Google Google Certifications PROFESSIONAL-DATA-ENGINEER Questions & Answers

• Question 291:

  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.

  Correct Answer: D

  Dataplex is a Google Cloud service that provides a unified data fabric for data lakes and data warehouses. It enables data governance, management, and discovery across multiple data domains, zones, and assets. Dataplex also supports data lineage tracking, which shows the origin and transformation of data over time. Dataplex also integrates with Dataprep, a data preparation and quality tool that allows users to clean, enrich, and transform data using a visual interface. Dataprep can also monitor data quality and detect anomalies using machine learning. Therefore, Dataplex is the most suitable solution for the given scenario, as it meets all the requirements of data management and discovery, data lineage tracking, and data quality validation. References: Dataplex overview Automate data governance, extend your data fabric with Dataplex-BigLake integration Dataprep documentation

• Question 292:

  You need to create a new transaction table in Cloud Spanner that stores product sales data. You are deciding what to use as a primary key. From a performance perspective, which strategy should you choose?

  A. The current epoch time

  B. A concatenation of the product name and the current epoch time

  C. A random universally unique identifier number (version 4 UUID)

  D. The original order identification number from the sales system, which is a monotonically increasing integer

  Correct Answer: C

• Question 293:

  You're training a model to predict housing prices based on an available dataset with real estate properties. Your plan is to train a fully connected neural net, and you've discovered that the dataset contains latitude and longtitude of the property. Real estate professionals have told you that the location of the property is highly influential on price, so you'd like to engineer a feature that incorporates this physical dependency.

  What should you do?

  A. Provide latitude and longtitude as input vectors to your neural net.

  B. Create a numeric column from a feature cross of latitude and longtitude.

  C. Create a feature cross of latitude and longtitude, bucketize at the minute level and use L1 regularization during optimization.

  D. Create a feature cross of latitude and longtitude, bucketize it at the minute level and use L2 regularization during optimization.

  Correct Answer: C

  Reference https://cloud.google.com/bigquery/docs/gis-dataa

• Question 294:

  You are responsible for writing your company's ETL pipelines to run on an Apache Hadoop cluster. The pipeline will require some checkpointing and splitting pipelines. Which method should you use to write the pipelines?

  A. PigLatin using Pig

  B. HiveQL using Hive

  C. Java using MapReduce

  D. Python using MapReduce

  Correct Answer: D

• Question 295:

  An aerospace company uses a proprietary data format to store its night data. You need to connect this new data source to BigQuery and stream the data into BigQuery. You want to efficiency import the data into BigQuery where consuming

  as few resources as possible.

  What should you do?

  A. Use a standard Dataflow pipeline to store the raw data m BigQuery and then transform the format later when the data is used

  B. Write a she script that triggers a Cloud Function that performs periodic ETL batch jobs on the new data source

  C. Use Apache Hive to write a Dataproc job that streams the data into BigQuery in CSV format

  D. Use an Apache Beam custom connector to write a Dataflow pipeline that streams the data into BigQuery in Avro format

  Correct Answer: D

• Question 296:

  You are running a streaming pipeline with Dataflow and are using hopping windows to group the data as the data arrives. You noticed that some data is arriving late but is not being marked as late data, which is resulting in inaccurate

  aggregations downstream. You need to find a solution that allows you to capture the late data in the appropriate window.

  What should you do?

  A. Change your windowing function to session windows to define your windows based on certain activity.

  B. Change your windowing function to tumbling windows to avoid overlapping window periods.

  C. Expand your hopping window so that the late data has more time to arrive within the grouping.

  D. Use watermarks to define the expected data arrival window Allow late data as it arrives.

  Correct Answer: D

  Watermarks are a way of tracking the progress of time in a streaming pipeline. They are used to determine when a window can be closed and the results emitted. Watermarks can be either event-time based or processing-time based. Event-time watermarks track the progress of time based on the timestamps of the data elements, while processing-time watermarks track the progress of time based on the system clock. Event-time watermarks are more accurate, but they require the data source to provide reliable timestamps. Processing-time watermarks are simpler, but they can be affected by system delays or backlogs. By using watermarks, you can define the expected data arrival window for each windowing function. You can also specify how to handle late data, which is data that arrives after the watermark has passed. You can either discard late data, or allow late data and update the results as new data arrives. Allowing late data requires you to use triggers to control when the results are emitted. In this case, using watermarks and allowing late data is the best solution to capture the late data in the appropriate window. Changing the windowing function to session windows or tumbling windows will not solve the problem of late data, as they still rely on watermarks to determine when to close the windows. Expanding the hopping window might reduce the amount of late data, but it will also change the semantics of the windowing function and the results. References: Streaming pipelines | Cloud Dataflow | Google Cloud Windowing | Apache Beam

• Question 297:

  You are implementing a chatbot to help an online retailer streamline their customer service. The chatbot must be able to respond to both text and voice inquiries. You are looking for a low-code or no-code option, and you want to be able to easily train the chatbot to provide answers to keywords. What should you do?

