staging location as a regional Cloud Storage bucket.
- D. Submit duplicate pipelines in two different zones by using the -zone flag.
Answer: B
Explanation:
By specifying a worker region, you can run your Dataflow pipeline in a multi-zone or multi-region configuration, which provides higher availability and resilience in case of zonal failures1. The -region flag allows you to specify the regional endpoint for your pipeline, which determines the location of the Dataflow service and the default location of the Compute Engine resources1. If you do not specify a zone by using the
-zone flag, Dataflow automatically selects a zone within the region for your job workers1. This option is recommended over submitting duplicate pipelines in two different zones, which would incur additional costs and complexity. Setting the pipeline staging location as a regional Cloud Storage bucket does not affect the availability of your pipeline, as the staging location only stores the pipeline code and dependencies2. Creating an Eventarc trigger to resubmit the job in case of zonal failure is not a reliable solution, as it depends on the availability of the Eventarc service and the zonal resources at the time of resubmission. References:
* 1: Pipeline troubleshooting and debugging | Cloud Dataflow | Google Cloud
* 3: Regional endpoints | Cloud Dataflow | Google Cloud
NEW QUESTION # 34
You work for a large real estate firm and are preparing 6 TB of home sales data lo be used for machine learning You will use SOL to transform the data and use BigQuery ML lo create a machine learning model.
You plan to use the model for predictions against a raw dataset that has not been transformed. How should you set up your workflow in order to prevent skew at prediction time?
- A. When creating your model, use BigQuery's TRANSFORM clause to define preprocessing steps Before requesting predictions, use a saved query to transform your raw input data, and then use ML.
EVALUATE - B. Use a BigOuery to define your preprocessing logic. When creating your model, use the view as your model training data. At prediction lime, use BigQuery's ML EVALUATE clause without specifying any transformations on the raw input data.
- C. When creating your model, use BigQuerys TRANSFORM clause to define preprocessing stops. At prediction time, use BigQuery"s ML. EVALUATE clause without specifying any transformations on the raw input data.
- D. Preprocess all data using Dataflow. At prediction time, use BigOuery"s ML. EVALUATE clause without specifying any further transformations on the input data.
Answer: C
Explanation:
https://cloud.google.com/bigquery-ml/docs/bigqueryml-transform Using the TRANSFORM clause, you can specify all preprocessing during model creation. The preprocessing is automatically applied during the prediction and evaluation phases of machine learning
NEW QUESTION # 35
You are designing storage for two relational tables that are part of a 10-TB database on Google Cloud. You want to support transactions that scale horizontally. You also want to optimize data for range queries on nonkey columns. What should you do?
- A. Use Cloud Spanner for storage. Add secondary indexes to support query patterns.
- B. Use Cloud SQL for storage. Use Cloud Dataflow to transform data to support query patterns.
- C. Use Cloud Spanner for storage. Use Cloud Dataflow to transform data to support query patterns.
- D. Use Cloud SQL for storage. Add secondary indexes to support query patterns.
Answer: C
NEW QUESTION # 36
You have a BigQuery table that ingests data directly from a Pub/Sub subscription. The ingested data is encrypted with a Google-managed encryption key. You need to meet a new organization policy that requires you to use keys from a centralized Cloud Key Management Service (Cloud KMS) project to encrypt data at rest. What should you do?
- A. Use Cloud KMS encryption key with Dataflow to ingest the existing Pub/Sub subscription to the existing BigQuery table.
- B. Create a new BigOuory table by using customer-managed encryption keys (CMEK), and migrate the data from the old BigQuery table.
- C. Create a new Pub/Sub topic with CMEK and use the existing BigQuery table by using Google-managed encryption key.
- D. Create a new BigOuery table and Pub/Sub topic by using customer-managed encryption keys (CMEK), and migrate the data from the old Bigauery table.
Answer: B
Explanation:
To use CMEK for BigQuery, you need to create a key ring and a key in Cloud KMS, and then specify the key resource name when creating or updating a BigQuery table. You cannot change the encryption type of an existing table, so you need to create a new table with CMEK and copy the data from the old table with Google-managed encryption key.
References:
* Customer-managed Cloud KMS keys | BigQuery | Google Cloud
* Creating and managing encryption keys | Cloud KMS Documentation | Google Cloud
NEW QUESTION # 37
Your company is using WILDCARD tables to query data across multiple tables with similar names. The SQL statement is currently failing with the following error:
# Syntax error : Expected end of statement but got "-" at [4:11]
SELECT age
FROM
bigquery-public-data.noaa_gsod.gsod
WHERE
age != 99
AND_TABLE_SUFFIX = '1929'
ORDER BY
age DESC
Which table name will make the SQL statement work correctly?
