All About Top Quality Professional-Data-Engineer Brain Dumps

NEW QUESTION 1

MJTelco needs you to create a schema in Google Bigtable that will allow for the historical analysis of the last 2 years of records. Each record that comes in is sent every 15 minutes, and contains a unique identifier of the device and a data record. The most common query is for all the data for a given device for a given day. Which schema should you use?

• A. Rowkey: date#device_idColumn data: data_point
• B. Rowkey: dateColumn data: device_id, data_point
• C. Rowkey: device_idColumn data: date, data_point
• D. Rowkey: data_pointColumn data: device_id, date
• E. Rowkey: date#data_pointColumn data: device_id

Answer: D

NEW QUESTION 2

Which SQL keyword can be used to reduce the number of columns processed by BigQuery?

• A. BETWEEN
• B. WHERE
• C. SELECT
• D. LIMIT

Answer: C

Explanation:
SELECT allows you to query specific columns rather than the whole table.
LIMIT, BETWEEN, and WHERE clauses will not reduce the number of columns processed by BigQuery.
Reference:
https://cloud.google.com/bigquery/launch-checklist#architecture_design_and_development_checklist

NEW QUESTION 3

You need to compose visualizations for operations teams with the following requirements: Which approach meets the requirements?

• A. Load the data into Google Sheets, use formulas to calculate a metric, and use filters/sorting to show only suboptimal links in a table.
• B. Load the data into Google BigQuery tables, write Google Apps Script that queries the data, calculates the metric, and shows only suboptimal rows in a table in Google Sheets.
• C. Load the data into Google Cloud Datastore tables, write a Google App Engine Application that queries all rows, applies a function to derive the metric, and then renders results in a table using the Google charts and visualization API.
• D. Load the data into Google BigQuery tables, write a Google Data Studio 360 report that connects to your data, calculates a metric, and then uses a filter expression to show only suboptimal rows in a table.

Answer: C

NEW QUESTION 4

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?

• A. Export Bigtable dump to GCS and run your analytical job on top of the exported files.
• B. Add 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.
• C. Add 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.
• D. Increase the size of your existing cluster twice and execute your analytics workload on your new resized cluster.

Answer: B

NEW QUESTION 5

You are building new real-time data warehouse for your company and will use Google BigQuery streaming inserts. There is no guarantee that data will only be sent in once but you do have a unique ID for each row of data and an event timestamp. You want to ensure that duplicates are not included while interactively querying data. Which query type should you use?

• A. Include ORDER BY DESK on timestamp column and LIMIT to 1.
• B. Use GROUP BY on the unique ID column and timestamp column and SUM on the values.
• C. Use the LAG window function with PARTITION by unique ID along with WHERE LAG IS NOT NULL.
• D. Use the ROW_NUMBER window function with PARTITION by unique ID along with WHERE row equals 1.

Answer: D

NEW QUESTION 6

The Dataflow SDKs have been recently transitioned into which Apache service?

• A. Apache Spark
• B. Apache Hadoop
• C. Apache Kafka
• D. Apache Beam

Answer: D

Explanation:
Dataflow SDKs are being transitioned to Apache Beam, as per the latest Google directive Reference: https://cloud.google.com/dataflow/docs/

NEW QUESTION 7

Your financial services company is moving to cloud technology and wants to store 50 TB of financial timeseries data in the cloud. This data is updated frequently and new data will be streaming in all the time. Your company also wants to move their existing Apache Hadoop jobs to the cloud to get insights into this data.
Which product should they use to store the data?

• A. Cloud Bigtable
• B. Google BigQuery
• C. Google Cloud Storage
• D. Google Cloud Datastore

Answer: A

Explanation:
Reference: https://cloud.google.com/bigtable/docs/schema-design-time-series

NEW QUESTION 8

An organization maintains a Google BigQuery dataset that contains tables with user-level datA. They want to expose aggregates of this data to other Google Cloud projects, while still controlling access to the user-level data. Additionally, they need to minimize their overall storage cost and ensure the analysis cost for other projects is assigned to those projects. What should they do?

• A. Create and share an authorized view that provides the aggregate results.
• B. Create and share a new dataset and view that provides the aggregate results.
• C. Create and share a new dataset and table that contains the aggregate results.
• D. Create dataViewer Identity and Access Management (IAM) roles on the dataset to enable sharing.

