PROFESSIONAL-DATA-ENGINEER Online Practice Questions and Answers

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?

A. Use a row key of the form .

B. Use a row key of the form .

C. Use a row key of the form #.

D. Use a row key of the form >##.

Your company has a hybrid cloud initiative. You have a complex data pipeline that moves data between cloud provider services and leverages services from each of the cloud providers. Which cloud-native service should you use to orchestrate the entire pipeline?

A. Cloud Dataflow

B. Cloud Composer

C. Cloud Dataprep

D. Cloud Dataproc

You used Cloud Dataprep to create a recipe on a sample of data in a BigQuery table. You want to reuse this recipe on a daily upload of data with the same schema, after the load job with variable execution time completes. What should you do?

A. Create a cron schedule in Cloud Dataprep.

B. Create an App Engine cron job to schedule the execution of the Cloud Dataprep job.

C. Export the recipe as a Cloud Dataprep template, and create a job in Cloud Scheduler.

D. Export the Cloud Dataprep job as a Cloud Dataflow template, and incorporate it into a Cloud Composer job.

You are migrating a large number of files from a public HTTPS endpoint to Cloud Storage. The files are protected from unauthorized access using signed URLs. You created a TSV file that contains the list of object URLs and started a transfer job by using Storage Transfer Service. You notice that the job has run for a long time and eventually failed Checking the logs of the transfer job reveals that the job was running fine until one point, and then it failed due to HTTP 403 errors on the remaining files You verified that there were no changes to the source system You need to fix the problem to resume the migration process. What should you do?

A. Set up Cloud Storage FUSE, and mount the Cloud Storage bucket on a Compute Engine Instance Remove the completed files from the TSV file Use a shell script to iterate through the TSV file and download the remaining URLs to the FUSE mount point.

B. Update the file checksums in the TSV file from using MD5 to SHA256. Remove the completed files from the TSV file and rerun the Storage Transfer Service job.

C. Renew the TLS certificate of the HTTPS endpoint Remove the completed files from the TSV file and rerun the Storage Transfer Service job.

D. Create a new TSV file for the remaining files by generating signed URLs with a longer validity period. Split the TSV file into multiple smaller files and submit them as separate Storage Transfer Service jobs in parallel.

You are designing a fault-tolerant architecture to store data in a regional BigOuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution. What should you do?

A. Export the data from BigQuery into a new table that excludes the corrupted data.

B. Migrate your data to multi-region BigQuery buckets.

C. Access historical data by using time travel in BigQuery.

D. Create a BigQuery table snapshot on a daily basis.

Your organization has been collecting and analyzing data in Google BigQuery for 6 months. The majority of the data analyzed is placed in a time-partitioned table named events_partitioned. To reduce the cost of queries, your organization created a view called events, which queries only the last 14 days of data. The view is described in legacy SQL. Next month, existing applications will be connecting to BigQuery to read the events data via an ODBC connection. You need to ensure the applications can connect. Which two actions should you take? (Choose two.)

A. Create a new view over events using standard SQL

B. Create a new partitioned table using a standard SQL query

C. Create a new view over events_partitioned using standard SQL

D. Create a service account for the ODBC connection to use for authentication

E. Create a Google Cloud Identity and Access Management (Cloud IAM) role for the ODBC connection and shared "events"

You need to create a data pipeline that copies time-series transaction data so that it can be queried from within BigQuery by your data science team for analysis. Every hour, thousands of transactions are updated with a new status. The size of the intitial dataset is 1.5 PB, and it will grow by 3 TB per day. The data is heavily structured, and your data science team will build machine learning models based on this data. You want to maximize performance and usability for your data science team. Which two strategies should you adopt? Choose 2 answers.

A. Denormalize the data as must as possible.

B. Preserve the structure of the data as much as possible.

C. Use BigQuery UPDATE to further reduce the size of the dataset.

D. Develop a data pipeline where status updates are appended to BigQuery instead of updated.

E. Copy a daily snapshot of transaction data to Cloud Storage and store it as an Avro file.Use BigQuery's support for external data sources to query.

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 assigned the timestamp, which causes the job to fail

B. They have not set the triggers to accommodate the data coming in late, which causes the job to fail

C. They have not applied a global windowing function, which causes the job to fail when the pipeline is created

D. They have not applied a non-global windowing function, which causes the job to fail when the pipeline is created

If a dataset contains rows with individual people and columns for year of birth, country, and income, how many of the columns are continuous and how many are categorical?

A. 1 continuous and 2 categorical

B. 3 categorical

C. 3 continuous

D. 2 continuous and 1 categorical

The YARN ResourceManager and the HDFS NameNode interfaces are available on a Cloud Dataproc cluster ____.

A. application node

B. conditional node

C. master node

D. worker node

Which of these statements about exporting data from BigQuery is false?

A. To export more than 1 GB of data, you need to put a wildcard in the destination filename.

B. The only supported export destination is Google Cloud Storage.

C. Data can only be exported in JSON or Avro format.

D. The only compression option available is GZIP.

Which of these are examples of a value in a sparse vector? (Select 2 answers.)

A. [0, 5, 0, 0, 0, 0]

B. [0, 0, 0, 1, 0, 0, 1]

C. [0, 1]

D. [1, 0, 0, 0, 0, 0, 0]

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

1.

Data management and discovery

2.

Data lineage tracking

3.

Data quality validation

How should you build the solution?

A. Use BigLake to convert the current solution into a data lake architecture.

B. Build a new data discovery tool on Google Kubernetes Engine that helps with new source onboarding and data lineage tracking.

C. Use BigOuery to track data lineage, and use Dataprep to manage data and perform data quality validation.

D. Use Dataplex to manage data, track data lineage, and perform data quality validation.

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?

A. Migrate the Hive partitioned data objects to a multi-region Cloud Storage bucket.

B. Create an individual external table for each Hive partition by using a common table name prefix Use wildcard table queries to reference the partitioned data.

C. Change the storage class of the Hive partitioned data objects from Coldline to Standard.

D. Upgrade the external table to a BigLake table Enable metadata caching for the table.

You operate a database that stores stock trades and an application that retrieves average stock price for a given company over an adjustable window of time. The data is stored in Cloud Bigtable where the datetime of the stock trade is the beginning of the row key. Your application has thousands of concurrent users, and you notice that performance is starting to degrade as more stocks are added. What should you do to improve the performance of your application?

A. Change the row key syntax in your Cloud Bigtable table to begin with the stock symbol.

B. Change the row key syntax in your Cloud Bigtable table to begin with a random number per second.

C. Change the data pipeline to use BigQuery for storing stock trades, and update your application.

D. Use Cloud Dataflow to write summary of each day's stock trades to an Avro file on Cloud Storage. Update your application to read from Cloud Storage and Cloud Bigtable to compute the responses.