- A. 'bigquery-public-data.noaa_gsod.gsod'
- B. 'bigquery-public-data.noaa_gsod.gsod*`
- C. bigquery-public-data.noaa_gsod.gsod*
- D. 'bigquery-public-data.noaa_gsod.gsod'*
Answer: B
NEW QUESTION # 38
You operate an IoT pipeline built around Apache Kafka that normally receives around 5000 messages per second. You want to use Google Cloud Platform to create an alert as soon as the moving average over 1 hour drops below 4000 messages per second. What should you do?
- A. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to Cloud Bigtable. Use Cloud Scheduler to run a script every hour that counts the number of rows created in Cloud Bigtable in the last hour. If that number falls below 4000, send an alert.
- B. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a fixed time window of 1 hour.
Compute the average when the window closes, and send an alert if the average is less than 4000 messages. - C. Consume the stream of data in Cloud Dataflow using Kafka IO. Set a sliding time window of 1 hour every 5 minutes. Compute the average when the window closes, and send an alert if the average is less than 4000 messages.
- D. Use Kafka Connect to link your Kafka message queue to Cloud Pub/Sub. Use a Cloud Dataflow template to write your messages from Cloud Pub/Sub to BigQuery. Use Cloud Scheduler to run a script every five minutes that counts the number of rows created in BigQuery in the last hour. If that number falls below 4000, send an alert.
Answer: A
NEW QUESTION # 39
You are developing a model to identify the factors that lead to sales conversions for your customers. You have completed processing your data. You want to continue through the model development lifecycle. What should you do next?
- A. Use your model to run predictions on fresh customer input data.
- B. Test and evaluate your model on your curated data to determine how well the model performs.
- C. Monitor your model performance, and make any adjustments needed.
- D. Delineate what data will be used for testing and what will be used for training the model.
Answer: B
Explanation:
After processing your data, the next step in the model development lifecycle is to test and evaluate your model on the curated data. This is crucial to determine the performance of the model and to understand how well it can predict sales conversions for your customers. The evaluation phase involves using various metrics and techniques to assess the accuracy, precision, recall, and other relevant performance indicators of the model. It helps in identifying any issues or areas for improvement before deploying the model in a production environment. References: The information provided here is verified by the Google Professional Data Engineer Certification Exam Guide and related resources, which outline the steps and best practices in the model development lifecycle
NEW QUESTION # 40
You are designing a cloud-native historical data processing system to meet the following conditions:
* The data being analyzed is in CSV, Avro, and PDF formats and will be accessed by multiple analysis tools including Cloud Dataproc, BigQuery, and Compute Engine.
* A streaming data pipeline stores new data daily.
* Peformance is not a factor in the solution.
* The solution design should maximize availability.
How should you design data storage for this solution?
- A. Store the data in a multi-regional Cloud Storage bucket. Access the data directly using Cloud Dataproc, BigQuery, and Compute Engine.
- B. Create a Cloud Dataproc cluster with high availability. Store the data in HDFS, and peform analysis as needed.
- C. Store the data in BigQuery. Access the data using the BigQuery Connector on Cloud Dataproc and Compute Engine.
- D. Store the data in a regional Cloud Storage bucket. Access the bucket directly using Cloud Dataproc, BigQuery, and Compute Engine.
Answer: D
NEW QUESTION # 41
Which of these statements about BigQuery caching is true?
- A. Query results are cached even if you specify a destination table.
- B. There is no charge for a query that retrieves its results from cache.
- C. BigQuery caches query results for 48 hours.
- D. By default, a query's results are not cached.
Answer: B
Explanation:
When query results are retrieved from a cached results table, you are not charged for the query. BigQuery caches query results for 24 hours, not 48 hours. Query results are not cached if you specify a destination table. A query's results are always cached except under certain conditions, such as if you specify a destination table.
Reference: https://cloud.google.com/bigquery/querying-data#query-caching
NEW QUESTION # 42
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?
- A. Streaming job, PubSubIO, BigQueryIO, side-outputs
- B. Streaming job, PubSubIO, JdbcIO, side-outputs
- C. Batch job, PubSubIO, side-inputs
- D. Streaming job, PubSubIO, BigQueryIO, side-inputs
Answer: C
NEW QUESTION # 43
Your company receives both batch- and stream-based event data. You want to process the data using Google Cloud Dataflow over a predictable time period. However, you realize that in some instances data can arrive late or out of order. How should you design your Cloud Dataflow pipeline to handle data that is late or out of order?
- A. Set sliding windows to capture all the lagged data.
- B. Ensure every datasource type (stream or batch) has a timestamp, and use the timestamps to define the logic for lagged data.
- C. Use watermarks and timestamps to capture the lagged data.
- D. Set a single global window to capture all the data.
Answer: A
NEW QUESTION # 44
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