Answer: D

Explanation:
Reference: https://cloud.google.com/bigquery/docs/access-control

NEW QUESTION 9

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]

Answer: CD

Explanation:
Categorical features in linear models are typically translated into a sparse vector in which each possible value has a corresponding index or id. For example, if there are only three possible eye colors you can represent 'eye_color' as a length 3 vector: 'brown' would become [1, 0, 0], 'blue' would become [0, 1, 0] and 'green' would become [0, 0, 1]. These vectors are called "sparse" because they may be very long, with many zeros, when the set of possible values is very large (such as all English words).
[0, 0, 0, 1, 0, 0, 1] is not a sparse vector because it has two 1s in it. A sparse vector contains only a single 1. [0, 5, 0, 0, 0, 0] is not a sparse vector because it has a 5 in it. Sparse vectors only contain 0s and 1s. Reference: https://www.tensorflow.org/tutorials/linear#feature_columns_and_transformations

NEW QUESTION 10

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.

Answer: B

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

NEW QUESTION 11

You are deploying a new storage system for your mobile application, which is a media streaming service. You decide the best fit is Google Cloud Datastore. You have entities with multiple properties, some of which can take on multiple values. For example, in the entity ‘Movie’ the property ‘actors’ and the property ‘tags’ have multiple values but the property ‘date released’ does not. A typical query would ask for all movies with actor=<actorname> ordered by date_released or all movies with tag=Comedy ordered by date_released. How should you avoid a combinatorial explosion in the number of indexes?

• A. Option A
• B. Option B.
• C. Option C
• D. Option D

Answer: A

NEW QUESTION 12

You are building an application to share financial market data with consumers, who will receive data feeds. Data is collected from the markets in real time. Consumers will receive the data in the following ways:
Real-time event stream
ANSI SQL access to real-time stream and historical data
Batch historical exports
Which solution should you use?

• A. Cloud Dataflow, Cloud SQL, Cloud Spanner
• B. Cloud Pub/Sub, Cloud Storage, BigQuery
• C. Cloud Dataproc, Cloud Dataflow, BigQuery
• D. Cloud Pub/Sub, Cloud Dataproc, Cloud SQL

Answer: A

NEW QUESTION 13

You are building a data pipeline on Google Cloud. You need to prepare data using a casual method for a machine-learning process. You want to support a logistic regression model. You also need to monitor and adjust for null values, which must remain real-valued and cannot be removed. What should you do?

• A. Use Cloud Dataprep to find null values in sample source dat
• B. Convert all nulls to ‘none’ using a Cloud Dataproc job.
• C. Use Cloud Dataprep to find null values in sample source dat
• D. Convert all nulls to 0 using a Cloud Dataprep job.
• E. Use Cloud Dataflow to find null values in sample source dat
• F. Convert all nulls to ‘none’ using a Cloud Dataprep job.
• G. Use Cloud Dataflow to find null values in sample source dat
• H. Convert all nulls to using a custom script.

Answer: C

NEW QUESTION 14

You need to choose a database for a new project that has the following requirements:
Fully managed
Able to automatically scale up
Transactionally consistent
Able to scale up to 6 TB
Able to be queried using SQL Which database do you choose?

• A. Cloud SQL
• B. Cloud Bigtable
• C. Cloud Spanner
• D. Cloud Datastore

Answer: C

NEW QUESTION 15

To give a user read permission for only the first three columns of a table, which access control method would you use?

• A. Primitive role
• B. Predefined role
• C. Authorized view
• D. It's not possible to give access to only the first three columns of a table.

Answer: C

Explanation:
An authorized view allows you to share query results with particular users and groups without giving them
read access to the underlying tables. Authorized views can only be created in a dataset that does not contain the tables queried by the view.
When you create an authorized view, you use the view's SQL query to restrict access to only the rows and columns you want the users to see.
Reference: https://cloud.google.com/bigquery/docs/views#authorized-views

NEW QUESTION 16

Your neural network model is taking days to train. You want to increase the training speed. What can you do?

• A. Subsample your test dataset.
• B. Subsample your training dataset.
• C. Increase the number of input features to your model.
• D. Increase the number of layers in your neural network.

Answer: D

Explanation:
Reference: https://towardsdatascience.com/how-to-increase-the-accuracy-of-a-neural-network-9f5d1c6f407d

NEW QUESTION 17

You need to compose visualization for operations teams with the following requirements:
Telemetry must include data from all 50,000 installations for the most recent 6 weeks (sampling once every minute)
The report must not be more than 3 hours delayed from live data.
The actionable report should only show suboptimal links.
Most suboptimal links should be sorted to the top.
Suboptimal links can be grouped and filtered by regional geography.
User response time to load the report must be <5 seconds.
You create a data source to store the last 6 weeks of data, and create visualizations that allow viewers to see multiple date ranges, distinct geographic regions, and unique installation types. You always show the latest data without any changes to your visualizations. You want to avoid creating and updating new visualizations each month. What should you do?