  A. Use the Speech-to-Text API to build a Python application in App Engine.

  B. Use the Speech-to-Text API to build a Python application in a Compute Engine instance.

  C. Use Dialogflow for simple queries and the Speech-to-Text API for complex queries.

  D. Use Dialogflow to implement the chatbot. defining the intents based on the most common queries collected.

  Correct Answer: D

  Dialogflow is a conversational AI platform that allows for easy implementation of chatbots without needing to code. It has built-in integration for both text and voice input via APIs like Cloud Speech-to-Text. Defining intents and entity types allows you to map common queries and keywords to responses. This would provide a low/no- code way to quickly build and iteratively improve the chatbot capabilities.

  https://cloud.google.com/dialogflow/docs Dialogflow is a natural language understanding platform that makes it easy to design and integrate a conversational user interface into your mobile app, web application, device, bot, interactive voice response system, and so on. Using Dialogflow, you can provide new and engaging ways for users to interact with your product. Dialogflow can analyze multiple types of input from your customers, including text or audio inputs (like from a phone or voice recording). It can also respond to your customers in a couple of ways, either through text or with synthetic speech.

• Question 298:

  You work for a large ecommerce company. You store your customers 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?

  A. Set the expiring values of the column families to 30 days and set the number of versions to 2.

  B. Use a timestamp range filter in the query to fetch the customer's data for a specific range.

  C. Set the expiring values of the column families to 29 days and keep the number of versions to 1.

  D. Schedule a job daily to scan the data in the table and delete data older than 30 days.

  Correct Answer: B

  By using a timestamp range filter in the query, you can ensure that the analysts only see the customer data that is within the desired time range, regardless of the garbage collection policy1. This option is the most cost-effective and simple way to avoid fetching data that is marked for deletion by garbage collection, as it does not require changing the existing policy or creating additional jobs. You can use the Bigtable client libraries or the cbt CLI to apply a timestamp range filter to your read requests2. Option A is not effective, as it increases the number of versions to 2, which may cause more data to be retained and increase the storage costs. Option C is not reliable, as it reduces the expiring values to 29 days, which may not match the actual data arrival and usage patterns. Option D is not efficient, as it requires scheduling a job daily to scan and delete the data, which may incur additional overhead and complexity. Moreover, none of these options guarantee that the data older than 30 days will be immediately deleted, as garbage collection is an asynchronous process that can take up to a week to remove the data3. References:

  1: Filters | Cloud Bigtable Documentation | Google Cloud

  2: Read data | Cloud Bigtable Documentation | Google Cloud

  3: Garbage collection overview | Cloud Bigtable Documentation | Google Cloud

• Question 299:

  You need to create a near real-time inventory dashboard that reads the main inventory tables in your BigQuery data warehouse. Historical inventory data is stored as inventory balances by item and location. You have several thousand

  updates to inventory every hour. You want to maximize performance of the dashboard and ensure that the data is accurate.

  What should you do?

  A. Leverage BigQuery UPDATE statements to update the inventory balances as they are changing.

  B. Partition the inventory balance table by item to reduce the amount of data scanned with each inventory update.

  C. Use the BigQuery streaming the stream changes into a daily inventory movement table.Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

  D. Use the BigQuery bulk loader to batch load inventory changes into a daily inventory movement table. Calculate balances in a view that joins it to the historical inventory balance table. Update the inventory balance table nightly.

  Correct Answer: A

• Question 300:

  You are operating a streaming Cloud Dataflow pipeline. Your engineers have a new version of the pipeline with a different windowing algorithm and triggering strategy. You want to update the running pipeline with the new version. You want to ensure that no data is lost during the update. What should you do?

  A. Update the Cloud Dataflow pipeline inflight by passing the --update option with the -- jobName set to the existing job name

  B. Update the Cloud Dataflow pipeline inflight by passing the --update option with the -- jobName set to a new unique job name

  C. Stop the Cloud Dataflow pipeline with the Cancel option. Create a new Cloud Dataflow job with the updated code

  D. Stop the Cloud Dataflow pipeline with the Drain option. Create a new Cloud Dataflow job with the updated code

  Correct Answer: A