• A. Look through the current data and compose a series of charts and tables, one for each possible combination of criteria.
• B. Look through the current data and compose a small set of generalized charts and tables bound to criteria filters that allow value selection.
• C. Export the data to a spreadsheet, compose a series of charts and tables, one for each possible combination of criteria, and spread them across multiple tabs.
• D. Load the data into relational database tables, write a Google App Engine application that queries all rows, summarizes the data across each criteria, and then renders results using the Google Charts and visualization API.

Answer: B

NEW QUESTION 18

The marketing team at your organization provides regular updates of a segment of your customer dataset. The marketing team has given you a CSV with 1 million records that must be updated in BigQuery. When you use the UPDATE statement in BigQuery, you receive a quotaExceeded error. What should you do?

• A. Reduce the number of records updated each day to stay within the BigQuery UPDATE DML statement limit.
• B. Increase the BigQuery UPDATE DML statement limit in the Quota management section of the Google Cloud Platform Console.
• C. Split the source CSV file into smaller CSV files in Cloud Storage to reduce the number of BigQuery UPDATE DML statements per BigQuery job.
• D. Import the new records from the CSV file into a new BigQuery tabl
• E. Create a BigQuery job that merges the new records with the existing records and writes the results to a new BigQuery table.

Answer: A

NEW QUESTION 19

Your company is using WHILECARD 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: D

NEW QUESTION 20

You architect a system to analyze seismic data. Your extract, transform, and load (ETL) process runs as a series of MapReduce jobs on an Apache Hadoop cluster. The ETL process takes days to process a data set because some steps are computationally expensive. Then you discover that a sensor calibration step has been omitted. How should you change your ETL process to carry out sensor calibration systematically in the future?

• A. Modify the transformMapReduce jobs to apply sensor calibration before they do anything else.
• B. Introduce a new MapReduce job to apply sensor calibration to raw data, and ensure all other MapReduce jobs are chained after this.
• C. Add sensor calibration data to the output of the ETL process, and document that all users need to apply sensor calibration themselves.
• D. Develop an algorithm through simulation to predict variance of data output from the last MapReduce job based on calibration factors, and apply the correction to all data.

Answer: A

NEW QUESTION 21

You work for a bank. You have a labelled dataset that contains information on already granted loan application and whether these applications have been defaulted. You have been asked to train a model to predict default rates for credit applicants.
What should you do?

• A. Increase the size of the dataset by collecting additional data.
• B. Train a linear regression to predict a credit default risk score.
• C. Remove the bias from the data and collect applications that have been declined loans.
• D. Match loan applicants with their social profiles to enable feature engineering.

Answer: B

NEW QUESTION 22

What are the minimum permissions needed for a service account used with Google Dataproc?

• A. Execute to Google Cloud Storage; write to Google Cloud Logging
• B. Write to Google Cloud Storage; read to Google Cloud Logging
• C. Execute to Google Cloud Storage; execute to Google Cloud Logging
• D. Read and write to Google Cloud Storage; write to Google Cloud Logging

Answer: D

Explanation:
Service accounts authenticate applications running on your virtual machine instances to other Google Cloud Platform services. For example, if you write an application that reads and writes files on Google Cloud Storage, it must first authenticate to the Google Cloud Storage API. At a minimum, service accounts used with Cloud Dataproc need permissions to read and write to Google Cloud Storage, and to write to Google Cloud Logging.
Reference: https://cloud.google.com/dataproc/docs/concepts/service-accounts#important_notes

NEW QUESTION 23

You are designing storage for very large text files for a data pipeline on Google Cloud. You want to support ANSI SQL queries. You also want to support compression and parallel load from the input locations using Google recommended practices. What should you do?

• A. Transform text files to compressed Avro using Cloud Dataflo
• B. Use BigQuery for storage and query.
• C. Transform text files to compressed Avro using Cloud Dataflo
• D. Use Cloud Storage and BigQuerypermanent linked tables for query.
• E. Compress text files to gzip using the Grid Computing Tool
• F. Use BigQuery for storage and query.
• G. Compress text files to gzip using the Grid Computing Tool
• H. Use Cloud Storage, and then import into Cloud Bigtable for query.

Answer: D

NEW QUESTION 24
......